Prof Amir Hussain's Outputs (512)

A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks (2024)
Journal Article
Aldosary, A., Tanveer, M., Ahmad, M., Maghrabi, L. A., Ahmed, E. A., Hussain, A., & El-Latif, A. A. A. (2024). A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/tce.2024.3473930

The Mobile crowdsourcing network (MCN) leverages collaborative intelligence to solve complex tasks through group cooperation. It comprises three main components: the end-user, the service provider, and the mobile user. The end-user requests crowd-sen... Read More about A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks.

Open-Pose 3D zero-shot learning: Benchmark and challenges (2024)
Journal Article
Zhao, W., Yang, G., Zhang, R., Jiang, C., Yang, C., Yan, Y., Hussain, A., & Huang, K. (2025). Open-Pose 3D zero-shot learning: Benchmark and challenges. Neural Networks, 181, Article 106775. https://doi.org/10.1016/j.neunet.2024.106775

With the explosive 3D data growth, the urgency of utilizing zero-shot learning to facilitate data labeling becomes evident. Recently, methods transferring language or language-image pre-training models like Contrastive Language-Image Pre-training (CL... Read More about Open-Pose 3D zero-shot learning: Benchmark and challenges.

MTFDN: An image copy‐move forgery detection method based on multi‐task learning (2024)
Journal Article
Liang, P., Tu, H., Hussain, A., & Li, Z. (online). MTFDN: An image copy‐move forgery detection method based on multi‐task learning. Expert Systems, https://doi.org/10.1111/exsy.13729

Image copy-move forgery, where an image region is copied and pasted within the same image, is a simple yet widely employed manipulation. In this paper, we rethink copy-move forgery detection from the perspective of multi-task learning and summarize t... Read More about MTFDN: An image copy‐move forgery detection method based on multi‐task learning.

Transition-aware human activity recognition using an ensemble deep learning framework (2024)
Journal Article
Khan, S. I., Dawood, H., Khan, M., F. Issa, G., Hussain, A., Alnfiai, M. M., & Adnan, K. M. (2025). Transition-aware human activity recognition using an ensemble deep learning framework. Computers in Human Behavior, 162, Article 108435. https://doi.org/10.1016/j.chb.2024.108435

Understanding human activities in daily life is of utmost importance, especially in the context of personalized and adaptive ubiquitous learning. Although existing HAR systems perform well-identifying activities based on their inter-spatial and tempo... Read More about Transition-aware human activity recognition using an ensemble deep learning framework.

Federated learning‐driven dual blockchain for data sharing and reputation management in Internet of medical things (2024)
Journal Article
Gan, C., Xiao, X., Zhu, Q., Jain, D. K., Saini, A., & Hussain, A. (online). Federated learning‐driven dual blockchain for data sharing and reputation management in Internet of medical things. Expert Systems, https://doi.org/10.1111/exsy.13714

In the Internet of Medical Things (IoMT), the vulnerability of federated learning (FL) to single points of failure, low-quality nodes, and poisoning attacks necessitates innovative solutions. This article introduces a FL-driven dual-blockchain approa... Read More about Federated learning‐driven dual blockchain for data sharing and reputation management in Internet of medical things.

A Comprehensive Survey on Generative AI for Metaverse: Enabling Immersive Experience (2024)
Journal Article
Chamola, V., Sai, S., Bhargava, A., Sahu, A., Jiang, W., Xiong, Z., Niyato, D., & Hussain, A. (2024). A Comprehensive Survey on Generative AI for Metaverse: Enabling Immersive Experience. Cognitive Computation, 16, 3286–3315. https://doi.org/10.1007/s12559-024-10342-9

Generative Artificial Intelligence models are Artificial Intelligence models that generate new content based on a prompt or input. The output content can be in various forms, including text, images, and video. Metaverse refers to a virtual world wher... Read More about A Comprehensive Survey on Generative AI for Metaverse: Enabling Immersive Experience.

Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition (2024)
Journal Article
Gao, F., Luo, X., Lang, R., Wang, J., Sun, J., & Hussain, A. (2024). Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition. Remote Sensing, 16(17), Article 3277. https://doi.org/10.3390/rs16173277

Current synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenario... Read More about Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition.

Context-Aware Audio-Visual Speech Enhancement Based on Neuro-Fuzzy Modelling and User Preference Learning (2024)
Journal Article
Chen, S., Kirton-Wingate, J., Doctor, F., Arshad, U., Dashtipour, K., Gogate, M., Halim, Z., Al-Dubai, A., Arslan, T., & Hussain, A. (2024). Context-Aware Audio-Visual Speech Enhancement Based on Neuro-Fuzzy Modelling and User Preference Learning. IEEE Transactions on Fuzzy Systems, 32(10), 5400-5412. https://doi.org/10.1109/tfuzz.2024.3435050

It is estimated that by 2050 approximately one in ten individuals globally will experience disabling hearing impairment. In the presence of everyday reverberant noise, a substantial proportion of individual users encounter challenges in speech compre... Read More about Context-Aware Audio-Visual Speech Enhancement Based on Neuro-Fuzzy Modelling and User Preference Learning.

Utilizing ubiquitous learning to foster sustainable development in rural areas: Insights from 6G technology (2024)
Journal Article
Liu, Y., Razman, M. R., Syed Zakaria, S. Z., Ern, L. K., Hussain, A., & Chamola, V. (2024). Utilizing ubiquitous learning to foster sustainable development in rural areas: Insights from 6G technology. Computers in Human Behavior, 161, Article 108418. https://doi.org/10.1016/j.chb.2024.108418

Rural education frequently grapples with demanding situations such as isolation, confined assets, and a virtual divide. The emergence of the sixth generation (6G) era, characterized by its speedy connectivity, minimal latency, and robust reliability,... Read More about Utilizing ubiquitous learning to foster sustainable development in rural areas: Insights from 6G technology.

A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs (2024)
Journal Article
Tmamna, J., Fourati, R., Ayed, E. B., Passos, L. A., Papa, J. P., Ayed, M. B., & Hussain, A. (2024). A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs. Neurocomputing, 608, Article 128378. https://doi.org/10.1016/j.neucom.2024.128378

Deep Convolutional Neural Networks (CNNs), continue to demonstrate remarkable performance across various tasks. However, their computational demands and energy consumption present significant drawbacks, restricting their practical deployment and cont... Read More about A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs.

A CNN pruning approach using constrained binary particle swarm optimization with a reduced search space for image classification (2024)
Journal Article
Tmamna, J., Ayed, E. B., Fourati, R., Hussain, A., & Ayed, M. B. (2024). A CNN pruning approach using constrained binary particle swarm optimization with a reduced search space for image classification. Applied Soft Computing, 164, Article 111978. https://doi.org/10.1016/j.asoc.2024.111978

Deep convolutional neural networks (CNNs) have exhibited exceptional performance in a range of computer vision tasks. However, these deep CNNs typically demand significant computational resources, which not only hinders their practical deployment but... Read More about A CNN pruning approach using constrained binary particle swarm optimization with a reduced search space for image classification.

Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey (2024)
Journal Article
Tmamna, J., Ayed, E. B., Fourati, R., Gogate, M., Arslan, T., Hussain, A., & Ayed, M. B. (2024). Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey. Cognitive Computation, 16, 2931–2952. https://doi.org/10.1007/s12559-024-10313-0

Over the past few years, larger and deeper neural network models, particularly convolutional neural networks (CNNs), have consistently advanced state-of-the-art performance across various disciplines. Yet, the computational demands of these models ha... Read More about Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey.

Arabic text classification based on analogical proportions (2024)
Journal Article
Bounhas, M., Elayeb, B., Chouigui, A., Hussain, A., & Cambria, E. (2024). Arabic text classification based on analogical proportions. Expert Systems, 41(10), Article e13609. https://doi.org/10.1111/exsy.13609

Text classification is the process of labelling a given set of text documents with predefined classes or categories. Existing Arabic text classifiers are either applying classic Machine Learning algorithms such as k-NN and SVM or using modern deep le... Read More about Arabic text classification based on analogical proportions.

Multi‐model deep learning system for screening human monkeypox using skin images (2024)
Journal Article
Gupta, K., Bajaj, V., Jain, D. K., & Hussain, A. (2024). Multi‐model deep learning system for screening human monkeypox using skin images. Expert Systems, 41(10), Article e13651. https://doi.org/10.1111/exsy.13651

Purpose
Human monkeypox (MPX) is a viral infection that transmits between individuals via direct contact with animals, bodily fluids, respiratory droplets, and contaminated objects like bedding. Traditional manual screening for the MPX infection is... Read More about Multi‐model deep learning system for screening human monkeypox using skin images.

A Change Severity Degree-based Dynamic Multi-Objective Optimization Algorithm with Adaptive Response Strategy (2024)
Journal Article
Kouka, N., Fourati, R., Fdhila, R., Hussain, A., & Alimi, A. M. (2024). A Change Severity Degree-based Dynamic Multi-Objective Optimization Algorithm with Adaptive Response Strategy. Information Sciences, 677, Article 120794. https://doi.org/10.1016/j.ins.2024.120794

Many real-world optimization problems are dynamic by nature, exhibiting temporal variations in objective functions, constraints, and parameters. These problems present significant challenges for algorithm convergence and diversity,... Read More about A Change Severity Degree-based Dynamic Multi-Objective Optimization Algorithm with Adaptive Response Strategy.

DDformer: Dimension decomposition transformer with semi-supervised learning for underwater image enhancement (2024)
Journal Article
Gao, Z., Yang, J., Jiang, F., Jiao, X., Dashtipour, K., Gogate, M., & Hussain, A. (2024). DDformer: Dimension decomposition transformer with semi-supervised learning for underwater image enhancement. Knowledge-Based Systems, 297, Article 111977. https://doi.org/10.1016/j.knosys.2024.111977

Vision-guided Autonomous Underwater Vehicles (AUVs) have gradually become significant tools for human exploration of the ocean. However, distorted images severely limit the visual ability, making it difficult to meet the needs of complex underwater e... Read More about DDformer: Dimension decomposition transformer with semi-supervised learning for underwater image enhancement.

Impact of the Covid-19 pandemic on audiology service delivery: Observational study of the role of social media in patient communication (2024)
Journal Article
Hussain, A., Hussain, Z., Gogate, M., Dashtipour, K., Ng, D., Riaz, M. S., Goman, A., Sheikh, A., & Hussain, A. (2024). Impact of the Covid-19 pandemic on audiology service delivery: Observational study of the role of social media in patient communication. PLOS ONE, 19(4), Article e0288223. https://doi.org/10.1371/journal.pone.0288223

The Covid-19 pandemic has highlighted an era in hearing health care that necessitates a comprehensive rethinking of audiology service delivery. There has been a significant increase in the number of individuals with hearing loss who seek information... Read More about Impact of the Covid-19 pandemic on audiology service delivery: Observational study of the role of social media in patient communication.

Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare (2024)
Journal Article
Naseem, U., Thapa, S., Zhang, Q., Wang, S., Rashid, J., Hu, L., & Hussain, A. (2024). Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare. Neurocomputing, 592, Article 127736. https://doi.org/10.1016/j.neucom.2024.127736

The digitization of healthcare systems has led to the proliferation of electronic health records (EHRs), serving as comprehensive repositories of patient information. However, the vast volume and complexity of EHR data present challenges in extractin... Read More about Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare.

SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement (2024)
Journal Article
Gao, F., Han, X., Wang, J., Sun, J., Hussain, A., & Zhou, H. (2024). SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 11365-11385. https://doi.org/10.1109/jstars.2024.3383779

There are several unresolved issues in the field of ship instance segmentation in synthetic aperture radar (SAR) images. First, in inshore dense ship area, the problems of missed detections and mask overlap frequently occur. Second, in inshore scenes... Read More about SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement.

Generative AI for Consumer Electronics: Enhancing User Experience with Cognitive and Semantic Computing (2024)
Journal Article
Chamola, V., Sai, S., Sai, R., Hussain, A., & Sikdar, B. (online). Generative AI for Consumer Electronics: Enhancing User Experience with Cognitive and Semantic Computing. IEEE Consumer Electronics Magazine, https://doi.org/10.1109/mce.2024.3387049

Generative Artificial Intelligence(GAI) models such as ChatGPT , DALL-E , and the recently introduced Gemini have attracted considerable interest in both business and academia because of their capacity to produce material in response to human inputs.... Read More about Generative AI for Consumer Electronics: Enhancing User Experience with Cognitive and Semantic Computing.

Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Datasets, Methods, and Challenges (2024)
Journal Article
Chamola, V., Chougule, A., Sam, A., Hussain, A., & Yu, F. R. (2024). Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Datasets, Methods, and Challenges. IEEE Internet of Things, 11(10), 17911-17933. https://doi.org/10.1109/jiot.2024.3362851

The field of autonomous driving research has made significant strides towards achieving full automation, endowing vehicles with self-awareness and independent decision-making. However, integrating automation into vehicular operations presents formida... Read More about Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Datasets, Methods, and Challenges.

Can Generative AI Models Extract Deeper Sentiments as Compared to Traditional Deep Learning Algorithms? (2024)
Journal Article
Anas, M., Saiyeda, A., Sohail, S. S., Cambria, E., & Hussain, A. (2024). Can Generative AI Models Extract Deeper Sentiments as Compared to Traditional Deep Learning Algorithms?. IEEE Intelligent Systems, 39(2), 5-10. https://doi.org/10.1109/mis.2024.3374582

Recent advances in the context of deep learning have led to the development of generative artificial intelligence (AI) models which have shown remarkable performance in complex language understanding tasks. This study proposes an evaluation of tradit... Read More about Can Generative AI Models Extract Deeper Sentiments as Compared to Traditional Deep Learning Algorithms?.

Application of machine learning in predicting frailty syndrome in patients with heart failure (2024)
Journal Article
Szczepanowski, R., Uchmanowicz, I., Pasieczna-Dixit, A. H., Sobecki, J., Katarzyniak, R., Kołaczek, G., Lorkiewicz, W., Kędras, M., Dixit, A., Biegus, J., Wleklik, M., Gobbens, R. J., Hill, L., Jaarsma, T., Hussain, A., Barbagallo, M., Veronese, N., Morabito, F. C., & Kahsin, A. (2024). Application of machine learning in predicting frailty syndrome in patients with heart failure. Advances in Clinical and Experimental Medicine, 33(3), 309-315. https://doi.org/10.17219/acem/184040

Prevention and diagnosis of frailty syndrome (FS) in patients with heart failure (HF) require innovative systems to help medical personnel tailor and optimize their treatment and care. Traditional methods of diagnosing FS in patients could be more sa... Read More about Application of machine learning in predicting frailty syndrome in patients with heart failure.

Guest Editorial: Special Issue on Physics-Informed Machine Learning (2024)
Journal Article
Piccialli, F., Raissi, M., Viana, F. A. C., Fortino, G., Lu, H., & Hussain, A. (2024). Guest Editorial: Special Issue on Physics-Informed Machine Learning. IEEE Transactions on Artificial Intelligence, 5(3), 964-966. https://doi.org/10.1109/tai.2023.3342563

Opto-electrochemical variation with gel polymer electrolytes in transparent electrochemical capacitors for ionotronics (2024)
Journal Article
Kumar, C., Sebastian, A. K., Markapudi, P. R., Beg, M., Sundaram, S., Hussain, A., & Manjakkal, L. (2024). Opto-electrochemical variation with gel polymer electrolytes in transparent electrochemical capacitors for ionotronics. Applied Physics Letters, 124(11), Article 111603. https://doi.org/10.1063/5.0190801

Advanced flexible ionotronic devices have found excellent applications in the next generation of electronic skin (e-skin) development for smart wearables, robotics, and prosthesis. In this work, we developed transparent ionotronic-based flexible elec... Read More about Opto-electrochemical variation with gel polymer electrolytes in transparent electrochemical capacitors for ionotronics.

Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System (2024)
Presentation / Conference Contribution
Gupta, A., Bishnu, A., Ratnarajah, T., Adeel, A., Hussain, A., & Sellathurai, M. (2023, December). Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System. Presented at GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia

Designing an efficient receiver for multiple users transmitting orthogonal frequency-division multiplexing signals to the base station remain a challenging interference-limited problem in 5G-new radio (5G-NR) system. This can lead to stagnation of de... Read More about Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System.

Hate speech detection: A comprehensive review of recent works (2024)
Journal Article
Gandhi, A., Ahir, P., Adhvaryu, K., Shah, P., Lohiya, R., Cambria, E., Poria, S., & Hussain, A. (2024). Hate speech detection: A comprehensive review of recent works. Expert Systems, 41(8), Article e13562. https://doi.org/10.1111/exsy.13562

There has been surge in the usage of Internet as well as social media platforms which has led to rise in online hate speech targeted on individual or group. In the recent years, hate speech has resulted in one of the challenging problems that can unf... Read More about Hate speech detection: A comprehensive review of recent works.

BBox-Free SAR Ship Instance Segmentation Method Based on Gaussian Heatmap (2024)
Journal Article
Gao, F., Zhong, F., Sun, J., Hussain, A., & Zhou, H. (2024). BBox-Free SAR Ship Instance Segmentation Method Based on Gaussian Heatmap. IEEE Transactions on Geoscience and Remote Sensing, 62, Article 5206218. https://doi.org/10.1109/tgrs.2024.3369614

Recently, deep learning methods have been widely adopted for ship detection in synthetic aperture radar (SAR) images. However, many of the existing methods miss adjacent ship instances when detecting densely arranged ship targets in inshore scenes. B... Read More about BBox-Free SAR Ship Instance Segmentation Method Based on Gaussian Heatmap.

A novel generative adversarial network‐based super‐resolution approach for face recognition (2024)
Journal Article
Chougule, A., Kolte, S., Chamola, V., & Hussain, A. (2024). A novel generative adversarial network‐based super‐resolution approach for face recognition. Expert Systems, 41(8), Article e13564. https://doi.org/10.1111/exsy.13564

Face recognition is an essential feature required for a range of computer vision applications such as security, attendance systems, emotion detection, airport check-in, and many others. The super-resolution of subject images is an important and chall... Read More about A novel generative adversarial network‐based super‐resolution approach for face recognition.

Robust Real-time Audio-Visual Speech Enhancement based on DNN and GAN (2024)
Journal Article
Gogate, M., Dashtipour, K., & Hussain, A. (in press). Robust Real-time Audio-Visual Speech Enhancement based on DNN and GAN. IEEE Transactions on Artificial Intelligence, https://doi.org/10.1109/tai.2024.3366141

The human auditory cortex contextually integrates audio-visual (AV) cues to better understand speech in a cocktail party situation. Recent studies have shown that AV speech enhancement (SE) models can significantly improve speech quality and intellig... Read More about Robust Real-time Audio-Visual Speech Enhancement based on DNN and GAN.

STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation (2024)
Journal Article
Fang, M., Yu, L., Xie, H., Tan, Q., Tan, Z., Hussain, A., Wang, Z., Li, J., & Tian, Z. (2024). STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation. IEEE Transactions on Computational Social Systems, 11(4), 5354 - 5366. https://doi.org/10.1109/tcss.2024.3356549

The impressive development of facial manipulation techniques has raised severe public concerns. Identity-aware methods, especially suitable for protecting celebrities, are seen as one of promising face forgery detection approaches with additional ref... Read More about STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation.

RI-L1Approx: A novel Resnet-Inception-based Fast L1-approximation method for face recognition (2024)
Journal Article
Bajpai, S., Mishra, G., Jain, R., Jain, D. K., Saini, D., & Hussain, A. (2024). RI-L1Approx: A novel Resnet-Inception-based Fast L1-approximation method for face recognition. Neurocomputing, 613, Article 128708. https://doi.org/10.1016/j.neucom.2024.128708

Performance of deep learning methods for face recognition often relies on abundant data, posing challenges in surveillance and security where data availability is limited and environments are unconstrained. To address this challenge, we propose a nov... Read More about RI-L1Approx: A novel Resnet-Inception-based Fast L1-approximation method for face recognition.

Novel Category Discovery without Forgetting for Automatic Target Recognition (2024)
Journal Article
Huang, H., Gao, F., Sun, J., Wang, J., Hussain, A., & Zhou, H. (2024). Novel Category Discovery without Forgetting for Automatic Target Recognition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 4408-4420. https://doi.org/10.1109/jstars.2024.3358449

We explore a cutting-edge concept known as C lass Incremental Learning in N ovel Category Discovery for Synthetic Aperture Radar T argets (CNT). This innovative task involves the challenge of identifying categories within unlabeled datasets by utiliz... Read More about Novel Category Discovery without Forgetting for Automatic Target Recognition.

Deep learning methods for early detection of Alzheimer’s disease using structural MR images: A survey (2024)
Journal Article
Hassen, S. B., Neji, M., Hussain, Z., Hussain, A., Alimi, A. M., & Frikha, M. (2024). Deep learning methods for early detection of Alzheimer’s disease using structural MR images: A survey. Neurocomputing, 576, Article 127325. https://doi.org/10.1016/j.neucom.2024.127325

In this paper, we present an extensive review of the most recent works for Alzheimer’s disease (AD) prediction, particularly Moderate Cognitive Impairment (MCI) conversion prediction. We aimed to identify the most useful brain-magnetic resonance imag... Read More about Deep learning methods for early detection of Alzheimer’s disease using structural MR images: A survey.

SAR Target Incremental Recognition Based on Features With Strong Separability (2024)
Journal Article
Gao, F., Kong, L., Lang, R., Sun, J., Wang, J., Hussain, A., & Zhou, H. (2024). SAR Target Incremental Recognition Based on Features With Strong Separability. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-13. https://doi.org/10.1109/tgrs.2024.3351636

With the rapid development of deep learning technology, many synthetic aperture radar (SAR) target recognition algorithms based on convolutional neural networks have achieved exceptional performance on various datasets. However, conventional neural n... Read More about SAR Target Incremental Recognition Based on Features With Strong Separability.

Application for Real-time Audio-Visual Speech Enhancement (2023)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2023, August). Application for Real-time Audio-Visual Speech Enhancement. Presented at Interspeech 2023, Dublin, Ireland

This short paper demonstrates a first of its kind audio-visual (AV) speech enhancement (SE) desktop application that isolates, in real-time, the voice of a target speaker from noisy audio input. The deep neural network model integrated in this applic... Read More about Application for Real-time Audio-Visual Speech Enhancement.

5G-IoT Cloud based Demonstration of Real-Time Audio-Visual Speech Enhancement for Multimodal Hearing-aids (2023)
Presentation / Conference Contribution
Gupta, A., Bishnu, A., Gogate, M., Dashtipour, K., Arslan, T., Adeel, A., Hussain, A., Ratnarajah, T., & Sellathurai, M. (2023, August). 5G-IoT Cloud based Demonstration of Real-Time Audio-Visual Speech Enhancement for Multimodal Hearing-aids. Presented at Interspeech 2023, Dublin, Ireland

Over twenty percent of the world's population suffers from some form of hearing loss, making it one of the most significant public health challenges. Current hearing aids commonly amplify noises while failing to improve speech comprehension in crowde... Read More about 5G-IoT Cloud based Demonstration of Real-Time Audio-Visual Speech Enhancement for Multimodal Hearing-aids.

A novel end-to-end deep convolutional neural network based skin lesion classification framework (2023)
Journal Article
Sulthana A., R. S., Chamola, V., Hussain, A., Hussain, Z., & Albalwy, F. (2024). A novel end-to-end deep convolutional neural network based skin lesion classification framework. Expert Systems with Applications, 246, Article 123056. https://doi.org/10.1016/j.eswa.2023.123056

Background:
Skin diseases are reported to contribute 1.79% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classif... Read More about A novel end-to-end deep convolutional neural network based skin lesion classification framework.

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review (2023)
Journal Article
Rashid, A., Al-Obeidat, F., Kanthimathinathan, H. K., Benakatti, G., Hafez, W., Ramaiah, R., Brierley, J., Hanisch, B., Khilnani, P., Koutentis, C., Brusletto, B. S., Toufiq, M., Hussain, Z., Vyas, H., Malik, Z. A., Schumacher, M., Malik, R. A., Deshpande, S., Quraishi, N., Kadwa, R., …Hussain, A. (2024). Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review. Informatics in Medicine Unlocked, 44, Article 101419. https://doi.org/10.1016/j.imu.2023.101419

Sepsis continues to be recognized as a significant global health challenge across all ages and is characterized by a complex pathophysiology. In this scoping review, PRISMA-ScR guidelines were adhered to, and a transcriptomic methodology was adopted,... Read More about Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review.

Bare‐Bones particle Swarm optimization‐based quantization for fast and energy efficient convolutional neural networks (2023)
Journal Article
Tmamna, J., Ayed, E. B., Fourati, R., Hussain, A., & Ayed, M. B. (2024). Bare‐Bones particle Swarm optimization‐based quantization for fast and energy efficient convolutional neural networks. Expert Systems, 41(4), Article e13522. https://doi.org/10.1111/exsy.13522

Neural network quantization is a critical method for reducing memory usage and computational complexity in deep learning models, making them more suitable for deployment on resource-constrained devices. In this article, we propose a method called BBP... Read More about Bare‐Bones particle Swarm optimization‐based quantization for fast and energy efficient convolutional neural networks.

Machine Un-learning: An Overview of Techniques, Applications, and Future Directions (2023)
Journal Article
Sai, S., Mittal, U., Chamola, V., Huang, K., Spinelli, I., Scardapane, S., Tan, Z., & Hussain, A. (2024). Machine Un-learning: An Overview of Techniques, Applications, and Future Directions. Cognitive Computation, 16, 482-506. https://doi.org/10.1007/s12559-023-10219-3

ML applications proliferate across various sectors. Large internet firms employ ML to train intelligent models using vast datasets, including sensitive user information. However, new regulations like GDPR require data removal by businesses. Deleting... Read More about Machine Un-learning: An Overview of Techniques, Applications, and Future Directions.

Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance (2023)
Journal Article
Anwar, K., Zafar, A., Iqbal, A., Sohail, S. S., Hussain, A., Karaca, Y., Hijji, M., Saudagar, A. K. J., & Muhammad, K. (2023). Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance. Fractals, 31(10), Article 2340149. https://doi.org/10.1142/s0218348x23401497

The proliferation of fractal artificial intelligence (AI)-based decision-making has propelled advances in intelligent computing techniques. Fractal AI-driven decision-making approaches are used to solve a variety of real-world complex problems, espec... Read More about Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance.

Multi-criteria decision making-based waste management: A bibliometric analysis (2023)
Journal Article
Sohail, S. S., Javed, Z., Nadeem, M., Anwer, F., Farhat, F., Hussain, A., Himeur, Y., & Madsen, D. Ø. (2023). Multi-criteria decision making-based waste management: A bibliometric analysis. Heliyon, 9(11), Article e21261. https://doi.org/10.1016/j.heliyon.2023.e21261

Waste management is a complex research domain. While the domain is challenging in terms of content, it is also a diverse and cross-disciplinary research subject. One of its important components includes efficient decision-making at various levels and... Read More about Multi-criteria decision making-based waste management: A bibliometric analysis.

A dual covariant biomarker approach to Kawasaki disease, using vascular endothelial growth factor A and B gene expression; implications for coronary pathogenesis (2023)
Journal Article
Rashid, A., Benakatti, G., Al-Obeidat, F., Phatak, R., Malik, Z. A., Sharief, J., Kadwa, R., Hafez, W., Toufiq, M., Chaussabel, D., Malik, R., Quraishi, N., Zaki, S. A., Shaikh, G., & Hussain, A. (2023). A dual covariant biomarker approach to Kawasaki disease, using vascular endothelial growth factor A and B gene expression; implications for coronary pathogenesis. Informatics in Medicine Unlocked, 43, Article 101384. https://doi.org/10.1016/j.imu.2023.101384

Introduction
Kawasaki disease (KD) is the most common vasculitis in young children, with coronary artery lesions (CALs) and coronary aneurysms (CAAs) being responsible for most KD-related deaths.

Objective
We hypothesized that Vascular Endotheli... Read More about A dual covariant biomarker approach to Kawasaki disease, using vascular endothelial growth factor A and B gene expression; implications for coronary pathogenesis.

Intrusion Detection Systems Using Machine Learning (2023)
Book Chapter
Taylor, W., Hussain, A., Gogate, M., Dashtipour, K., & Ahmad, J. (2024). Intrusion Detection Systems Using Machine Learning. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (75-98). Springer. https://doi.org/10.1007/978-3-031-47590-0_5

Intrusion detection systems (IDS) have developed and evolved over time to form an important component in network security. The aim of an intrusion detection system is to successfully detect intrusions within a network and to trigger alerts to system... Read More about Intrusion Detection Systems Using Machine Learning.

VLC-Assisted Safety Message Dissemination in Roadside Infrastructure-Less IoV Systems: Modeling and Analysis (2023)
Journal Article
Xie, Y., Xu, D., Zhang, T., Yu, K., Hussain, A., & Guizani, M. (2024). VLC-Assisted Safety Message Dissemination in Roadside Infrastructure-Less IoV Systems: Modeling and Analysis. IEEE Internet of Things, 11(5), 8185-8198. https://doi.org/10.1109/jiot.2023.3321268

Internet of Vehicles (IoV) is an emerging paradigm with significant potential to improve traffic efficiency and driving safety. Here, we focus on the design of a novel visible light communication (VLC)-assisted scheme to enable driving safety-related... Read More about VLC-Assisted Safety Message Dissemination in Roadside Infrastructure-Less IoV Systems: Modeling and Analysis.

Solving the cocktail party problem using Multi-modal Hearing Assistive Technology Prototype (2023)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2023, December). Solving the cocktail party problem using Multi-modal Hearing Assistive Technology Prototype. Presented at Acoustics 2023, Sydney, Australia

Hearing loss is a major global health problem, affecting over 1.5 billion people. According to estimations by the World Health Organization, 83% of those who could benefit from hearing assistive devices do not use them. The limited adoption of hearin... Read More about Solving the cocktail party problem using Multi-modal Hearing Assistive Technology Prototype.

CoDeS: A Deep Learning Framework for Identifying COVID-Caused Depression Symptoms (2023)
Journal Article
Wani, M. A., ELAffendi, M., Bours, P., Imran, A. S., Hussain, A., & Abd El-Latif, A. A. (2024). CoDeS: A Deep Learning Framework for Identifying COVID-Caused Depression Symptoms. Cognitive Computation, 16(1), 305-325. https://doi.org/10.1007/s12559-023-10190-z

Depression is a serious mental health condition that affects a person’s ability to feel happy and engaged in activities. The COVID-19 pandemic has led to an increase in depression due to factors such as isolation, financial stress, and uncertainty ab... Read More about CoDeS: A Deep Learning Framework for Identifying COVID-Caused Depression Symptoms.

Resolving the Decreased Rank Attack in RPL’s IoT Networks (2023)
Presentation / Conference Contribution
Ghaleb, B., Al-Duba, A., Hussain, A., Romdhani, I., & Jaroucheh, Z. (2023, June). Resolving the Decreased Rank Attack in RPL’s IoT Networks. Presented at 19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2023), Pafos, Cyprus

The Routing Protocol for Low power and Lossy networks (RPL) has been developed by the Internet Engineering Task Force (IETF) standardization body to serve as a part of the 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Networks) standard, a core... Read More about Resolving the Decreased Rank Attack in RPL’s IoT Networks.

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes (2023)
Journal Article
Rashid, A., Al-Obeida, F., Hafez, W., Benakatti, G., Malik, R. A., Koutentis, C., Sharief, J., Brierley, J., Quraishi, N., Malik, Z. A., Anwary, A., Alkhzaimi, H., Zaki, S. A., Khilnani, P., Kadwa, R., Phatak, R., Schumacher, M., Shaikh, G., Al-Dubai, A., & Hussain, A. (2024). Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes. Shock, 61(1), 4-18. https://doi.org/10.1097/shk.0000000000002227

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of Machine Learning (ML) techniques to bridge the gap between clinical data and gene expression information to bette... Read More about Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes.

A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing (2023)
Journal Article
Zhao, L., Zhao, Z., Zhang, E., Hawbani, A., Al-Dubai, A., Tan, Z., & Hussain, A. (2023). A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing. IEEE Journal on Selected Areas in Communications, 41(11), 3386-3400. https://doi.org/10.1109/jsac.2023.3310062

Vehicle Edge Computing (VEC) is a promising paradigm that exposes Mobile Edge Computing (MEC) to road scenarios. In VEC, task offloading can enable vehicles to offload the computing tasks to nearby Roadside Units (RSUs) that deploy computing capabili... Read More about A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing.

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023)
Journal Article
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognitive Computation, 16(1), 45-74. https://doi.org/10.1007/s12559-023-10179-8

Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep L... Read More about Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence.

A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation? (2023)
Journal Article
Rashid, A., Brusletto, B. S., Al-Obeidat, F., Toufiq, M., Benakatti, G., Brierley, J., Malik, Z. A., Hussain, Z., Alkhazaimi, H., Sharief, J., Kadwa, R., Sarpal, A., Chaussabel, D., Malik, R. A., Quraishi, N., Khilnani, P., Zaki, S. A., Nadeem, R., Shaikh, G., Al-Dubai, A., …Hussain, A. (2023). A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation?. Shock, 60(4), 503-516. https://doi.org/10.1097/shk.0000000000002192

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudin... Read More about A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation?.

Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids (2023)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2023, June). Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

Classical audio-visual (AV) speech enhancement (SE) and separation methods have been successful at operating under constrained environments; however, the speech quality and intelligibility improvement is significantly reduced in unconstrained real-wo... Read More about Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids.

Audio-visual speech enhancement and separation by leveraging multimodal self-supervised embeddings (2023)
Presentation / Conference Contribution
Chern, I., Hung, K., Chen, Y., Hussain, T., Gogate, M., Hussain, A., Tsao, Y., & Hou, J. (2023, June). Audio-visual speech enhancement and separation by leveraging multimodal self-supervised embeddings. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective for categorical problems such as automatic speech recognition and lip-reading. This suggests that useful audio-visual speech representations can be obtained via u... Read More about Audio-visual speech enhancement and separation by leveraging multimodal self-supervised embeddings.

Frequency-Domain Functional Links For Nonlinear Feedback Cancellation In Hearing Aids (2023)
Presentation / Conference Contribution
Nezamdoust, A., Gogate, M., Dashtipour, K., Hussain, A., & Comminiello, D. (2023, June). Frequency-Domain Functional Links For Nonlinear Feedback Cancellation In Hearing Aids. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

The problem of feedback cancellation can be seen as a function approximation task, which often is nonlinear in real-world hearing assistive technologies. Nonlinear methods adopted for this task must exhibit outstanding modeling performance and reduce... Read More about Frequency-Domain Functional Links For Nonlinear Feedback Cancellation In Hearing Aids.

Audio-visual speech enhancement and separation by utilizing multi-modal self-supervised embeddings (2023)
Presentation / Conference Contribution
Chern, I., Hung, K., Chen, Y., Hussain, T., Gogate, M., Hussain, A., Tsao, Y., & Hou, J. (2023, June). Audio-visual speech enhancement and separation by utilizing multi-modal self-supervised embeddings. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective for categorical problems such as automatic speech recognition and lip-reading. This suggests that useful audio-visual speech representations can be obtained via u... Read More about Audio-visual speech enhancement and separation by utilizing multi-modal self-supervised embeddings.

ESPP: Efficient Sector-based Charging Scheduling and Path Planning for WRSNs with Hexagonal Topology (2023)
Journal Article
Naji, A., Hawbani, A., Wang, X., Al-Gunid, H. M., Al-Dhabi, Y., Al-Dubai, A., Hussain, A., Zhao, L., & Alsamhi, S. H. (2024). ESPP: Efficient Sector-based Charging Scheduling and Path Planning for WRSNs with Hexagonal Topology. IEEE Transactions on Sustainable Computing, 9(1), 31 - 45. https://doi.org/10.1109/tsusc.2023.3296607

Wireless Power Transfer (WPT) is a promising technology that can potentially mitigate the energy provisioning problem for sensor networks. In order to efficiently replenish energy for these battery-powered devices, designing appropriate scheduling an... Read More about ESPP: Efficient Sector-based Charging Scheduling and Path Planning for WRSNs with Hexagonal Topology.

Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis (2023)
Journal Article
Diwali, A., Saeedi, K., Dashtipour, K., Gogate, M., Cambria, E., & Hussain, A. (2024). Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis. IEEE Transactions on Affective Computing, 15(3), 837-846. https://doi.org/10.1109/taffc.2023.3296373

Sentiment analysis can be used to derive knowledge that is connected to emotions and opinions from textual data generated by people. As computer power has grown, and the availability of benchmark datasets has increased, deep learning models based on... Read More about Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis.

Underwater image clarifying based on human visual colour constancy using double‐opponency (2023)
Journal Article
Kong, B., Qian, J., Song, P., Yang, J., & Hussain, A. (2024). Underwater image clarifying based on human visual colour constancy using double‐opponency. CAAI Transactions on Intelligence Technology, 9(3), 632-648. https://doi.org/10.1049/cit2.12260

Underwater images are often with biased colours and reduced contrast because of the absorption and scattering effects when light propagates in water. Such images with degradation cannot meet the needs of underwater operations. The main problem in cla... Read More about Underwater image clarifying based on human visual colour constancy using double‐opponency.

Editorial: The new frontier in brain network physiology: from temporal dynamics to the principles of integration in physiological brain networks (2023)
Journal Article
Trenado, C., Mendez-Balbuena, I., Damborská, A., Hussain, A., Mahmud, M., & Daliri, M. R. (2023). Editorial: The new frontier in brain network physiology: from temporal dynamics to the principles of integration in physiological brain networks. Frontiers in Computational Neuroscience, 17, Article 1242834. https://doi.org/10.3389/fncom.2023.1242834

Editorial on the Research Topic -
The new frontier in brain network physiology: from temporal dynamics to the principles of integration in physiological brain networks

Steel surface defect detection based on self-supervised contrastive representation learning with matching metric (2023)
Journal Article
Hu, X., Yang, J., Jiang, F., Hussain, A., Dashtipour, K., & Gogate, M. (2023). Steel surface defect detection based on self-supervised contrastive representation learning with matching metric. Applied Soft Computing, 145, Article 110578. https://doi.org/10.1016/j.asoc.2023.110578

Defect detection is crucial in the quality control of industrial applications. Existing supervised methods are heavily reliant on the large amounts of labeled data. However, labeled data in some specific fields are still scarce, and it requires profe... Read More about Steel surface defect detection based on self-supervised contrastive representation learning with matching metric.

Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions (2023)
Journal Article
Javed, A. R., Saadia, A., Mughal, H., Gadekallu, T. R., Rizwan, M., Maddikunta, P. K. R., Mahmud, M., Liyanage, M., & Hussain, A. (2023). Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions. Cognitive Computation, 15, 1767-1812. https://doi.org/10.1007/s12559-023-10153-4

The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligenc... Read More about Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions.

A Hurst‐based diffusion model using time series characteristics for influence maximization in social networks (2023)
Journal Article
Saxena, B., Saxena, V., Anand, N., Hassija, V., Chamola, V., & Hussain, A. (2023). A Hurst‐based diffusion model using time series characteristics for influence maximization in social networks. Expert Systems, 40(9), Article e13375. https://doi.org/10.1111/exsy.13375

Online social networks have grown exponentially in the recent years while finding applications in real life like marketing, recommendation systems, and social awareness campaigns. An important research area in this field is Influence Maximization, wh... Read More about A Hurst‐based diffusion model using time series characteristics for influence maximization in social networks.

Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning (2023)
Journal Article
Elhassan, N., Varone, G., Ahmed, R., Gogate, M., Dashtipour, K., Almoamari, H., El-Affendi, M. A., Al-Tamimi, B. N., Albalwy, F., & Hussain, A. (2023). Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning. Computers, 12(6), Article 126. https://doi.org/10.3390/computers12060126

Social media networks have grown exponentially over the last two decades, providing the opportunity for users of the internet to communicate and exchange ideas on a variety of topics. The outcome is that opinion mining plays a crucial role in analyzi... Read More about Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning.

Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis (2023)
Journal Article
Rashid, A., Anwary, A. R., Al-Obeidat, F., Brierley, J., Uddin, M., Alkhzaimi, H., Sarpal, A., Toufiq, M., Malik, Z. A., Kadwa, R., Khilnani, P., Guftar Shaikh, M., Benakatti, G., Sharief, J., Ahmed Zaki, S., Zeyada, A., Al-Dubai, A., Hafez, W., & Hussain, A. (2023). Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis. Informatics in Medicine Unlocked, 41, Article 101293. https://doi.org/10.1016/j.imu.2023.101293

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The... Read More about Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis.

Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation (2023)
Journal Article
Wani, M. A., ELAffendi, M., Shakil, K. A., Abuhaimed, I. M., Nayyar, A., Hussain, A., & El-Latif, A. A. A. (2024). Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation. IEEE Transactions on Computational Social Systems, 11(4), 5101 - 5118. https://doi.org/10.1109/tcss.2023.3276764

The emergence of COVID-19 has led to a surge in fake news on social media, with toxic fake news having adverse effects on individuals, society, and governments. Detecting toxic fake news is crucial, but little prior research has been done in this are... Read More about Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation.

PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification (2023)
Journal Article
Yao, K., Huang, K., Sun, J., & Hussain, A. (2023). PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification. IEEE Transactions on Emerging Topics in Computational Intelligence, https://doi.org/10.1109/tetci.2023.3281864

Automatic nuclei segmentation and classification play a vital role in digital pathology. However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or misleading in actual downstream tasks... Read More about PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification.

WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text (2023)
Journal Article
Rashid, J., Kim, J., Hussain, A., & Naseem, U. (2023). WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text. Pattern Recognition Letters, 172, 158-164. https://doi.org/10.1016/j.patrec.2023.06.007

Short texts are a common source of knowledge, and the extraction of such valuable information is beneficial for several purposes. Traditional topic models are incapable of analyzing the internal structural information of topics. They are mostly based... Read More about WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text.

Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids (2023)
Presentation / Conference Contribution
Kirton-Wingate, J., Ahmed, S., Gogate, M., Tsao, Y., & Hussain, A. (2023, June). Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

Since the advent of deep learning (DL), speech enhancement (SE) models have performed well under a variety of noise conditions. However, such systems may still introduce sonic artefacts, sound unnatural, and restrict the ability for a user to hear am... Read More about Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids.

A real‐time lane detection network using two‐directional separation attention (2023)
Journal Article
Zhang, L., Jiang, F., Yang, J., Kong, B., & Hussain, A. (2023). A real‐time lane detection network using two‐directional separation attention. Computer-Aided Civil and Infrastructure Engineering, https://doi.org/10.1111/mice.13051

Real-time network by adopting attention mechanism is helpful for enhancing lane detection capability of autonomous vehicles. This paper proposes a real-time lane detection network (TSA-LNet) that incorporates a lightweight network (LNet) and a two-di... Read More about A real‐time lane detection network using two‐directional separation attention.

Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG (2023)
Journal Article
Shah, J., Chougule, A., Chamola, V., & Hussain, A. (2023). Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG. Neurocomputing, 549, Article 126387. https://doi.org/10.1016/j.neucom.2023.126387

The growing demand for semi-autonomous human–machine systems has led to an increased requirement for human fatigue detection. Direct and invasive approaches for microsleep detection include cognitive computing methods using Brain-Computer Interfaces... Read More about Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG.

A novel multimodal online news popularity prediction model based on ensemble learning (2023)
Journal Article
Arora, A., Hassija, V., Bansal, S., Yadav, S., Chamola, V., & Hussain, A. (2023). A novel multimodal online news popularity prediction model based on ensemble learning. Expert Systems, 40(8), Article e13336. https://doi.org/10.1111/exsy.13336

The prediction of news popularity is having substantial importance for the digital advertisement community in terms of selecting and engaging users. Traditional approaches are based on empirical data collected through surveys and applied statistical... Read More about A novel multimodal online news popularity prediction model based on ensemble learning.

Live Demonstration: Real-time Multi-modal Hearing Assistive Technology Prototype (2023)
Presentation / Conference Contribution
Gogate, M., Hussain, A., Dashtipour, K., & Hussain, A. (2023). Live Demonstration: Real-time Multi-modal Hearing Assistive Technology Prototype. In IEEE ISCAS 2023 Symposium Proceedings. https://doi.org/10.1109/iscas46773.2023.10182070

Hearing loss affects at least 1.5 billion people globally. The WHO estimates 83% of people who could benefit from hearing aids do not use them. Barriers to HA uptake are multifaceted but include ineffectiveness of current HA technology in noisy envir... Read More about Live Demonstration: Real-time Multi-modal Hearing Assistive Technology Prototype.

Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids (2023)
Presentation / Conference Contribution
Bishnu, A., Gupta, A., Gogate, M., Dashtipour, K., Arslan, T., Adeel, A., Hussain, A., Sellathurai, M., & Ratnarajah, T. (2023, May). Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids. Presented at 2023 IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, California

Hearing loss is among the most serious public health problems, affecting as much as 20% of the worldwide population. Even cutting-edge multi-channel audio-only speech enhancement (SE) algorithms used in modern hearing aids face significant hurdles si... Read More about Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids.

A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction (2023)
Journal Article
Huang, H., Zhao, B., Gao, F., Chen, P., Wang, J., & Hussain, A. (2023). A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction. Sensors, 23(10), Article 4828. https://doi.org/10.3390/s23104828

Reconstruction-based and prediction-based approaches are widely used for video anomaly detection (VAD) in smart city surveillance applications. However, neither of these approaches can effectively utilize the rich contextual information that exists i... Read More about A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction.

Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning (2023)
Journal Article
Basabain, S., Cambria, E., Alomar, K., & Hussain, A. (2023). Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning. Expert Systems, 40(8), Article e13329. https://doi.org/10.1111/exsy.13329

A growing amount of research use pre-trained language models to address few/zero-shot text classification problems. Most of these studies neglect the semantic information hidden implicitly beneath the natural language names of class labels and develo... Read More about Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning.

An Incremental SAR Target Recognition Framework via Memory-Augmented Weight Alignment and Enhancement Discrimination (2023)
Journal Article
Huang, H., Gao, F., Wang, J., Hussain, A., & Zhou, H. (2023). An Incremental SAR Target Recognition Framework via Memory-Augmented Weight Alignment and Enhancement Discrimination. IEEE Geoscience and Remote Sensing Letters, 20, https://doi.org/10.1109/lgrs.2023.3269480

Synthetic aperture radar automatic target recognition (SAR ATR) is one of the most important research directions in SAR image interpretation. While much existing research into SAR ATR has focused on deep learning technology, an equally important yet... Read More about An Incremental SAR Target Recognition Framework via Memory-Augmented Weight Alignment and Enhancement Discrimination.

Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets (2023)
Journal Article
Adeel, A., Adetomi, A., Ahmed, K., Hussain, A., Arslan, T., & Phillips, W. A. (2023). Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(4), 818-828. https://doi.org/10.1109/tetci.2022.3228537

Context-sensitive two-point layer 5 pyramidal cells (L5PCs) were discovered as long ago as 1999. However, the potential of this discovery to provide useful neural computation has yet to be demonstrated. Here we show for the first time how a transform... Read More about Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets.

Editorial: Physiological computing of social cognition, volume II (2023)
Journal Article
Fernández-Caballero, A., Hussain, A., Latorre, J. M., Martínez-Rodrigo, A., Rodriguez-Jimenez, R., & Fernández-Sotos, P. (2023). Editorial: Physiological computing of social cognition, volume II. Frontiers in Human Neuroscience, 17, Article 1152291. https://doi.org/10.3389/fnhum.2023.1152291

[Abstract unavailable.]

Randomized block-coordinate adaptive algorithms for nonconvex optimization problems (2023)
Journal Article
Zhou, Y., Huang, K., Li, J., Cheng, C., Wang, X., Hussian, A., & Liu, X. (2023). Randomized block-coordinate adaptive algorithms for nonconvex optimization problems. Engineering Applications of Artificial Intelligence, 121, Article 105968. https://doi.org/10.1016/j.engappai.2023.105968

Nonconvex optimization problems have always been one focus in deep learning, in which many fast adaptive algorithms based on momentum are applied. However, the full gradient computation of high-dimensional feature vector in the above tasks become pro... Read More about Randomized block-coordinate adaptive algorithms for nonconvex optimization problems.

A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting (2023)
Journal Article
Varone, G., Ieracitano, C., Çiftçioğlu, A. Ö., Hussain, T., Gogate, M., Dashtipour, K., Al-Tamimi, B. N., Almoamari, H., Akkurt, I., & Hussain, A. (2023). A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting. Entropy, 25(2), Article 253. https://doi.org/10.3390/e25020253

The development of reinforced polymer composite materials has had a significant influence on the challenging problem of shielding against high-energy photons, particularly X-rays and γ-rays in industrial and healthcare facilities. Heavy materials’ sh... Read More about A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting.

AVSE Challenge: Audio-Visual Speech Enhancement Challenge (2023)
Presentation / Conference Contribution
Aldana Blanco, A. L., Valentini-Botinhao, C., Klejch, O., Gogate, M., Dashtipour, K., Hussain, A., & Bell, P. (2023, January). AVSE Challenge: Audio-Visual Speech Enhancement Challenge. Presented at 2022 IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar

Audio-visual speech enhancement is the task of improving the quality of a speech signal when video of the speaker is available. It opens-up the opportunity of improving speech intelligibility in adverse listening scenarios that are currently too chal... Read More about AVSE Challenge: Audio-Visual Speech Enhancement Challenge.

Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead (2023)
Journal Article
Zhang, K., Zhang, F., Wan, W., Yu, H., Sun, J., Del Ser, J., Elyan, E., & Hussain, A. (2023). Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead. Information Fusion, 93, 227-242. https://doi.org/10.1016/j.inffus.2022.12.026

Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks c... Read More about Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead.

Diverse features discovery transformer for pedestrian attribute recognition (2022)
Journal Article
Zheng, A., Wang, H., Wang, J., Huang, H., He, R., & Hussain, A. (2023). Diverse features discovery transformer for pedestrian attribute recognition. Engineering Applications of Artificial Intelligence, 119, Article 105708. https://doi.org/10.1016/j.engappai.2022.105708

Recently, Swin Transformer has been widely explored as a general backbone for computer vision, which helps to improve the performance of vision tasks due to the ability to establish associations for long-range dependencies of different spatial locati... Read More about Diverse features discovery transformer for pedestrian attribute recognition.

A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids (2022)
Presentation / Conference Contribution
Bishnu, A., Gupta, A., Gogate, M., Dashtipour, K., Adeel, A., Hussain, A., Sellathurai, M., & Ratnarajah, T. (2022, October). A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids. Presented at 2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom), Genoa, Italy

In this paper, we design a first of its kind transceiver (PHY layer) prototype for cloud-based audio-visual (AV) speech enhancement (SE) complying with high data rate and low latency requirements of future multimodal hearing assistive technology. The... Read More about A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids.

Multimodal salient object detection via adversarial learning with collaborative generator (2022)
Journal Article
Tu, Z., Yang, W., Wang, K., Hussain, A., Luo, B., & Li, C. (2023). Multimodal salient object detection via adversarial learning with collaborative generator. Engineering Applications of Artificial Intelligence, 119, Article 105707. https://doi.org/10.1016/j.engappai.2022.105707

Multimodal salient object detection(MSOD), which utilizes multimodal information (e.g., RGB image and thermal infrared or depth image) to detect common salient objects, has received much attention recently. Different modalities reflect different appe... Read More about Multimodal salient object detection via adversarial learning with collaborative generator.

A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator (2022)
Journal Article
Kouka, N., BenSaid, F., Fdhila, R., Fourati, R., Hussain, A., & Alimi, A. M. (2023). A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator. Information Sciences, 623, 220-241. https://doi.org/10.1016/j.ins.2022.12.021

Most evolutionary algorithms, including particle swarm optimization (PSO), use Pareto dominance as a major selection criterion and face significant challenges when dealing with many-objective problems. To tackle this issue, this paper proposes a nove... Read More about A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator.

Towards Simple and Accurate Human Pose Estimation With Stair Network (2022)
Journal Article
Jiang, C., Huang, K., Zhang, S., Wang, X., Xiao, J., Niu, Z., & Hussain, A. (2023). Towards Simple and Accurate Human Pose Estimation With Stair Network. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(3), 805-817. https://doi.org/10.1109/tetci.2022.3224954

In this paper, we focus on tackling the precise keypoint coordinates regression task. Most existing approaches adopt complicated networks with a large number of parameters, leading to a heavy model with poor cost-effectiveness in practice. To overcom... Read More about Towards Simple and Accurate Human Pose Estimation With Stair Network.

Canonical cortical graph neural networks and its application for speech enhancement in audio-visual hearing aids (2022)
Journal Article
Passos, L. A., Papa, J. P., Hussain, A., & Adeel, A. (2023). Canonical cortical graph neural networks and its application for speech enhancement in audio-visual hearing aids. Neurocomputing, 527, 196-203. https://doi.org/10.1016/j.neucom.2022.11.081

Despite the recent success of machine learning algorithms, most models face drawbacks when considering more complex tasks requiring interaction between different sources, such as multimodal input data and logical time sequences. On the other hand, th... Read More about Canonical cortical graph neural networks and its application for speech enhancement in audio-visual hearing aids.

Ellipse Encoding for Arbitrary-Oriented SAR Ship Detection Based on Dynamic Key Points (2022)
Journal Article
Gao, F., Huo, Y., Sun, J., Yu, T., Hussain, A., & Zhou, H. (2022). Ellipse Encoding for Arbitrary-Oriented SAR Ship Detection Based on Dynamic Key Points. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5240528. https://doi.org/10.1109/tgrs.2022.3227260

In recent years, there has been growing interest in developing oriented bounding box (OBB)-based deep learning approaches to detect arbitrary-oriented ship targets in synthetic aperture radar (SAR) images. However, most existing OBB-based detection m... Read More about Ellipse Encoding for Arbitrary-Oriented SAR Ship Detection Based on Dynamic Key Points.

Fusing external knowledge resources for natural language understanding techniques: A survey (2022)
Journal Article
Wang, Y., Wang, W., Chen, Q., Huang, K., Nguyen, A., De, S., & Hussain, A. (2023). Fusing external knowledge resources for natural language understanding techniques: A survey. Information Fusion, 92, 190-204. https://doi.org/10.1016/j.inffus.2022.11.025

Knowledge resources, e.g. knowledge graphs, which formally represent essential semantics and information for logic inference and reasoning, can compensate for the unawareness nature of many natural language processing techniques based on deep neural... Read More about Fusing external knowledge resources for natural language understanding techniques: A survey.

A robust deep learning approach for tomato plant leaf disease localization and classification (2022)
Journal Article
Nawaz, M., Nazir, T., Javed, A., Masood, M., Rashid, J., Kim, J., & Hussain, A. (2022). A robust deep learning approach for tomato plant leaf disease localization and classification. Scientific Reports, 12(1), Article 18568. https://doi.org/10.1038/s41598-022-21498-5

Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato pla... Read More about A robust deep learning approach for tomato plant leaf disease localization and classification.

A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks (2022)
Journal Article
Yan, S., Zhang, Y., Gao, F., Sun, J., Hussain, A., & Zhou, H. (2022). A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 9566-9583. https://doi.org/10.1109/jstars.2022.3218360

Semisupervised learning in synthetic aperture radars (SARs) is one of the research hotspots in the field of radar image automatic target recognition. It can efficiently deal with challenging environments where there are insufficient labeled samples a... Read More about A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks.

PointGS: Bridging and fusing geometric and semantic space for 3D point cloud analysis (2022)
Journal Article
Jiang, C., Huang, K., Wu, J., Wang, X., Xiao, J., & Hussain, A. (2023). PointGS: Bridging and fusing geometric and semantic space for 3D point cloud analysis. Information Fusion, 91, 316-326. https://doi.org/10.1016/j.inffus.2022.10.016

Directly processing 3D point cloud data becomes dominant in classification and segmentation tasks. Present mainstream point based methods usually focus on learning in either geometric space ( PointNet++) or semantic space ( DGCNN). Owing to the irreg... Read More about PointGS: Bridging and fusing geometric and semantic space for 3D point cloud analysis.

WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs (2022)
Journal Article
Ta, H. T., Rahman, A. B. S., Majumder, N., Hussain, A., Najjar, L., Howard, N., Poria, S., & Gelbukh, A. (2023). WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs. Information Fusion, 90, 265-282. https://doi.org/10.1016/j.inffus.2022.09.022

As free online encyclopedias with massive volumes of content, Wikipedia and Wikidata are key to many Natural Language Processing (NLP) tasks, such as information retrieval, knowledge base building, machine translation, text classification, and text s... Read More about WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs.

Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions (2022)
Journal Article
Gandhi, A., Adhvaryu, K., Poria, S., Cambria, E., & Hussain, A. (2023). Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions. Information Fusion, 91, 424-444. https://doi.org/10.1016/j.inffus.2022.09.025

Sentiment analysis (SA) has gained much traction In the field of artificial intelligence (AI) and natural language processing (NLP). There is growing demand to automate analysis of user sentiment towards products or services. Opinions are increasingl... Read More about Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions.

Towards real-time privacy-preserving audio-visual speech enhancement (2022)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2022, September). Towards real-time privacy-preserving audio-visual speech enhancement. Presented at 2nd Symposium on Security and Privacy in Speech Communication, Incheon, Korea

Human auditory cortex in everyday noisy situations is known to exploit aural and visual cues that are contextually combined by the brain’s multi-level integration strategies to selectively suppress the background noise and focus on the target speaker... Read More about Towards real-time privacy-preserving audio-visual speech enhancement.

A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling (2022)
Journal Article
Comminiello, D., Nezamdoust, A., Scardapane, S., Scarpiniti, M., Hussain, A., & Uncini, A. (2023). A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling. IEEE Transactions on Systems, Man and Cybernetics: Systems, 53(3), 1384-1396. https://doi.org/10.1109/tsmc.2022.3202656

Nonlinear models are known to provide excellent performance in real-world applications that often operate in nonideal conditions. However, such applications often require online processing to be performed with limited computational resources. To addr... Read More about A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling.

DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm (2022)
Journal Article
Aboud, A., Rokbani, N., Fdhila, R., Qahtani, A. M., Almutiry, O., Dhahri, H., Hussain, A., & Alimi, A. M. (2022). DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm. Applied Soft Computing, 129, Article 109622. https://doi.org/10.1016/j.asoc.2022.109622

Particle Swarm Optimization (PSO) system based on the distributed architecture over multiple sub-swarms is very efficient for static multi-objective optimization but has not been considered for solving dynamic multi-objective problems (DMOPs). Tracki... Read More about DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm.

A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion (2022)
Journal Article
Gao, F., Xu, J., Lang, R., Wang, J., Hussain, A., & Zhou, H. (2022). A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion. Remote Sensing, 14(18), Article 4583. https://doi.org/10.3390/rs14184583

Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usually encounter the problem of poor feature representation ability due to insuf... Read More about A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion.

Multimodal audio-visual information fusion using canonical-correlated Graph Neural Network for energy-efficient speech enhancement (2022)
Journal Article
Passos, L. A., Papa, J. P., Del Ser, J., Hussain, A., & Adeel, A. (2023). Multimodal audio-visual information fusion using canonical-correlated Graph Neural Network for energy-efficient speech enhancement. Information Fusion, 90, 1-11. https://doi.org/10.1016/j.inffus.2022.09.006

This paper proposes a novel multimodal self-supervised architecture for energy-efficient audio-visual (AV) speech enhancement that integrates Graph Neural Networks with canonical correlation analysis (CCA-GNN). The proposed approach lays its foundati... Read More about Multimodal audio-visual information fusion using canonical-correlated Graph Neural Network for energy-efficient speech enhancement.

A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning (2022)
Presentation / Conference Contribution
Hussain, T., Diyan, M., Gogate, M., Dashtipour, K., Adeel, A., Tsao, Y., & Hussain, A. (2022, July). A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning. Presented at 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland

Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signals. Despite improving the speech quality, su... Read More about A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning.

Pushing the limits of remote RF sensing by reading lips under the face mask (2022)
Journal Article
Hameed, H., Usman, M., Tahir, A., Hussain, A., Abbas, H., Cui, T. J., Imran, M. A., & Abbasi, Q. H. (2022). Pushing the limits of remote RF sensing by reading lips under the face mask. Nature Communications, 13(1), Article 5168. https://doi.org/10.1038/s41467-022-32231-1

The problem of Lip-reading has become an important research challenge in recent years. The goal is to recognise speech from lip movements. Most of the Lip-reading technologies developed so far are camera-based, which require video recording of the ta... Read More about Pushing the limits of remote RF sensing by reading lips under the face mask.

A Mixed Approach for Aggressive Political Discourse Analysis on Twitter (2022)
Journal Article
Torregrosa, J., D’Antonio-Maceiras, S., Villar-Rodríguez, G., Hussain, A., Cambria, E., & Camacho, D. (2023). A Mixed Approach for Aggressive Political Discourse Analysis on Twitter. Cognitive Computation, 15, 440-465. https://doi.org/10.1007/s12559-022-10048-w

Political tensions have grown throughout Europe since the beginning of the new century. The consecutive crises led to the rise of different social movements in several countries, in which the political status quo changed. These changes included an in... Read More about A Mixed Approach for Aggressive Political Discourse Analysis on Twitter.

Next Generation Cognition-Aware Hearing Aid Devices With Microwave Sensors: Opportunities and Challenges (2022)
Journal Article
Anwar, U., Arslan, T., Hussain, A., & Lomax, P. (2022). Next Generation Cognition-Aware Hearing Aid Devices With Microwave Sensors: Opportunities and Challenges. IEEE Access, 10, 82214-82235. https://doi.org/10.1109/access.2022.3195875

The strong association between hearing loss and cognitive decline has developed into a major health challenge that calls for early detection, diagnosis and prevention. Hearing loss usually results in severe health implications that include loss of mo... Read More about Next Generation Cognition-Aware Hearing Aid Devices With Microwave Sensors: Opportunities and Challenges.

DNet-CNet: A novel cascaded deep network for real-time lane detection and classification (2022)
Journal Article
Zhang, L., Jiang, F., Yang, J., Kong, B., Hussain, A., Gogate, M., & Dashtipour, K. (2023). DNet-CNet: A novel cascaded deep network for real-time lane detection and classification. Journal of Ambient Intelligence and Humanized Computing, 14, 10745-10760. https://doi.org/10.1007/s12652-022-04346-2

Robust understanding of the lane position and type is essential for changing lanes in autonomous vehicles. However, accomplishing this task in real time with high level of precision is not trivial. In this paper, we propose a novel cascaded deep neur... Read More about DNet-CNet: A novel cascaded deep network for real-time lane detection and classification.

Arabic sentiment analysis using dependency-based rules and deep neural networks (2022)
Journal Article
Diwali, A., Dashtipour, K., Saeedi, K., Gogate, M., Cambria, E., & Hussain, A. (2022). Arabic sentiment analysis using dependency-based rules and deep neural networks. Applied Soft Computing, 127, Article 109377. https://doi.org/10.1016/j.asoc.2022.109377

With the growth of social platforms in recent years and the rapid increase in the means of communication through these platforms, a significant amount of textual data is available that contains an abundance of individuals’ opinions. Sentiment analysi... Read More about Arabic sentiment analysis using dependency-based rules and deep neural networks.

A novel multiple kernel fuzzy topic modeling technique for biomedical data (2022)
Journal Article
Rashid, J., Kim, J., Hussain, A., Naseem, U., & Juneja, S. (2022). A novel multiple kernel fuzzy topic modeling technique for biomedical data. BMC Bioinformatics, 23(1), Article 275. https://doi.org/10.1186/s12859-022-04780-1

Background: Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used t... Read More about A novel multiple kernel fuzzy topic modeling technique for biomedical data.

Novel single and multi-layer echo-state recurrent autoencoders for representation learning (2022)
Journal Article
Chouikhi, N., Ammar, B., Hussain, A., & Alimi, A. M. (2022). Novel single and multi-layer echo-state recurrent autoencoders for representation learning. Engineering Applications of Artificial Intelligence, 114, Article 105051. https://doi.org/10.1016/j.engappai.2022.105051

Representation learning impacts the performance of Machine Learning (ML) models. Feature extraction-based methods such as Auto-Encoders (AEs) are used to find new, more accurate data representations from original ones. They perform efficiently on a s... Read More about Novel single and multi-layer echo-state recurrent autoencoders for representation learning.

Educational data mining to predict students' academic performance: A survey study (2022)
Journal Article
Batool, S., Rashid, J., Nisar, M. W., Kim, J., Kwon, H., & Hussain, A. (2023). Educational data mining to predict students' academic performance: A survey study. Education and Information Technologies, 28(1), 905-971. https://doi.org/10.1007/s10639-022-11152-y

Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various da... Read More about Educational data mining to predict students' academic performance: A survey study.

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation (2022)
Journal Article
Rashid, J., Kanwal, S., Wasif Nisar, M., Kim, J., & Hussain, A. (2023). An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation. Computer Systems Science and Engineering, 44(2), 1309-1324. https://doi.org/10.32604/csse.2023.026018

In project management, effective cost estimation is one of the most crucial activities to efficiently manage resources by predicting the required cost to fulfill a given task. However, finding the best estimation results in software development is ch... Read More about An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation.

Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition (2022)
Journal Article
Xu, H., Jin, X., Wang, Q., Hussain, A., & Huang, K. (2022). Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition. ACM transactions on multimedia computing communications and applications, 18(2S), Article 119. https://doi.org/10.1145/3538749

Currently, many action recognition methods mostly consider the information from spatial streams. We propose a new perspective inspired by the human visual system to combine both spatial and temporal streams to measure their attention consistency. Spe... Read More about Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition.

Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study (2022)
Journal Article
Hussain, Z., Sheikh, Z., Tahir, A., Dashtipour, K., Gogate, M., Sheikh, A., & Hussain, A. (2022). Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study. JMIR Public Health and Surveillance, 8(5), Article e32543. https://doi.org/10.2196/32543

Background:
The roll-out of vaccines for SARS-CoV-2 in the United Kingdom, started in December 2020. Uptake has been high, and there has been a subsequent reduction in infections, hospitalisations and deaths in vaccinated individuals. However, vacci... Read More about Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study.

Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions (2022)
Journal Article
Zhou, Y., Huang, K., Cheng, C., Wang, X., Hussain, A., & Liu, X. (2023). Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(2), 565-577. https://doi.org/10.1109/tetci.2022.3171797

The training process for deep learning and pattern recognition normally involves the use of convex and strongly convex optimization algorithms such as AdaBelief and SAdam to handle lots of “uninformative” samples that should be ignored, thus incurrin... Read More about Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions.

RoadSeg-CD: A Network With Connectivity Array and Direction Map for Road Extraction From SAR Images (2022)
Journal Article
Gao, F., Tu, J., Wang, J., Hussain, A., & Zhou, H. (2022). RoadSeg-CD: A Network With Connectivity Array and Direction Map for Road Extraction From SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 3992-4003. https://doi.org/10.1109/jstars.2022.3175594

Road extraction from synthetic aperture radar (SAR) images has attracted much attention in the field of remote sensing image processing. General road extraction algorithms, affected by shadows of buildings and trees, are prone to producing fragmented... Read More about RoadSeg-CD: A Network With Connectivity Array and Direction Map for Road Extraction From SAR Images.

Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications (2022)
Journal Article
Hammad, M., Abd El-Latif, A. A., Hussain, A., Abd El-Samie, F. E., Gupta, B. B., Ugail, H., & Sedik, A. (2022). Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications. Computers and Electrical Engineering, 100, Article 108011. https://doi.org/10.1016/j.compeleceng.2022.108011

In this paper, novel convolutional neural network (CNN) and convolutional long short-term (ConvLSTM) deep learning models (DLMs) are presented for automatic detection of arrhythmia for IoT applications. The input ECG signals are represented in 2D for... Read More about Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications.

A novel temporal attentive-pooling based convolutional recurrent architecture for acoustic signal enhancement (2022)
Journal Article
Hussain, T., Wang, W., Gogate, M., Dashtipour, K., Tsao, Y., Lu, X., Ahsan, A., & Hussain, A. (2022). A novel temporal attentive-pooling based convolutional recurrent architecture for acoustic signal enhancement. IEEE Transactions on Artificial Intelligence, 3(5), 833-842. https://doi.org/10.1109/TAI.2022.3169995

Removing background noise from acoustic observations to obtain clean signals is an important research topic regarding numerous real acoustic applications. Owing to their strong model capacity in function mapping, deep neural network-based algorithms... Read More about A novel temporal attentive-pooling based convolutional recurrent architecture for acoustic signal enhancement.

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction (2022)
Journal Article
Rashid, J., Batool, S., Kim, J., Wasif Nisar, M., Hussain, A., Juneja, S., & Kushwaha, R. (2022). An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction. Frontiers in Public Health, 10, Article 860396. https://doi.org/10.3389/fpubh.2022.860396

Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific di... Read More about An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction.

A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation (2022)
Journal Article
Yao, K., Su, Z., Huang, K., Yang, X., Sun, J., Hussain, A., & Coenen, F. (2022). A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation. IEEE Journal of Biomedical and Health Informatics, 26(10), 4976-4986. https://doi.org/10.1109/jbhi.2022.3162118

We consider the problem of volumetric (3D) unsupervised domain adaptation (UDA) in cross-modality medical image segmentation, aiming to perform segmentation on the unannotated target domain (e.g. MRI) with the help of labeled source domain (e.g. CT).... Read More about A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation.

Design and Evaluation of a Button Sensor Antenna for On-Body Monitoring Activity in Healthcare Applications (2022)
Journal Article
Ali, S. M., Sovuthy, C., Noghanian, S., Saeidi, T., Majeed, M. F., Hussain, A., Masood, F., Khan, S. M., Shah, S. A., & Abbasi, Q. H. (2022). Design and Evaluation of a Button Sensor Antenna for On-Body Monitoring Activity in Healthcare Applications. Micromachines, 13(3), Article 475. https://doi.org/10.3390/mi13030475

A button sensor antenna for on-body monitoring in wireless body area network (WBAN) systems is presented. Due to the close coupling between the sensor antenna and the human body, it is highly challenging to design sensor antenna devices. In this pape... Read More about Design and Evaluation of a Button Sensor Antenna for On-Body Monitoring Activity in Healthcare Applications.

Antimicrobial Resistance and Machine Learning: Challenges and Opportunities (2022)
Journal Article
Elyan, E., Hussain, A., Sheikh, A., Elmanama, A. A., Vuttipittayamongkol, P., & Hijazi, K. (2022). Antimicrobial Resistance and Machine Learning: Challenges and Opportunities. IEEE Access, 10, 31561-31577. https://doi.org/10.1109/access.2022.3160213

Antimicrobial Resistance (AMR) has been identified by the World Health Organisation (WHO) as one of the top ten global health threats. Inappropriate use of antibiotics around the world and in particular in Low-to-Middle-Income Countries (LMICs), wher... Read More about Antimicrobial Resistance and Machine Learning: Challenges and Opportunities.

FastAdaBelief: Improving Convergence Rate for Belief-Based Adaptive Optimizers by Exploiting Strong Convexity (2022)
Journal Article
Zhou, Y., Huang, K., Cheng, C., Wang, X., Hussain, A., & Liu, X. (2023). FastAdaBelief: Improving Convergence Rate for Belief-Based Adaptive Optimizers by Exploiting Strong Convexity. IEEE Transactions on Neural Networks and Learning Systems, 34(9), 6515 - 6529. https://doi.org/10.1109/tnnls.2022.3143554

AdaBelief, one of the current best optimizers, demonstrates superior generalization ability over the popular Adam algorithm by viewing the exponential moving average of observed gradients. AdaBelief is theoretically appealing in which it has a data-d... Read More about FastAdaBelief: Improving Convergence Rate for Belief-Based Adaptive Optimizers by Exploiting Strong Convexity.

A Bibliometric Study and Science Mapping Research of Intelligent Decision (2022)
Journal Article
Li, B., Xu, Z., Hong, N., & Hussain, A. (2022). A Bibliometric Study and Science Mapping Research of Intelligent Decision. Cognitive Computation, 14, 989-1008. https://doi.org/10.1007/s12559-022-09993-3

Intelligent decision (ID) has received a great deal of attention and has been integrated into various fields, such as machine learning, fuzzy inference system, and natural language processing. The advanced technologies have become hot topics and have... Read More about A Bibliometric Study and Science Mapping Research of Intelligent Decision.

A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images (2022)
Journal Article
Ieracitano, C., Mammone, N., Versaci, M., Varone, G., Ali, A., Armentano, A., Calabrese, G., Ferrarelli, A., Turano, L., Tebala, C., Hussain, Z., Sheikh, Z., Sheikh, A., Sceni, G., Hussain, A., & Morabito, F. C. (2022). A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images. Neurocomputing, 481, 202-215. https://doi.org/10.1016/j.neucom.2022.01.055

The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR) have been an important imaging modality for assisting in the diagnosis and management of hospitalised Covid-19 patients. However, their interpretation is time... Read More about A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images.

An Attribute Weight Estimation Using Particle Swarm Optimization and Machine Learning Approaches for Customer Churn Prediction (2021)
Presentation / Conference Contribution
Kanwal, S., Rashid, J., Kim, J., Nisar, M. W., Hussain, A., Batool, S., & Kanwal, R. (2021, November). An Attribute Weight Estimation Using Particle Swarm Optimization and Machine Learning Approaches for Customer Churn Prediction. Presented at 2021 International Conference on Innovative Computing (ICIC), Lahore, Pakistan

One of the most challenging problems in the telecommunications industry is predicting customer churn (CCP). Decision-makers and business experts stressed that acquiring new clients is more expensive than maintaining current ones. From current churn d... Read More about An Attribute Weight Estimation Using Particle Swarm Optimization and Machine Learning Approaches for Customer Churn Prediction.

Towards intelligibility-oriented audio-visual speech enhancement (2021)
Presentation / Conference Contribution
Hussain, T., Gogate, M., Dashtipour, K., & Hussain, A. (2021, September). Towards intelligibility-oriented audio-visual speech enhancement. Presented at The Clarity Workshop on Machine Learning Challenges for Hearing Aids (Clarity-2021), Online

Existing deep learning (DL) based approaches are generally optimised to minimise the distance between clean and enhanced speech features. These often result in improved speech quality however they suffer from a lack of generalisation and may not deli... Read More about Towards intelligibility-oriented audio-visual speech enhancement.

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques (2021)
Journal Article
Kaur, I., Doja, M. N., Ahmad, T., Ahmad, M., Hussain, A., Nadeem, A., & Abd El-Latif, A. A. (2021). An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques. Computational Intelligence and Neuroscience, 2021, Article 6342226. https://doi.org/10.1155/2021/6342226

Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes.... Read More about An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques.

A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls (2021)
Journal Article
Varone, G., Boulila, W., Lo Giudice, M., Benjdira, B., Mammone, N., Ieracitano, C., Dashtipour, K., Neri, S., Gasparini, S., Morabito, F. C., Hussain, A., & Aguglia, U. (2022). A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls. Sensors, 22(1), Article 129. https://doi.org/10.3390/s22010129

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigate... Read More about A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls.

FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning (2021)
Journal Article
Spinelli, I., Scardapane, S., Hussain, A., & Uncini, A. (2022). FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning. IEEE Transactions on Artificial Intelligence, 3(3), 344-354. https://doi.org/10.1109/tai.2021.3133818

Graph representation learning has become a ubiquitous component in many scenarios, ranging from social network analysis to energy forecasting in smart grids. In several applications, ensuring the fairness of the node (or graph) representations with r... Read More about FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning.

Attributes Guided Feature Learning for Vehicle Re-Identification (2021)
Journal Article
Li, H., Lin, X., Zheng, A., Li, C., Luo, B., He, R., & Hussain, A. (2022). Attributes Guided Feature Learning for Vehicle Re-Identification. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(5), 1211-1221. https://doi.org/10.1109/tetci.2021.3127906

Vehicle Re-ID has recently attracted enthusiastic attention due to its potential applications in smart city and urban surveillance. However, it suffers from large intra-class variation caused by view variations and illumination changes, and inter-cla... Read More about Attributes Guided Feature Learning for Vehicle Re-Identification.

Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis (2021)
Journal Article
Dashtipour, K., Gogate, M., Gelbukh, A., & Hussain, A. (2022). Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis. Social Network Analysis and Mining, 12(1), Article 9. https://doi.org/10.1007/s13278-021-00840-1

Nowadays, it is important for buyers to know other customer opinions to make informed decisions on buying a product or service. In addition, companies and organizations can exploit customer opinions to improve their products and services. However, th... Read More about Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis.

A Novel Fingerprinting Technique for Data Storing and Sharing through Clouds (2021)
Journal Article
Fatima, M., Nisar, M. W., Rashid, J., Kim, J., Kamran, M., & Hussain, A. (2021). A Novel Fingerprinting Technique for Data Storing and Sharing through Clouds. Sensors, 21(22), Article 7647. https://doi.org/10.3390/s21227647

With the emerging growth of digital data in information systems, technology faces the challenge of knowledge prevention, ownership rights protection, security, and privacy measurement of valuable and sensitive data. On-demand availability of various... Read More about A Novel Fingerprinting Technique for Data Storing and Sharing through Clouds.

A novel multimodal fusion network based on a joint-coding model for lane line segmentation (2021)
Journal Article
Zou, Z., Zhang, X., Liu, H., Li, Z., Hussain, A., & Li, J. (2022). A novel multimodal fusion network based on a joint-coding model for lane line segmentation. Information Fusion, 80, 167-178. https://doi.org/10.1016/j.inffus.2021.10.008

There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architecture from an information theory perspective, and demonstrate its practica... Read More about A novel multimodal fusion network based on a joint-coding model for lane line segmentation.

Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model (2021)
Journal Article
Rabhi, B., Elbaati, A., Boubaker, H., Hamdi, Y., Hussain, A., & Alimi, A. M. (2021). Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model. Memetic Computing, 13, Article 459-475. https://doi.org/10.1007/s12293-021-00345-6

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than off... Read More about Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model.

A novel few-shot learning method for synthetic aperture radar image recognition (2021)
Journal Article
Yue, Z., Gao, F., Xiong, Q., Sun, J., Hussain, A., & Zhou, H. (2021). A novel few-shot learning method for synthetic aperture radar image recognition. Neurocomputing, 465, 215-227. https://doi.org/10.1016/j.neucom.2021.09.009

Synthetic aperture radar (SAR) image recognition is an important stage of SAR image interpretation. The standard convolutional neural network (CNN) has been successfully applied in the SAR image recognition due to its powerful feature extraction capa... Read More about A novel few-shot learning method for synthetic aperture radar image recognition.

Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps (2021)
Journal Article
Li, J., Jiang, F., Yang, J., Kong, B., Gogate, M., Dashtipour, K., & Hussain, A. (2021). Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps. Neurocomputing, 465, 15-25. https://doi.org/10.1016/j.neucom.2021.08.105

Accurate high-definition maps with lane markings are often used as the navigation back-end for commercial autonomous vehicles. Currently, most high-definition maps are manually constructed by human labelling. Therefore, it is urgently required to pro... Read More about Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps.

Effectiveness of virtual and augmented reality for improving knowledge and skills in medical students: protocol for a systematic review (2021)
Journal Article
Hussain, Z., Ng, D. M., Alnafisee, N., Sheikh, Z., Ng, N., Khan, A., Hussain, A., Aitken, D., & Sheikh, A. (2021). Effectiveness of virtual and augmented reality for improving knowledge and skills in medical students: protocol for a systematic review. BMJ Open, 11(8), Article e047004. https://doi.org/10.1136/bmjopen-2020-047004

Introduction Virtual reality (VR) and augmented reality (AR) technologies are increasingly being used in undergraduate medical education. We aim to evaluate the effectiveness of VR and AR technologies for improving knowledge and skills in medical stu... Read More about Effectiveness of virtual and augmented reality for improving knowledge and skills in medical students: protocol for a systematic review.

A Hybrid-Domain Deep Learni/ng-Based BCI For Discriminating Hand Motion Planning From EEG Sources (2021)
Journal Article
Ieracitano, C., Morabito, F. C., Hussain, A., & Mammone, N. (2021). A Hybrid-Domain Deep Learni/ng-Based BCI For Discriminating Hand Motion Planning From EEG Sources. International Journal of Neural Systems, 31(9), Article 2150038. https://doi.org/10.1142/s0129065721500386

In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand movement preparation phases from electroencephalographic (EEG) recordings. The system exploits information extracted from the temporal-domain and time-fr... Read More about A Hybrid-Domain Deep Learni/ng-Based BCI For Discriminating Hand Motion Planning From EEG Sources.

Conceptual Text Region Network: Cognition-Inspired Accurate Scene Text Detection (2021)
Journal Article
Cui, C., Lu, L., Tan, Z., & Hussain, A. (2021). Conceptual Text Region Network: Cognition-Inspired Accurate Scene Text Detection. Neurocomputing, 464, 252-264. https://doi.org/10.1016/j.neucom.2021.08.026

Segmentation-based methods are widely used for scene text detection due to their superiority in describing arbitrary-shaped text instances. However, two major problems still exist: (1) current label generation techniques are mostly empirical and lack... Read More about Conceptual Text Region Network: Cognition-Inspired Accurate Scene Text Detection.

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model (2021)
Journal Article
Nazir, T., Nawaz, M., Rashid, J., Mahum, R., Masood, M., Mehmood, A., Ali, F., Kim, J., Kwon, H., & Hussain, A. (2021). Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model. Sensors, 21(16), Article 5283. https://doi.org/10.3390/s21165283

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems... Read More about Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.

Leveraging label hierarchy using transfer and multi-task learning: A case study on patent classification (2021)
Journal Article
Aroyehun, S. T., Angel, J., Majumder, N., Gelbukh, A., & Hussain, A. (2021). Leveraging label hierarchy using transfer and multi-task learning: A case study on patent classification. Neurocomputing, 464, 421-431. https://doi.org/10.1016/j.neucom.2021.07.057

When labels are organized into a meaningful taxonomy, the parent-child relationship between labels at different levels can give the classifier additional information not deducible from the data alone, especially with limited training data. As a case... Read More about Leveraging label hierarchy using transfer and multi-task learning: A case study on patent classification.

Cloud based scalable object recognition from video streams using orientation fusion and convolutional neural networks (2021)
Journal Article
Usman Yaseen, M., Anjum, A., Fortino, G., Liotta, A., & Hussain, A. (2022). Cloud based scalable object recognition from video streams using orientation fusion and convolutional neural networks. Pattern Recognition, 121, Article 108207. https://doi.org/10.1016/j.patcog.2021.108207

Object recognition from live video streams comes with numerous challenges such as the variation in illumination conditions and poses. Convolutional neural networks (CNNs) have been widely used to perform intelligent visual object recognition. Yet, CN... Read More about Cloud based scalable object recognition from video streams using orientation fusion and convolutional neural networks.

A Novel Multi-Stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-Ray Images (2021)
Journal Article
Fang, Z., Ren, J., MacLellan, C., Li, H., Zhao, H., Hussain, A., & Fortino, G. (2022). A Novel Multi-Stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-Ray Images. IEEE Transactions on Molecular, Biological and Multi-Scale Communications, 8(1), 17-27. https://doi.org/10.1109/TMBMC.2021.3099367

To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial, chest screening with radiography imaging plays an important role in addition to the real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test. Due... Read More about A Novel Multi-Stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-Ray Images.

Arabic question answering system: a survey (2021)
Journal Article
Alwaneen, T. H., Azmi, A. M., Aboalsamh, H. A., Cambria, E., & Hussain, A. (2022). Arabic question answering system: a survey. Artificial Intelligence Review, 55, 207-253. https://doi.org/10.1007/s10462-021-10031-1

Question answering is a subfield of information retrieval. It is a task of answering a question posted in a natural language. A question answering system (QAS) may be considered a good alternative to search engines that return a set of related docume... Read More about Arabic question answering system: a survey.

Public perception of the fifth generation of cellular networks (5G) on social media (2021)
Journal Article
Dashtipour, K., Taylor, W., Ansari, S., Gogate, M., Zahid, A., Sambo, Y., Hussain, A., Abbasi, Q. H., & Imran, M. A. (2021). Public perception of the fifth generation of cellular networks (5G) on social media. Frontiers in Big Data, 4, Article 640868. https://doi.org/10.3389/fdata.2021.640868

With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these types of reviews. To utilize these reviews for user perception, there is a n... Read More about Public perception of the fifth generation of cellular networks (5G) on social media.

Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning (2021)
Journal Article
Taylor, W., Dashtipour, K., Shah, S. A., Hussain, A., Abbasi, Q. H., & Imran, M. A. (2021). Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning. Sensors, 21(11), Article 3881. https://doi.org/10.3390/s21113881

The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in act... Read More about Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning.

Ten Years of Sentic Computing (2021)
Journal Article
Susanto, Y., Cambria, E., Ng, B. C., & Hussain, A. (2022). Ten Years of Sentic Computing. Cognitive Computation, 14, 5-23. https://doi.org/10.1007/s12559-021-09824-x

Sentic computing is a multi-disciplinary approach to sentiment analysis at the crossroads between affective computing and commonsense computing, which exploits both computer and social sciences to better recognize, interpret, and process opinions and... Read More about Ten Years of Sentic Computing.

Advances in machine translation for sign language: approaches, limitations, and challenges (2021)
Journal Article
Farooq, U., Rahim, M. S. M., Sabir, N., Hussain, A., & Abid, A. (2021). Advances in machine translation for sign language: approaches, limitations, and challenges. Neural Computing and Applications, 33, 14357-14399. https://doi.org/10.1007/s00521-021-06079-3

Sign languages are used by the deaf community around the globe to communicate with one another. These are gesture-based languages where a deaf person performs gestures using hands and facial expressions. Every gesture represents a word or a phrase in... Read More about Advances in machine translation for sign language: approaches, limitations, and challenges.

Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence–Enabled Social Media Analysis (2021)
Journal Article
Cresswell, K., Tahir, A., Sheikh, Z., Hussain, Z., Domínguez Hernández, A., Harrison, E., Williams, R., Sheikh, A., & Hussain, A. (2021). Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence–Enabled Social Media Analysis. Journal of Medical Internet Research, 23(5), Article e26618. https://doi.org/10.2196/26618

Background: The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to conta... Read More about Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence–Enabled Social Media Analysis.

Sentiment analysis of persian movie reviews using deep learning (2021)
Journal Article
Dashtipour, K., Gogate, M., Adeel, A., Larijani, H., & Hussain, A. (2021). Sentiment analysis of persian movie reviews using deep learning. Entropy, 23(5), Article 596. https://doi.org/10.3390/e23050596

Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning... Read More about Sentiment analysis of persian movie reviews using deep learning.

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI images (2021)
Journal Article
Masood, M., Nazir, T., Nawaz, M., Mehmood, A., Rashid, J., Kwon, H., Mahmood, T., & Hussain, A. (2021). A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI images. Diagnostics, 11(5), Article 744. https://doi.org/10.3390/diagnostics11050744

A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmenta... Read More about A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI images.

Improving generative adversarial networks with simple latent distributions (2021)
Journal Article
Zhang, S., Huang, K., Qian, Z., Zhang, R., & Hussain, A. (2021). Improving generative adversarial networks with simple latent distributions. Neural Computing and Applications, 33, 13193-13203. https://doi.org/10.1007/s00521-021-05946-3

Generative Adversarial Networks (GANs) have drawn great attention recently since they are the powerful models to generate high-quality images. Although GANs have achieved great success, they usually suffer from unstable training and consequently may... Read More about Improving generative adversarial networks with simple latent distributions.

Does semantics aid syntax? An empirical study on named entity recognition and classification (2021)
Journal Article
Zhong, X., Cambria, E., & Hussain, A. (2022). Does semantics aid syntax? An empirical study on named entity recognition and classification. Neural Computing and Applications, 34, 8373-8384. https://doi.org/10.1007/s00521-021-05949-0

Many researchers jointly model multiple linguistic tasks (e.g., joint modeling of named entity recognition and named entity classification and joint modeling of syntactic parsing and semantic parsing) with an implicit assumption that these individual... Read More about Does semantics aid syntax? An empirical study on named entity recognition and classification.

Artificial intelligence--enabled analysis of public attitudes on facebook and twitter toward covid-19 vaccines in the united kingdom and the united states: Observational study (2021)
Journal Article
Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., Dashtipour, K., Ali, A., & Sheikh, A. (2021). Artificial intelligence--enabled analysis of public attitudes on facebook and twitter toward covid-19 vaccines in the united kingdom and the united states: Observational study. Journal of Medical Internet Research, 23(4), Article e26627. https://doi.org/10.2196/26627

Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general... Read More about Artificial intelligence--enabled analysis of public attitudes on facebook and twitter toward covid-19 vaccines in the united kingdom and the united states: Observational study.

COVID-19 UK Social Media Dataset for Public Health Research (2021)
Data
Plant, R., Hussain, A., & Sheikh, A. (2021). COVID-19 UK Social Media Dataset for Public Health Research. [Dataset]. https://doi.org/10.17869/enu.2021.2755974

We present a benchmark database of public social media postings from the United Kingdom related to the Covid-19 pandemic for academic research purposes, along with some initial analysis, including a taxonomy of key themes organised by keyword. This r... Read More about COVID-19 UK Social Media Dataset for Public Health Research.

A novel domain activation mapping-guided network (DA-GNT) for visual tracking (2021)
Journal Article
Tu, Z., Zhou, A., Gan, C., Jiang, B., Hussain, A., & Luo, B. (2021). A novel domain activation mapping-guided network (DA-GNT) for visual tracking. Neurocomputing, 449, 443-454. https://doi.org/10.1016/j.neucom.2021.03.056

Conventional convolution neural network (CNN)-based visual trackers are easily influenced by too much background information in candidate samples. Further, extreme imbalance of foreground and background samples has a negative impact on training the c... Read More about A novel domain activation mapping-guided network (DA-GNT) for visual tracking.

Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images (2021)
Journal Article
He, Y., Gao, F., Wang, J., Hussain, A., Yang, E., & Zhou, H. (2021). Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3846-3859. https://doi.org/10.1109/jstars.2021.3068530

Common horizontal bounding box-based methods are not capable of accurately locating slender ship targets with arbitrary orientations in synthetic aperture radar (SAR) images. Therefore, in recent years, methods based on oriented bounding box (OBB) ha... Read More about Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images.

A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling (2021)
Journal Article
Farouq, M. W., Boulila, W., Hussain, Z., Rashid, A., Shah, M., Hussain, S., Ng, N., Ng, D., Hanif, H., Shaikh, M. G., Sheikh, A., & Hussain, A. (2021). A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling. Sensors, 21(6), Article 2190. https://doi.org/10.3390/s21062190

Machine learning (ML)-based algorithms are playing an important role in cancer diagnosis and are increasingly being used to aid clinical decision-making. However, these commonly operate as ‘black boxes’ and it is unclear how decisions are derived. Re... Read More about A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling.

Novel deep convolutional neural network-based contextual recognition of Arabic handwritten scripts (2021)
Journal Article
Ahmed, R., Gogate, M., Tahir, A., Dashtipour, K., Al-Tamimi, B., Hawalah, A., El-Affendi, M. A., & Hussain, A. (2021). Novel deep convolutional neural network-based contextual recognition of Arabic handwritten scripts. Entropy, 23(3), Article 340. https://doi.org/10.3390/e23030340

Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continu... Read More about Novel deep convolutional neural network-based contextual recognition of Arabic handwritten scripts.

A novel explainable machine learning approach for EEG-based brain-computer interface systems (2021)
Journal Article
Ieracitano, C., Mammone, N., Hussain, A., & Morabito, F. C. (2022). A novel explainable machine learning approach for EEG-based brain-computer interface systems. Neural Computing and Applications, 34, 11347-11360. https://doi.org/10.1007/s00521-020-05624-w

Electroencephalographic (EEG) recordings can be of great help in decoding the open/close hand’s motion preparation. To this end, cortical EEG source signals in the motor cortex (evaluated in the 1-s window preceding movement onset) are extracted by s... Read More about A novel explainable machine learning approach for EEG-based brain-computer interface systems.

Discriminative Dictionary Design for Action Classification in Still Images and Videos (2021)
Journal Article
Roy, A., Banerjee, B., Hussain, A., & Poria, S. (2021). Discriminative Dictionary Design for Action Classification in Still Images and Videos. Cognitive Computation, 13, 698-708. https://doi.org/10.1007/s12559-021-09851-8

In this paper, we address the problem of action recognition from still images and videos. Traditional local features such as SIFT and STIP invariably pose two potential problems: 1) they are not evenly distributed in different entities of a given cat... Read More about Discriminative Dictionary Design for Action Classification in Still Images and Videos.

A novel context-aware multimodal framework for persian sentiment analysis (2021)
Journal Article
Dashtipour, K., Gogate, M., Cambria, E., & Hussain, A. (2021). A novel context-aware multimodal framework for persian sentiment analysis. Neurocomputing, 457, 377-388. https://doi.org/10.1016/j.neucom.2021.02.020

Most recent works on sentiment analysis have exploited the text modality. However, millions of hours of video recordings posted on social media platforms everyday hold vital unstructured information that can be exploited to more effectively gauge pub... Read More about A novel context-aware multimodal framework for persian sentiment analysis.

A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect (2021)
Journal Article
Guellil, I., Adeel, A., Azouaou, F., Benali, F., Hachani, A., Dashtipour, K., Gogate, M., Ieracitano, C., Kashani, R., & Hussain, A. (2021). A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect. SN Computer Science, 2, Article 118. https://doi.org/10.1007/s42979-021-00510-1

In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach c... Read More about A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect.

A Multipath Fusion Strategy Based Single Shot Detector (2021)
Journal Article
Qu, S., Huang, K., Hussain, A., & Goulermas, Y. (2021). A Multipath Fusion Strategy Based Single Shot Detector. Sensors, 21(4), Article 1360. https://doi.org/10.3390/s21041360

Object detection has wide applications in intelligent systems and sensor applications. Compared with two stage detectors, recent one stage counterparts are capable of running more efficiently with comparable accuracy, which satisfy the requirement of... Read More about A Multipath Fusion Strategy Based Single Shot Detector.

Information fusion for affective computing and sentiment analysis (2021)
Journal Article
Hussain, A., Cambria, E., Poria, S., Hawalah, A., & Herrera, F. (2021). Information fusion for affective computing and sentiment analysis. Information Fusion, 71, 97-98. https://doi.org/10.1016/j.inffus.2021.02.010

Abstract not available.

Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition (2021)
Journal Article
Rahal, N., Tounsi, M., Hussain, A., & Alimi, A. M. (2021). Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition. IEEE Access, 9, 18569-18584. https://doi.org/10.1109/access.2021.3053618

One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to t... Read More about Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition.

Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures (2021)
Journal Article
Varone, G., Hussain, Z., Sheikh, Z., Howard, A., Boulila, W., Mahmud, M., Howard, N., Morabito, F. C., & Hussain, A. (2021). Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures. Sensors, 21(2), Article 637. https://doi.org/10.3390/s21020637

Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulatio... Read More about Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures.

iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings (2021)
Journal Article
Kaiser, M. S., Mahmud, M., Noor, M. B. T., Zenia, N. Z., Mamun, S. A., Mahmud, K. M. A., Azad, S., Aradhya, V. N. M., Stephan, P., Stephan, T., Kannan, R., Hanif, M., Sharmeen, T., Chen, T., & Hussain, A. (2021). iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings. IEEE Access, 9, 13814-13828. https://doi.org/10.1109/access.2021.3050193

The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an atte... Read More about iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings.

A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis (2021)
Journal Article
El-Affendi, M. A., Alrajhi, K., & Hussain, A. (2021). A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis. IEEE Access, 9, 7508-7518. https://doi.org/10.1109/access.2021.3049626

Over the past few years, much work has been done to develop machine learning models that perform Arabic sentiment analysis (ASA) tasks at various levels and in different domains. However, most of this work has been based on shallow machine learning,... Read More about A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis.

Persuasive dialogue understanding: The baselines and negative results (2020)
Journal Article
Chen, H., Ghosal, D., Majumder, N., Hussain, A., & Poria, S. (2021). Persuasive dialogue understanding: The baselines and negative results. Neurocomputing, 431, 47-56. https://doi.org/10.1016/j.neucom.2020.11.040

Persuasion aims at forming one’s opinion and action via a series of persuasive messages containing persuader’s strategies. Due to its potential application in persuasive dialogue systems, the task of persuasive strategy recognition has gained much at... Read More about Persuasive dialogue understanding: The baselines and negative results.

Visual Speech Recognition with Lightweight Psychologically Motivated Gabor Features (2020)
Journal Article
Zhang, X., Xu, Y., Abel, A. K., Smith, L. S., Watt, R., Hussain, A., & Gao, C. (2020). Visual Speech Recognition with Lightweight Psychologically Motivated Gabor Features. Entropy, 22(12), Article 1367. https://doi.org/10.3390/e22121367

Extraction of relevant lip features is of continuing interest in the visual speech domain. Using end-to-end feature extraction can produce good results, but at the cost of the results being difficult for humans to comprehend and relate to. We present... Read More about Visual Speech Recognition with Lightweight Psychologically Motivated Gabor Features.

Big data and IoT-based applications in smart environments: A systematic review (2020)
Journal Article
Hajjaji, Y., Boulila, W., Farah, I. R., Romdhani, I., & Hussain, A. (2021). Big data and IoT-based applications in smart environments: A systematic review. Computer Science Review, 39, Article 100318. https://doi.org/10.1016/j.cosrev.2020.100318

This paper reviews big data and Internet of Things (IoT)-based applications in smart environments. The aim is to identify key areas of application, current trends, data architectures, and ongoing challenges in these fields. To the best of our knowled... Read More about Big data and IoT-based applications in smart environments: A systematic review.

Airport Detection in SAR Images via Salient Line Segment Detector and Edge-Oriented Region Growing (2020)
Journal Article
Tu, J., Gao, F., Sun, J., Hussain, A., & Zhou, H. (2020). Airport Detection in SAR Images via Salient Line Segment Detector and Edge-Oriented Region Growing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 314-326. https://doi.org/10.1109/jstars.2020.3036052

Airport detection in synthetic aperture radar (SAR) images has attracted much concern in the field of remote sensing. Affected by other salient objects with geometrical features similar to those of airports, traditional methods often generate false d... Read More about Airport Detection in SAR Images via Salient Line Segment Detector and Edge-Oriented Region Growing.

ASPIRE - Real noisy audio-visual speech enhancement corpus (2020)
Data
Gogate, M., Dashtipour, K., Adeel, A., & Hussain, A. (2020). ASPIRE - Real noisy audio-visual speech enhancement corpus. [Data]. https://doi.org/10.5281/zenodo.4585619

ASPIRE is a a first of its kind, audiovisual speech corpus recorded in real noisy environment (such as cafe, restaurants) which can be used to support reliable evaluation of multi-modal Speech Filtering technologies. This dataset follows the same sen... Read More about ASPIRE - Real noisy audio-visual speech enhancement corpus.

Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System. (2020)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2020, October). Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System. Presented at Interspeech 2020, Shanghai, China

In this paper, we present VIsual Speech In real nOisy eNvironments (VISION), a first of its kind audio-visual (AV) corpus comprising 2500 utterances from 209 speakers, recorded in real noisy environments including social gatherings, streets, cafeteri... Read More about Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System..

A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments (2020)
Journal Article
Al-Ghadir, A. I., Azmi, A. M., & Hussain, A. (2021). A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments. Information Fusion, 67, 29-40. https://doi.org/10.1016/j.inffus.2020.10.003

Stance detection is a relatively new concept in data mining that aims to assign a stance label (favor, against, or none) to a social media post towards a specific pre-determined target. These targets may not be referred to in the post, and may not be... Read More about A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments.

A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19 (2020)
Journal Article
Ren, J., Yan, Y., Zhao, H., Ma, P., Zabalza, J., Hussain, Z., Luo, S., Dai, Q., Zhao, S., Sheikh, A., Hussain, A., & Li, H. (2020). A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19. IEEE Journal of Biomedical and Health Informatics, 24(12), 3551-3563. https://doi.org/10.1109/JBHI.2020.3027987

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective mana... Read More about A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19.

Deep Neural Network Driven Binaural Audio Visual Speech Separation (2020)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., Bell, P., & Hussain, A. (2020, July). Deep Neural Network Driven Binaural Audio Visual Speech Separation. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow

The central auditory pathway exploits the auditory signals and visual information sent by both ears and eyes to segregate speech from multiple competing noise sources and help disambiguate phonological ambiguity. In this study, inspired from this uni... Read More about Deep Neural Network Driven Binaural Audio Visual Speech Separation.

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications (2020)
Journal Article
Ju, Z., Gun, L., Hussain, A., Mahmud, M., & Ieracitano, C. (2020). A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications. Applied Sciences, 10(19), Article 6761. https://doi.org/10.3390/app10196761

In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specif... Read More about A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications.

A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers (2020)
Journal Article
Ieracitano, C., Paviglianiti, A., Campolo, M., Hussain, A., Pasero, E., & Carlo Morabito, F. (2021). A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers. IEEE/CAA Journal of Automatica Sinica, 8(1), 64-76. https://doi.org/10.1109/JAS.2020.1003387

The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope ( SEM ) images of the electrospun nanofiber, to ensure that no structural defects are produced. The... Read More about A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers.

Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding (2020)
Journal Article
Chen, R., Yu, Y., Chen, J., Zhong, Y., Zhao, H., Hussain, A., & Tan, H. (2020). Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding. Sensors, 20(17), Article 4926. https://doi.org/10.3390/s20174926

With the development of commodity economy, the emergence of fake and shoddy products has seriously harmed the interests of consumers and enterprises. To tackle this challenge, customized 2D barcode is proposed to satisfy the requirements of the enter... Read More about Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding.

A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System (2020)
Journal Article
Farah, L., Hussain, A., Kerrouche, A., Ieracitano, C., Ahmad, J., & Mahmud, M. (2020). A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System. IEEE Access, 8, 163225-163237. https://doi.org/10.1109/access.2020.3016981

Most conventional Fuzzy Logic Controller ( FLC ) rules are based on the knowledge and experience of expert operators: given a specific input, FLCs produce the same output. However, FLCs do not perform very well when dealing with complex problems that... Read More about A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System.

Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images (2020)
Journal Article
Gao, F., He, Y., Wang, J., Hussain, A., & Zhou, H. (2020). Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images. Remote Sensing, 12(16), Article 2619. https://doi.org/10.3390/rs12162619

In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it is urgent to develop methods with higher accuracy and faster speed for ship detection in high-resolution SAR images. Among all kinds of methods, deep-learn... Read More about Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images.

A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation (2020)
Journal Article
Yue, Z., Gao, F., Xiong, Q., Wang, J., Hussain, A., & Zhou, H. (2020). A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4585-4598. https://doi.org/10.1109/jstars.2020.3016064

As an important step of synthetic aperture radar image interpretation, synthetic aperture radar image segmentation aims at segmenting an image into different regions in terms of homogeneity. Because of the deficiency of the labeled samples and the ex... Read More about A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation.

Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning (2020)
Journal Article
Xiong, F., Liu, Z., Huang, K., Yang, X., Qiao, H., & Hussain, A. (2020). Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning. Neural Networks, 129, 163-173. https://doi.org/10.1016/j.neunet.2020.06.003

Continual learning, a widespread ability in people and animals, aims to learn and acquire new knowledge and skills continuously. Catastrophic forgetting usually occurs in continual learning when an agent attempts to learn different tasks sequentially... Read More about Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning.

Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes (2020)
Journal Article
Zhong, X., Cambria, E., & Hussain, A. (2020). Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes. Cognitive Computation, 12, 844-862. https://doi.org/10.1007/s12559-020-09714-8

Time expressions and named entities play important roles in data mining, information retrieval, and natural language processing. However, the conventional position-based tagging schemes (e.g., the BIO and BILOU schemes) that previous research used to... Read More about Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes.

CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement (2020)
Journal Article
Gogate, M., Dashtipour, K., Adeel, A., & Hussain, A. (2020). CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement. Information Fusion, 63, 273-285. https://doi.org/10.1016/j.inffus.2020.04.001

Noisy situations cause huge problems for the hearing-impaired, as hearing aids often make speech more audible but do not always restore intelligibility. In noisy settings, humans routinely exploit the audio-visual (AV) nature of speech to selectively... Read More about CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement.

Novel deep neural network based pattern field classification architectures (2020)
Journal Article
Huang, K., Zhang, S., Zhang, R., & Hussain, A. (2020). Novel deep neural network based pattern field classification architectures. Neural Networks, 127, 82-95. https://doi.org/10.1016/j.neunet.2020.03.011

Field classification is a new extension of traditional classification frameworks that attempts to utilize consistent information from a group of samples (termed fields). By forgoing the independent identically distributed (i.i.d.) assumption, field c... Read More about Novel deep neural network based pattern field classification architectures.

BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework (2020)
Journal Article
Howard, N., Chouikhi, N., Adeel, A., Dial, K., Howard, A., & Hussain, A. (2020). BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework. Frontiers in Computational Neuroscience, 14, https://doi.org/10.3389/fncom.2020.00016

Human intelligence is constituted by a multitude of cognitive functions activated either directly or indirectly by external stimuli of various kinds. Computational approaches to the cognitive sciences and to neuroscience are partly premised on the id... Read More about BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework.

Advances in Brain Inspired Cognitive Systems: 10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings (2020)
Presentation / Conference Contribution
(2019, July). Advances in Brain Inspired Cognitive Systems: 10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings. Presented at BICS: International Conference on Brain Inspired Cognitive Systems, Guangzhou, China

This book constitutes the refereed proceedings of the 10th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2019, held in Guangzhou, China, in July 2019.

The 57 papers presented in this volume were carefully reviewed... Read More about Advances in Brain Inspired Cognitive Systems: 10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings.

Design and evaluation of a biologically-inspired cloud elasticity framework (2020)
Journal Article
Ullah, A., Li, J., & Hussain, A. (2020). Design and evaluation of a biologically-inspired cloud elasticity framework. Cluster Computing, 23, 3095-3117. https://doi.org/10.1007/s10586-020-03073-7

The elasticity in cloud is essential to the effective management of computational resources as it enables readjustment at runtime to meet application demands. Over the years, researchers and practitioners have proposed many auto-scaling solutions usi... Read More about Design and evaluation of a biologically-inspired cloud elasticity framework.

Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications (2020)
Journal Article
Tanoli, S. A. K., Shah, S. A., Khan, M. B., Nawaz, F., Hussain, A., Al-Dubai, A. Y., Khan, I., Shah, S. Y., & Alsarhan, A. (2020). Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications. IEEE Access, 8, 29395-29406. https://doi.org/10.1109/access.2020.2969750

Wireless communication using existing coding models poses several challenges for RF signals due to
multipath scattering, rapid fluctuations in signal strength and path loss effect. Unlike existing works, this
study presents a novel cod... Read More about Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications.

Self-focus Deep Embedding Model for Coarse-Grained Zero-Shot Classification (2020)
Presentation / Conference Contribution
Yang, G., Huang, K., Zhang, R., Goulermas, J. Y., & Hussain, A. (2019, July). Self-focus Deep Embedding Model for Coarse-Grained Zero-Shot Classification. Presented at 10th International Conference, BICS 2019, Guangzhou, China

Zero-shot learning (ZSL), i.e. classifying patterns where there is a lack of labeled training data, is a challenging yet important research topic. One of the most common ideas for ZSL is to map the data (e.g., images) and semantic attributes to the s... Read More about Self-focus Deep Embedding Model for Coarse-Grained Zero-Shot Classification.

Height Prediction for Growth Hormone Deficiency Treatment Planning Using Deep Learning (2020)
Presentation / Conference Contribution
Ilyas, M., Ahmad, J., Lawson, A., Khan, J. S., Tahir, A., Adeel, A., Larijani, H., Kerrouche, A., Shaikh, M. G., Buchanan, W., & Hussain, A. (2019, July). Height Prediction for Growth Hormone Deficiency Treatment Planning Using Deep Learning. Presented at 10th International Conference, BICS 2019, Guangzhou, China

Prospective studies using longitudinal patient data can be used to help to predict responsiveness to Growth Hormone (GH) therapy and assess any suspected risks. In this paper, a novel Clinical Decision Support System (CDSS) is developed to predict gr... Read More about Height Prediction for Growth Hormone Deficiency Treatment Planning Using Deep Learning.

Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances (2020)
Presentation / Conference Contribution
Ahmed, R., Dashtipour, K., Gogate, M., Raza, A., Zhang, R., Huang, K., Hawalah, A., Adeel, A., & Hussain, A. (2019, July). Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances. Presented at 10th International Conference, BICS 2019, Guangzhou, China

In pattern recognition, automatic handwriting recognition (AHWR) is an area of research that has developed rapidly in the last few years. It can play a significant role in broad-spectrum of applications rending from, bank cheque processing, applicati... Read More about Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances.

Generalized Adversarial Training in Riemannian Space (2020)
Presentation / Conference Contribution
Zhang, S., Huang, K., Zhang, R., & Hussain, A. (2020). Generalized Adversarial Training in Riemannian Space. In 2019 IEEE International Conference on Data Mining (ICDM) (826-835). https://doi.org/10.1109/icdm.2019.00093

Adversarial examples, referred to as augmented data points generated by imperceptible perturbations of input samples, have recently drawn much attention. Well-crafted adversarial examples may even mislead state-of-the-art deep neural network (DNN) mo... Read More about Generalized Adversarial Training in Riemannian Space.

Random Features and Random Neurons for Brain-Inspired Big Data Analytics (2020)
Presentation / Conference Contribution
Gogate, M., Hussain, A., & Huang, K. (2019, November). Random Features and Random Neurons for Brain-Inspired Big Data Analytics. Presented at 2019 International Conference on Data Mining Workshops (ICDMW), Beijing, China

With the explosion of Big Data, fast and frugal reasoning algorithms are increasingly needed to keep up with the size and the pace of user-generated contents on the Web. In many real-time applications, it is preferable to be able to process more data... Read More about Random Features and Random Neurons for Brain-Inspired Big Data Analytics.

A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia (2019)
Journal Article
Ieracitano, C., Mammone, N., Hussain, A., & Morabito, F. C. (2020). A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Networks, 123, 176-190. https://doi.org/10.1016/j.neunet.2019.12.006

Electroencephalographic (EEG) recordings generate an electrical map of the human brain that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things (IoT) and Brain-Computer Interface (BCI) applications. From a signal... Read More about A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia.

Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs (2019)
Journal Article
Alsarhan, A., Kilani, Y., Al-Dubai, A., Zomaya, A. Y., & Hussain, A. (2020). Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs. IEEE Transactions on Vehicular Technology, 69(2), 1568-1581. https://doi.org/10.1109/TVT.2019.2956228

Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintain... Read More about Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs.

A Cognitively Inspired System Architecture for the Mengshi Cognitive Vehicle (2019)
Journal Article
Zhang, X., Zhou, M., Liu, H., & Hussain, A. (2020). A Cognitively Inspired System Architecture for the Mengshi Cognitive Vehicle. Cognitive Computation, 12(1), 140-149. https://doi.org/10.1007/s12559-019-09692-6

This paper introduces the functional system architecture of the Mengshi intelligent vehicle, winner of the 2018 World Intelligent Driving Challenge (WIDC). Different from traditional smart vehicles, a cognitive module is introduced in the system arch... Read More about A Cognitively Inspired System Architecture for the Mengshi Cognitive Vehicle.

A novel statistical analysis and autoencoder driven intelligent intrusion detection approach (2019)
Journal Article
Ieracitano, C., Adeel, A., Morabito, F. C., & Hussain, A. (2020). A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing, 387, 51-62. https://doi.org/10.1016/j.neucom.2019.11.016

In the current digital era, one of the most critical and challenging issues is ensuring cybersecurity in information technology (IT) infrastructures. With significant improvements in technology, hackers have been developing ever more complex and dang... Read More about A novel statistical analysis and autoencoder driven intelligent intrusion detection approach.

Cognitive Modelling and Learning for Multimedia Mining and Understanding (2019)
Journal Article
Ren, J., Hussain, A., Han, J., & Jia, X. (2019). Cognitive Modelling and Learning for Multimedia Mining and Understanding. Cognitive Computation, 11(6), 761-762. https://doi.org/10.1007/s12559-019-09684-6

Abstract not available.

A Novel Real-Time, Lightweight Chaotic-Encryption Scheme for Next-Generation Audio-Visual Hearing Aids (2019)
Journal Article
Adeel, A., Ahmad, J., Larijani, H., & Hussain, A. (2020). A Novel Real-Time, Lightweight Chaotic-Encryption Scheme for Next-Generation Audio-Visual Hearing Aids. Cognitive Computation, 12, 589-601. https://doi.org/10.1007/s12559-019-09653-z

Next-generation audio-visual (AV) hearing aids stand as a major enabler to realize more intelligible audio. However, high data rate, low latency, low computational complexity, and privacy are some of the major bottlenecks to the successful deployment... Read More about A Novel Real-Time, Lightweight Chaotic-Encryption Scheme for Next-Generation Audio-Visual Hearing Aids.

Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data (2019)
Journal Article
Ma, F., Gao, F., Sun, J., Zhou, H., & Hussain, A. (2019). Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data. Remote Sensing, 11(21), 2586. https://doi.org/10.3390/rs11212586

The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learni... Read More about Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data.

A non-parametric softmax for improving neural attention in time-series forecasting (2019)
Journal Article
Totaro, S., Hussain, A., & Scardapane, S. (2020). A non-parametric softmax for improving neural attention in time-series forecasting. Neurocomputing, 381, 177-185. https://doi.org/10.1016/j.neucom.2019.10.084

Neural attention has become a key component in many deep learning applications, ranging from machine translation to time series forecasting. While many variations of attention have been developed over recent years, all share a common component in the... Read More about A non-parametric softmax for improving neural attention in time-series forecasting.

A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks (2019)
Journal Article
Dashtipour, K., Gogate, M., Li, J., Jiang, F., Kong, B., & Hussain, A. (2020). A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks. Neurocomputing, 380, 1-10. https://doi.org/10.1016/j.neucom.2019.10.009

Social media hold valuable, vast and unstructured information on public opinion that can be utilized to improve products and services. The automatic analysis of such data, however, requires a deep understanding of natural language. Current sentiment... Read More about A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks.

Editorial: Physiological Computing of Social Cognition (2019)
Journal Article
Fernández-Caballero, A., Miguel Latorre, J., Martínez-Rodrigo, A., Rodriguez-Jimenez, R., & Hussain, A. (2019). Editorial: Physiological Computing of Social Cognition. Frontiers in Human Neuroscience, 13, Article 326. https://doi.org/10.3389/fnhum.2019.00326

Editorial on the Research Topic Physiological Computing of Social Cognition

Style-Neutralized Pattern Classification Based on Adversarially Trained Upgraded U-Net (2019)
Journal Article
Jiang, H., Huang, K., Zhang, R., & Hussain, A. (2021). Style-Neutralized Pattern Classification Based on Adversarially Trained Upgraded U-Net. Cognitive Computation, 13(4), 845-858. https://doi.org/10.1007/s12559-019-09660-0

Traditional machine learning approaches usually hold the assumption that data for model training and in real applications are created following the identical and independent distribution (i.i.d.). However, several relevant research topics have demons... Read More about Style-Neutralized Pattern Classification Based on Adversarially Trained Upgraded U-Net.

Lip-reading driven deep learning approach for speech enhancement (2019)
Journal Article
Adeel, A., Gogate, M., Hussain, A., & Whitmer, W. M. (2021). Lip-reading driven deep learning approach for speech enhancement. IEEE Transactions on Emerging Topics in Computational Intelligence, 5(3), 481-490. https://doi.org/10.1109/tetci.2019.2917039

This paper proposes a novel lip-reading driven deep learning framework for speech enhancement. The approach leverages the complementary strengths of both deep learning and analytical acoustic modeling (filtering-based approach) as compared to benchma... Read More about Lip-reading driven deep learning approach for speech enhancement.

Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments (2019)
Journal Article
Adeel, A., Gogate, M., & Hussain, A. (2020). Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments. Information Fusion, 59, 163-170. https://doi.org/10.1016/j.inffus.2019.08.008

Human speech processing is inherently multi-modal, where visual cues (e.g. lip movements) can help better understand speech in noise. Our recent work [1] has shown that lip-reading driven, audio-visual (AV) speech enhancement can significantly outper... Read More about Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments.

Integrated GANs: Semi-Supervised SAR Target Recognition (2019)
Journal Article
Gao, F., Liu, Q., Sun, J., Hussain, A., & Zhou, H. (2019). Integrated GANs: Semi-Supervised SAR Target Recognition. IEEE Access, 7, 113999-114013. https://doi.org/10.1109/access.2019.2935167

With the advantage of working in all weathers and all days, synthetic aperture radar (SAR) imaging systems have a great application value. As an efficient image generation and recognition model, generative adversarial networks (GANs) have been applie... Read More about Integrated GANs: Semi-Supervised SAR Target Recognition.

A Semi-Supervised Synthetic Aperture Radar (SAR) Image Recognition Algorithm Based on an Attention Mechanism and Bias-Variance Decomposition (2019)
Journal Article
Gao, F., Shi, W., Wang, J., Hussain, A., & Zhou, H. (2019). A Semi-Supervised Synthetic Aperture Radar (SAR) Image Recognition Algorithm Based on an Attention Mechanism and Bias-Variance Decomposition. IEEE Access, 7, 108617-108632. https://doi.org/10.1109/access.2019.2933459

Synthetic Aperture Radar (SAR) target recognition is an important research direction of SAR image interpretation. In recent years, most of machine learning methods applied to SAR target recognition are supervised learning which requires a large numbe... Read More about A Semi-Supervised Synthetic Aperture Radar (SAR) Image Recognition Algorithm Based on an Attention Mechanism and Bias-Variance Decomposition.

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data (2019)
Journal Article
Feng, M., Zheng, J., Ren, J., Hussain, A., Li, X., Xi, Y., & Liu, Q. (2019). Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data. IEEE Access, 7, 106111-106123. https://doi.org/10.1109/access.2019.2930410

Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for vis... Read More about Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data.

Deep Cognitive Neural Network (DCNN) (2019)
Patent
Howard, N., Adeel, A., Gogate, M., & Hussain, A. (2019). Deep Cognitive Neural Network (DCNN). US2019/0156189

Embodiments of the present systems and methods may provide a more efficient and low-powered cognitive computational platform utilizing a deep cognitive neural network (DCNN), incorporating an architecture that integrates convolutional feedforward and... Read More about Deep Cognitive Neural Network (DCNN).

A Novel Multi-Input Bidirectional LSTM and HMM Based Approach for Target Recognition from Multi-Domain Radar Range Profiles (2019)
Journal Article
Gao, F., Huang, T., Wang, J., Sun, J., Hussain, A., & Zhou, H. (2019). A Novel Multi-Input Bidirectional LSTM and HMM Based Approach for Target Recognition from Multi-Domain Radar Range Profiles. Electronics, 8(5), https://doi.org/10.3390/electronics8050535

Radars, as active detection sensors, are known to play an important role in various intelligent devices. Target recognition based on high-resolution range profile (HRRP) is an important approach for radars to monitor interesting targets. Traditional... Read More about A Novel Multi-Input Bidirectional LSTM and HMM Based Approach for Target Recognition from Multi-Domain Radar Range Profiles.

Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization (2019)
Journal Article
Taha, T. M., Wajid, S. K., & Hussain, A. (2019). Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization. Journal of Computer Science, 15(5), 691-701. https://doi.org/10.3844/jcssp.2019.691.701

Speech enhancement is used in almost all modern communication systems. This is due to the quality of speech being degraded by environmental interference factors, such as: Acoustic additive noise, acoustic reverberation or white Gaussian noise. This p... Read More about Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization.

A novel visual attention method for target detection from SAR images (2019)
Journal Article
Gao, F., Liu, A., Liu, K., Yang, E., & Hussain, A. (2019). A novel visual attention method for target detection from SAR images. Chinese Journal of Aeronautics, 32(8), 1946-1958. https://doi.org/10.1016/j.cja.2019.03.021

Synthetic Aperture Radar (SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for no... Read More about A novel visual attention method for target detection from SAR images.

Computational and natural language processing based studies of hadith literature: a survey (2019)
Journal Article
Azmi, A. M., Al-Qabbany, A. O., & Hussain, A. (2019). Computational and natural language processing based studies of hadith literature: a survey. Artificial Intelligence Review, 52(2), 1369-1414. https://doi.org/10.1007/s10462-019-09692-w

Hadith is one of the most celebrated resources of Classical Arabic text. The hadiths, or Prophetic traditions (tradition for short), are narrations originating from the sayings and conduct of Prophet Muhammad. For Muslims, hadiths are the second most... Read More about Computational and natural language processing based studies of hadith literature: a survey.

Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations (2019)
Journal Article
Chouikhi, N., Ammar, B., Hussain, A., & Alimi, A. M. (2019). Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations. Neurocomputing, 341, 195-211. https://doi.org/10.1016/j.neucom.2019.03.012

The Multi-Layered Echo-State Network (ML-ESN) is a recently developed, highly powerful type of recurrent neural network. It has succeeded in dealing with several non-linear benchmark problems. On account of its rich dynamics, ML-ESN is exploited in t... Read More about Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations.

Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF (2019)
Journal Article
Ma, F., Gao, F., Sun, J., Zhou, H., & Hussain, A. (2019). Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF. Remote Sensing, 11(5), Article 512. https://doi.org/10.3390/rs11050512

Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based image and is the basis of image interpretation. However, most of the existing segmentation methods usually neglect the appearance and spatial... Read More about Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF.

A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection (2019)
Journal Article
Khan, F. A., Gumaei, A., Derhab, A., & Hussain, A. (2019). A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection. IEEE Access, 7, 30373-30385. https://doi.org/10.1109/access.2019.2899721

The network intrusion detection system is an important tool for protecting computer networks against threats and malicious attacks. Many techniques have recently been proposed; however, these techniques face significant challenges due to the continuo... Read More about A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection.

Cognitively inspired feature extraction and speech recognition for automated hearing loss testing (2019)
Journal Article
Nisar, S., Tariq, M., Adeel, A., Gogate, M., & Hussain, A. (2019). Cognitively inspired feature extraction and speech recognition for automated hearing loss testing. Cognitive Computation, 11(4), 489-502. https://doi.org/10.1007/s12559-018-9607-4

Hearing loss, a partial or total inability to hear, is one of the most commonly reported disabilities. A hearing test can be carried out by an audiologist to assess a patient’s auditory system. However, the procedure requires an appointment, which ca... Read More about Cognitively inspired feature extraction and speech recognition for automated hearing loss testing.

A Novel Multi-Stage Fusion based Approach for Gene Expression Profiling in Non-Small Cell Lung Cancer (2019)
Journal Article
Wael Farouq, M., Boulila, W., Abdel-aal, M., Hussain, A., & Salem, A. (2019). A Novel Multi-Stage Fusion based Approach for Gene Expression Profiling in Non-Small Cell Lung Cancer. IEEE Access, 7, 37141-37150. https://doi.org/10.1109/ACCESS.2019.2898897

Background: Non-small cell lung cancer is defined at the molecular level by mutations and alterations to oncogenes, including AKT1, ALK, BRAF, EGFR, HER2, KRAS, MEK1, MET, NRAS, PIK3CA, RET, and ROS1. A better understanding of non-small cell lung can... Read More about A Novel Multi-Stage Fusion based Approach for Gene Expression Profiling in Non-Small Cell Lung Cancer.

A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA) (2019)
Journal Article
Ozturk, M., Gogate, M., Onireti, O., Adeel, A., Hussain, A., & Imran, M. A. (2019). A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA). Neurocomputing, 358, 479-489. https://doi.org/10.1016/j.neucom.2019.01.031

One of the fundamental goals of mobile networks is to enable uninterrupted access to wireless services without compromising the expected quality of service (QoS). This paper reports a number of significant contributions. First, a novel analytical mod... Read More about A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA).

DNN driven speaker independent audio-visual mask estimation for speech separation (2018)
Presentation / Conference Contribution
Gogate, M., Adeel, A., Marxer, R., Barker, J., & Hussain, A. (2018, September). DNN driven speaker independent audio-visual mask estimation for speech separation. Presented at Interspeech 2018, Baixas, France

Preface (2018)
Presentation / Conference Contribution
Ren, J., Hussain, A., Zheng, J., Liu, C., Luo, B., Zhao, H., & Zhao, X. (2018). Preface. In Advances in Brain Inspired Cognitive Systems (V-VI). https://doi.org/10.1007/978-3-030-00563-4

Complex-Valued Neural Networks With Nonparametric Activation Functions (2018)
Journal Article
Scardapane, S., Van Vaerenbergh, S., Hussain, A., & Uncini, A. (2020). Complex-Valued Neural Networks With Nonparametric Activation Functions. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(2), 140-150. https://doi.org/10.1109/tetci.2018.2872600

Complex-valued neural networks (CVNNs) are a powerful modeling tool for domains where data can be naturally interpreted in terms of complex numbers. However, several analytical properties of the complex domain (such as holomorphicity) make the design... Read More about Complex-Valued Neural Networks With Nonparametric Activation Functions.

Adaptation of sentiment analysis techniques to Persian language (2018)
Presentation / Conference Contribution
Dashtipour, K., Hussain, A., & Gelbukh, A. (2017, April). Adaptation of sentiment analysis techniques to Persian language. Presented at 18th International Conference, CICLing 2017, Budapest, Hungary

In the recent years, people all around the world share their opinions about different fields with each other over Internet. Sentiment analysis techniques have been introduced to classify these rich data based on the polarity of the opinion. Sentiment... Read More about Adaptation of sentiment analysis techniques to Persian language.

Benchmarking multimodal sentiment analysis (2018)
Presentation / Conference Contribution
Cambria, E., Hazarika, D., Poria, S., Hussain, A., & Subramanyam, R. (2018). Benchmarking multimodal sentiment analysis. In Computational Linguistics and Intelligent Text Processing (166-179). https://doi.org/10.1007/978-3-319-77116-8_13

We propose a deep-learning-based framework for multimodal sentiment analysis and emotion recognition. In particular, we leverage on the power of convolutional neural networks to obtain a performance improvement of 10% over the state of the art by com... Read More about Benchmarking multimodal sentiment analysis.

A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings (2018)
Journal Article
Ieracitano, C., Mammone, N., Bramanti, A., Hussain, A., & Morabito, F. (2019). A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings. Neurocomputing, 323, 96-107. https://doi.org/10.1016/j.neucom.2018.09.071

A data-driven machine deep learning approach is proposed for differentiating subjects with Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC), by only analyzing noninvasive scalp EEG recordings. The methodology here pr... Read More about A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings.

A Novel Semi-supervised Classification Method Based on Class Certainty of Samples (2018)
Presentation / Conference Contribution
Gao, F., Yue, Z., Xiong, Q., Wang, J., Yang, E., & Hussain, A. (2018). A Novel Semi-supervised Classification Method Based on Class Certainty of Samples. . https://doi.org/10.1007/978-3-030-00563-4_30

The traditional classification method based on supervised learning classifies remote sensing (RS) images by using sufficient labelled samples. However, the number of labelled samples is limited due to the expensive and time-consuming collection. To e... Read More about A Novel Semi-supervised Classification Method Based on Class Certainty of Samples.

A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter (2018)
Journal Article
Alqarafi, A., Adeel, A., Hawalah, A., Swingler, K., & Hussain, A. (2018). A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter. Lecture Notes in Computer Science, 589-596. https://doi.org/10.1007/978-3-030-00563-4_57

In the literature, limited work has been conducted to develop sentiment resources for Saudi dialect. The lack of resources such as dialectical lexicons and corpora are some of the major bottlenecks to the successful development of Arabic sentiment an... Read More about A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter.

Comparison of Sentiment Analysis Approaches Using Modern Arabic and Sudanese Dialect (2018)
Presentation / Conference Contribution
Hussien, I., Dashtipour, K., & Hussain, A. (2018, July). Comparison of Sentiment Analysis Approaches Using Modern Arabic and Sudanese Dialect. Presented at 9th International Conference, BICS 2018, Xi'an, China

Sentiment analysis mainly focused on the automatic recognition of opinions’ polarity, as positive or negative. Nowadays, sentiment analysis is replacing the web-based and traditional survey methods commonly conducted by companies for finding the publ... Read More about Comparison of Sentiment Analysis Approaches Using Modern Arabic and Sudanese Dialect.

Exploiting Deep Learning for Persian Sentiment Analysis (2018)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Ieracitano, C., Larijani, H., & Hussain, A. (2018, July). Exploiting Deep Learning for Persian Sentiment Analysis. Presented at 9th International Conference, BICS 2018, Xi'an, China

The rise of social media is enabling people to freely express their opinions about products and services. The aim of sentiment analysis is to automatically determine subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspe... Read More about Exploiting Deep Learning for Persian Sentiment Analysis.

Saliency Detection via Bidirectional Absorbing Markov Chain (2018)
Presentation / Conference Contribution
Jiang, F., Kong, B., Adeel, A., Xiao, Y., & Hussain, A. (2018). Saliency Detection via Bidirectional Absorbing Markov Chain. . https://doi.org/10.1007/978-3-030-00563-4_48

Traditional saliency detection via Markov chain only consider boundaries nodes. However, in addition to boundaries cues, background prior and foreground prior cues play a complementary role to enhance saliency detection. In this paper, we propose an... Read More about Saliency Detection via Bidirectional Absorbing Markov Chain.

SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis (2018)
Presentation / Conference Contribution
Guellil, I., Adeel, A., Azouaou, F., & Hussain, A. (2018). SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis. . https://doi.org/10.1007/978-3-030-00563-4_54

Data annotation is an important but time-consuming and costly procedure. To sort a text into two classes, the very first thing we need is a good annotation guideline, establishing what is required to qualify for each class. In the literature, the dif... Read More about SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis.

Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection (2018)
Presentation / Conference Contribution
Ieracitano, C., Adeel, A., Gogate, M., Dashtipour, K., Morabito, F., Larijani, H., Raza, A., & Hussain, A. (2018, July). Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection. Presented at 9th International Conference, BICS 2018, Xi'an, China

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology (ICT) systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially... Read More about Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection.

Style Neutralization Generative Adversarial Classifier (2018)
Presentation / Conference Contribution
Jiang, H., Huang, K., Zhang, R., & Hussain, A. (2018). Style Neutralization Generative Adversarial Classifier. In BICS: International Conference on Brain Inspired Cognitive Systems (3-13). https://doi.org/10.1007/978-3-030-00563-4_1

Breathtaking improvement has been seen with the recently proposed deep Generative Adversarial Network (GAN). Purposes of most existing GAN-based models majorly concentrate on generating realistic and vivid patterns by a pattern generator with the aid... Read More about Style Neutralization Generative Adversarial Classifier.

Cross-modality interactive attention network for multispectral pedestrian detection (2018)
Journal Article
Zhang, L., Liu, Z., Zhang, S., Yang, X., Qiao, H., Huang, K., & Hussain, A. (2019). Cross-modality interactive attention network for multispectral pedestrian detection. Information Fusion, 50, 20-29. https://doi.org/10.1016/j.inffus.2018.09.015

Multispectral pedestrian detection is an emerging solution with great promise in many around-the-clock applications, such as automotive driving and security surveillance. To exploit the complementary nature and remedy contradictory appearance between... Read More about Cross-modality interactive attention network for multispectral pedestrian detection.

A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management (2018)
Book Chapter
Adeel, A., Gogate, M., Farooq, S., Ieracitano, C., Dashtipour, K., Larijani, H., & Hussain, A. (2019). A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management. In T. S. Durrani, W. Wang, & S. M. Forbes (Eds.), Geological Disaster Monitoring Based on Sensor Networks (57-66). Springer. https://doi.org/10.1007/978-981-13-0992-2_5

Extreme events and disasters resulting from climate change or other ecological factors are difficult to predict and manage. Current limitations of state-of-the-art approaches to disaster prediction and management could be addressed by adopting new un... Read More about A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management.

Accelerating Infinite Ensemble of Clustering by Pivot Features (2018)
Journal Article
Jin, X., Xie, G., Huang, K., & Hussain, A. (2018). Accelerating Infinite Ensemble of Clustering by Pivot Features. Cognitive Computation, 10(6), 1042-1050. https://doi.org/10.1007/s12559-018-9583-8

The infinite ensemble clustering (IEC) incorporates both ensemble clustering and representation learning by fusing infinite basic partitions and shows appealing performance in the unsupervised context. However, it needs to solve the linear equation s... Read More about Accelerating Infinite Ensemble of Clustering by Pivot Features.

Towards workload-aware cloud resource provisioning using a multi-controller fuzzy switching approach (2018)
Journal Article
Ullah, A., Li, J., & Hussain, A. (2018). Towards workload-aware cloud resource provisioning using a multi-controller fuzzy switching approach. International Journal of High Performance Computing and Networking, 12(1), 13-25. https://doi.org/10.1504/IJHPCN.2018.093838

Elasticity enables cloud customers to enrich their applications to dynamically adjust underlying cloud resources. Over the past, a plethora of techniques have been introduced to implement elasticity. Control theory is one such technique that offers a... Read More about Towards workload-aware cloud resource provisioning using a multi-controller fuzzy switching approach.

A comparison of two methods of using a serious game for teaching marine ecology in a university setting (2018)
Journal Article
Ameerbakhsh, O., Maharaj, S., Hussain, A., & McAdam, B. (2019). A comparison of two methods of using a serious game for teaching marine ecology in a university setting. International Journal of Human-Computer Studies, 127, 181-189. https://doi.org/10.1016/j.ijhcs.2018.07.004

There is increasing interest in the use of serious games in STEM education. Interactive simulations and serious games can be used by students to explore systems where it would be impractical or unethical to perform real world studies or experiments.... Read More about A comparison of two methods of using a serious game for teaching marine ecology in a university setting.

A New Algorithm of SAR Image Target Recognition Based on Improved Deep Convolutional Neural Network (2018)
Journal Article
Gao, F., Huang, T., Sun, J., Wang, J., Hussain, A., & Yang, E. (2018). A New Algorithm of SAR Image Target Recognition Based on Improved Deep Convolutional Neural Network. Cognitive Computation, 1-16. https://doi.org/10.1007/s12559-018-9563-z

In an attempt to exploit the automatic feature extraction ability of biologically-inspired deep learning models, and enhance the learning of target features, we propose a novel deep learning algorithm. This is based on a deep convolutional neural net... Read More about A New Algorithm of SAR Image Target Recognition Based on Improved Deep Convolutional Neural Network.

Clinical Decision Support Systems: A Visual Survey (2018)
Journal Article
Farooq, K., Khan, B. S., Niazi, M. A., Leslie, S. J., & Hussain, A. (2018). Clinical Decision Support Systems: A Visual Survey. Informatica, 42(4), 485-505. https://doi.org/10.31449/inf.v42i4.1571

Clinical Decision Support Systems (CDSS) form an important area of research. In spite of its importance, it is difficult for researchers to evaluate the domain primarily because of a considerable spread of relevant literature in interdisciplinary dom... Read More about Clinical Decision Support Systems: A Visual Survey.

A comparative study of Persian sentiment analysis based on different feature combinations (2018)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Hussain, A., Alqarafi, A., & Durrani, T. (2017, July). A comparative study of Persian sentiment analysis based on different feature combinations. Presented at International Conference in Communications, Signal Processing, and Systems, Harbin, China

In recent years, the use of internet and correspondingly the number of online reviews, comments and opinions have increased significantly. It is indeed very difficult for humans to read these opinions and classify them accurately. Consequently, there... Read More about A comparative study of Persian sentiment analysis based on different feature combinations.

Toward's Arabic multi-modal sentiment analysis (2018)
Presentation / Conference Contribution
Alqarafi, A., Adeel, A., Gogate, M., Dashtipour, K., Hussain, A., & Durrani, T. (2017, July). Toward's Arabic multi-modal sentiment analysis. Presented at International Conference in Communications, Signal Processing, and Systems, Harbin, China

In everyday life, people use internet to express and share opinions, facts, and sentiments about products and services. In addition, social media applications such as Facebook, Twitter, WhatsApp, Snapchat etc., have become important information shari... Read More about Toward's Arabic multi-modal sentiment analysis.

Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis (2018)
Journal Article
Mondal, A., Cambria, E., Das, D., Hussain, A., & Bandyopadhyay, S. (2018). Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis. Cognitive Computation, 10(4), 670-685. https://doi.org/10.1007/s12559-018-9567-8

In healthcare services, information extraction is the key to understand any corpus-based knowledge. The process becomes laborious when the annotation is done manually for the availability of a large number of text corpora. Hence, future automated ext... Read More about Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis.

A new two-layer mixture of factor analyzers with joint factor loading model for the classification of small dataset problems (2018)
Journal Article
Yang, X., Huang, K., Zhang, R., Goulermas, J., & Hussain, A. (2018). A new two-layer mixture of factor analyzers with joint factor loading model for the classification of small dataset problems. Neurocomputing, 312, 352-363. https://doi.org/10.1016/j.neucom.2018.05.085

Dimensionality Reduction (DR) is a fundamental topic of pattern classification and machine learning. For classification tasks, DR is typically employed as a pre-processing step, succeeded by an independent classifier training stage. However, such ind... Read More about A new two-layer mixture of factor analyzers with joint factor loading model for the classification of small dataset problems.

A control theoretical view of cloud elasticity: taxonomy, survey and challenges (2018)
Journal Article
Ullah, A., Li, J., Shen, Y., & Hussain, A. (2018). A control theoretical view of cloud elasticity: taxonomy, survey and challenges. Cluster Computing, 21(4), 1735-1764. https://doi.org/10.1007/s10586-018-2807-6

The lucrative features of cloud computing such as pay-as-you-go pricing model and dynamic resource provisioning (elasticity) attract clients to host their applications over the cloud to save up-front capital expenditure and to reduce the operational... Read More about A control theoretical view of cloud elasticity: taxonomy, survey and challenges.

A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications (2018)
Journal Article
Mahmud, M., Kaiser, M. S., Rahman, M. M., Rahman, M. A., Shabut, A., Al-Mamun, S., & Hussain, A. (2018). A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications. Cognitive Computation, 10(5), 864-873. https://doi.org/10.1007/s12559-018-9543-3

Rapid advancement of Internet of Things (IoT) and cloud computing enables neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable... Read More about A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications.

Special Issue of BICS 2016 (2018)
Journal Article
Liu, C., Hussain, A., Luo, B., Tan, K., Zeng, Y., & Zhang, Z. (2018). Special Issue of BICS 2016. Cognitive Computation, 10(2), 282-283. https://doi.org/10.1007/s12559-018-9551-3

Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization (2018)
Journal Article
Yang, X., Huang, K., Zhang, R., & Hussain, A. (2018). Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(6), 450-463. https://doi.org/10.1109/tetci.2018.2806934

Nonnegative matrix factorization (NMF) has been widely exploited in many computational intelligence and pattern recognition problems. In particular, it can be used to extract latent features from data. However, previous NMF models often assume a fixe... Read More about Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization.

Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis (2018)
Journal Article
Ma, Y., Peng, H., Khan, T., Cambria, E., & Hussain, A. (2018). Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis. Cognitive Computation, 10(4), 639-650. https://doi.org/10.1007/s12559-018-9549-x

Sentiment analysis has emerged as one of the most popular natural language processing (NLP) tasks in recent years. A classic setting of the task mainly involves classifying the overall sentiment polarity of the inputs. However, it is based on the ass... Read More about Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis.

Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study (2018)
Journal Article
Arafat, S., Aljohani, N., Abbasi, R., Hussain, A., & Lytras, M. (2019). Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study. Computers in Human Behavior, 92, 478-486. https://doi.org/10.1016/j.chb.2018.02.026

In this paper we explore the interrelationship between the sociotechnical-pedagogical culture of e-learning, the emerging disciplines of Web science, Social Sensing and that of Cognitive Computation–as an emerging paradigm of computation. We comment... Read More about Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study.

Guided Policy Search for Sequential Multitask Learning (2018)
Journal Article
Xiong, F., Sun, B., Yang, X., Qiao, H., Huang, K., Hussain, A., & Liu, Z. (2019). Guided Policy Search for Sequential Multitask Learning. IEEE Transactions on Systems, Man and Cybernetics: Systems, 49(1), 216-226. https://doi.org/10.1109/tsmc.2018.2800040

Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection. A promising algorithm, known as guided pol... Read More about Guided Policy Search for Sequential Multitask Learning.

Spatial-temporal representatives selection and weighted patch descriptor for person re-identification (2018)
Journal Article
Zheng, A., Wang, F., Hussain, A., Tang, J., & Jiang, B. (2018). Spatial-temporal representatives selection and weighted patch descriptor for person re-identification. Neurocomputing, 290, 121-129. https://doi.org/10.1016/j.neucom.2018.02.039

How to represent the sequential person images is a crucial issue in multi-shot person re-identification. In this paper, we propose to select the spatial-temporal informative representatives to describe the image sequence. Specifically, we address rep... Read More about Spatial-temporal representatives selection and weighted patch descriptor for person re-identification.

A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition (2018)
Presentation / Conference Contribution
Gogate, M., Adeel, A., & Hussain, A. (2018). A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition. . https://doi.org/10.1109/SSCI.2017.8285377

The curse of dimensionality is a well-established phenomenon. However, the properties of high dimensional data are often poorly understood and overlooked during the process of data modelling and analysis. Similarly, how to optimally fuse different mo... Read More about A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition.

Deep learning driven multimodal fusion for automated deception detection (2018)
Presentation / Conference Contribution
Gogate, M., Adeel, A., & Hussain, A. (2018). Deep learning driven multimodal fusion for automated deception detection. . https://doi.org/10.1109/SSCI.2017.8285382

Humans ability to detect lies is no more accurate than chance according to the American Psychological Association. The state-of-the-art deception detection methods, such as deception detection stem from early theories and polygraph have proven to be... Read More about Deep learning driven multimodal fusion for automated deception detection.

Multi-modal Fusion (2018)
Journal Article
Liu, H., Hussain, A., & Wang, S. (2018). Multi-modal Fusion. Information Sciences, 432, 462. https://doi.org/10.1016/j.ins.2018.01.017

Combining deep convolutional neural network and SVM to SAR image target recognition (2018)
Presentation / Conference Contribution
Gao, F., Huang, T., Wang, J., Sun, J., Yang, E., & Hussain, A. (2018). Combining deep convolutional neural network and SVM to SAR image target recognition. . https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.165

To address the challenging problem on target recognition from synthetic aperture radar (SAR) images, a novel method is proposed by combining Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM). First, an improved DCNN is employe... Read More about Combining deep convolutional neural network and SVM to SAR image target recognition.

Message from the SmartData-2017 General Chairs (2018)
Presentation / Conference Contribution
Everson, R., Zhang, X., Li, P., & Hussain, A. (2018). Message from the SmartData-2017 General Chairs. . https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.193

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Applications of Deep Learning and Reinforcement Learning to Biological Data (2018)
Journal Article
Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018). Applications of Deep Learning and Reinforcement Learning to Biological Data. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2063-2079. https://doi.org/10.1109/tnnls.2018.2790388

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine... Read More about Applications of Deep Learning and Reinforcement Learning to Biological Data.

A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data (2018)
Journal Article
Abdullah, A., Hussain, A., & Khan, I. H. (2018). A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data. Cognitive Computation, 10(4), 591-609. https://doi.org/10.1007/s12559-017-9541-x

Globally, there has been a dramatic increase in obesity, with prevalence in males and females expected to increase to 18 and 21%, respectively (NCD Risk Factor Collaboration, Lancet 387(10026):1377–96, 2016). However, there are hardly any data-analyt... Read More about A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data.

A Novel Method of Signal Fusion Based on Dimension Expansion (2018)
Journal Article
Zhang, T., Xu, L., Yang, E., Yan, X., Qin, E. A., Wang, Q., & Hussain, A. (2018). A Novel Method of Signal Fusion Based on Dimension Expansion. Circuits, Systems, and Signal Processing, 37(10), 4295-4318. https://doi.org/10.1007/s00034-018-0760-5

A novel method of signal fusion, namely multi-dimensional unified signal (MDUS) fusion algorithm, is proposed based on dimensionality expansion of the cognitive radio (CR). The paper focuses on the issue of under-utilized and overcrowded spectrum ban... Read More about A Novel Method of Signal Fusion Based on Dimension Expansion.

Special Issue Editorial: Cognitively-Inspired Computing for Knowledge Discovery (2018)
Journal Article
Huang, K., Zhang, R., Jin, X., & Hussain, A. (2018). Special Issue Editorial: Cognitively-Inspired Computing for Knowledge Discovery. Cognitive Computation, 10(1), 1-2. https://doi.org/10.1007/s12559-017-9532-y

Abstract not available.

The Fundamental Code Unit of the Brain: Towards a New Model for Cognitive Geometry (2018)
Journal Article
Howard, N., & Hussain, A. (2018). The Fundamental Code Unit of the Brain: Towards a New Model for Cognitive Geometry. Cognitive Computation, 10(3), 426-436. https://doi.org/10.1007/s12559-017-9538-5

This paper discusses the problems arising from the multidisciplinary nature of cognitive research and the need to conceptually unify insights from multiple fields into the phenomena that drive cognition. Specifically, the Fundamental Code Unit (FCU)... Read More about The Fundamental Code Unit of the Brain: Towards a New Model for Cognitive Geometry.

Computational Intelligence Techniques for Mobile Network Optimization [Guest Editorial] (2018)
Journal Article
Qadir, J., Hussain, A., Yau, K., Imran, M., & Wolisz, A. (2018). Computational Intelligence Techniques for Mobile Network Optimization [Guest Editorial]. IEEE Computational Intelligence Magazine, 13(1), 28. https://doi.org/10.1109/MCI.2017.2773799

Modern society has become increasingly reliant on mobile networks for their communication needs. Such networks are characterized by their dynamic, heterogeneous, complex, and data intensive nature, which makes them more amenable to automated mobile n... Read More about Computational Intelligence Techniques for Mobile Network Optimization [Guest Editorial].

Knowledge-oriented leadership and innovation: A mediating role of knowledge creation: A case of software industry (2018)
Presentation / Conference Contribution
Mehmood, K., & Hussain, A. (2018). Knowledge-oriented leadership and innovation: A mediating role of knowledge creation: A case of software industry. . https://doi.org/10.1109/ICSAI.2017.8248548

This study describes the mediating role of knowledge creation between knowledge-oriented leadership and innovation. We investigate 150 respondents in the software industry of Pakistan. The path analysis demonstrates that knowledge creation mediates t... Read More about Knowledge-oriented leadership and innovation: A mediating role of knowledge creation: A case of software industry.

Managing association of information system outsourcing: A case of information technology (IT) service provider (2018)
Presentation / Conference Contribution
Mehmood, K., & Hussain, A. (2018). Managing association of information system outsourcing: A case of information technology (IT) service provider. . https://doi.org/10.1109/ICSAI.2017.8248558

Managing relationship is vital fundamental realistic concern in the accomplishment development of outsourcing information system (IS) and also begins to be a realistic dilemma that epidemic business of firms' information system. while the information... Read More about Managing association of information system outsourcing: A case of information technology (IT) service provider.

IEEE Access Special Section Editorial: Health Informatics for the Developing World (2017)
Journal Article
Qadir, J., Mujeeb-U-Rahman, M., Rehmani, M., Pathan, A., Imran, M., Hussain, A., Rana, R., & Luo, B. (2017). IEEE Access Special Section Editorial: Health Informatics for the Developing World. IEEE Access, 5, 27818-27823. https://doi.org/10.1109/ACCESS.2017.2783118

We live in a world with growing disparity in the quality of life available to people in the developed and developing countries. Healthcare in the developing world is fraught with numerous problems such as the lack of health infrastructure, and human... Read More about IEEE Access Special Section Editorial: Health Informatics for the Developing World.

Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data (2017)
Presentation / Conference Contribution
Abdullah, A., Hussain, A., & Khan, I. (2017, May). Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data. Presented at ICCDA '17: International Conference on Compute and Data Analysis, Lakeland, FL, USA

Globally there has been a dramatic increase in obesity. Thus understanding, predicting and managing obesity has the potential to save lives and billions. Behavioral studies suggest that binging by obese persons is prompted by inflated brain reward ce... Read More about Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data.

Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo (2017)
Presentation / Conference Contribution
Adeel, A., Gogate, M., & Hussain, A. (2017, August). Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo. Presented at 1st International Workshop on Challenges in Hearing Assistive Technology (CHAT 2017), Stockholm, Sweden

Speech enhancement aims to enhance the perceived speech quality and intelligibility in the presence of noise. Classical speech enhancement methods are mainly based on audio only processing which often perform poorly in adverse conditions, where overw... Read More about Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo.

Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique (2017)
Journal Article
Wajid, S. K., Hussain, A., & Huang, K. (2018). Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique. Expert Systems with Applications, 112, 388-400. https://doi.org/10.1016/j.eswa.2017.11.057

In this paper, we present a novel feature extraction technique, termed Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH), and exploit it to detect breast cancer in volumetric medical images. The technique is incorporated as part of an in... Read More about Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique.

Affective Reasoning for Big Social Data Analysis (2017)
Journal Article
Cambria, E., Hussain, A., & Vinciarelli, A. (2017). Affective Reasoning for Big Social Data Analysis. IEEE Transactions on Affective Computing, 8(4), 426-427. https://doi.org/10.1109/TAFFC.2017.2763218

This special section focuses on the introduction, presentation, and discussion of novel techniques that further develop and apply affective reasoning tools and techniques for big social data analysis. A key motivation for this special section, in par... Read More about Affective Reasoning for Big Social Data Analysis.

Complex-valued computational model of hippocampal CA3 recurrent collaterals (2017)
Presentation / Conference Contribution
Shiva, A., Gogate, M., Howard, N., Graham, B., & Hussain, A. (2017). Complex-valued computational model of hippocampal CA3 recurrent collaterals. . https://doi.org/10.1109/ICCI-CC.2017.8109745

Complex planes are known to simplify the complexity of real world problems, providing a better comprehension of their functionality and design. The need for complex numbers in both artificial and biological neural networks is equally well established... Read More about Complex-valued computational model of hippocampal CA3 recurrent collaterals.

Formal Ontology Generation by deep machine learning (2017)
Presentation / Conference Contribution
Wang, Y., Valipour, M., Zatarain, O., Gavrilova, M., Hussain, A., Howard, N., & Patel, S. (2017). Formal Ontology Generation by deep machine learning. . https://doi.org/10.1109/ICCI-CC.2017.8109723

An ontology is a taxonomic hierarchy of lexical terms and their syntactic and semantic relations for representing a framework of structured knowledge. Ontology used to be problem-specific and manually built due to its extreme complexity. Based on the... Read More about Formal Ontology Generation by deep machine learning.

Machine learning based computer-aided diagnosis of liver tumours (2017)
Presentation / Conference Contribution
Ali, L., Khelil, K., Wajid, S. K., Hussain, Z. U., Shah, M. A., Howard, A., …Hussain, A. (2017). Machine learning based computer-aided diagnosis of liver tumours. . https://doi.org/10.1109/ICCI-CC.2017.8109742

Image processing plays a vital role in the early detection and diagnosis of Hepatocellular Carcinoma (HCC). In this paper, we present a computational intelligence based Computer-Aided Diagnosis (CAD) system that helps medical specialists detect and d... Read More about Machine learning based computer-aided diagnosis of liver tumours.

Persian Named Entity Recognition (2017)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Algarafi, A., Howard, N., & Hussain, A. (2017, July). Persian Named Entity Recognition. Presented at 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Oxford, UK

Named Entity Recognition (NER) is an important natural language processing (NLP) tool for information extraction and retrieval from unstructured texts such as newspapers, blogs and emails. NER involves processing unstructured text for classification... Read More about Persian Named Entity Recognition.

Learning from Few Samples with Memory Network (2017)
Journal Article
Zhang, S., Huang, K., Zhang, R., & Hussain, A. (2018). Learning from Few Samples with Memory Network. Cognitive Computation, 10(1), 15-22. https://doi.org/10.1007/s12559-017-9507-z

Neural networks (NN) have achieved great successes in pattern recognition and machine learning. However, the success of a NN usually relies on the provision of a sufficiently large number of data samples as training data. When fed with a limited data... Read More about Learning from Few Samples with Memory Network.

Improve deep learning with unsupervised objective (2017)
Presentation / Conference Contribution
Zhang, S., Huang, K., Zhang, R., & Hussain, A. (2017). Improve deep learning with unsupervised objective. . https://doi.org/10.1007/978-3-319-70087-8_74

We propose a novel approach capable of embedding the unsupervised objective into hidden layers of the deep neural network (DNN) for preserving important unsupervised information. To this end, we exploit a very simple yet effective unsupervised method... Read More about Improve deep learning with unsupervised objective.

A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks (2017)
Journal Article
Anbar, M., Abdullah, R., Al-Tamimi, B. N., & Hussain, A. (2018). A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks. Cognitive Computation, 10(2), 201-214. https://doi.org/10.1007/s12559-017-9519-8

Router advertisement (RA) flooding attack aims to exhaust all node resources, such as CPU and memory, attached to routers on the same link. A biologically inspired machine learning-based approach is proposed in this study to detect RA flooding attack... Read More about A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks.

Semi-supervised learning for big social data analysis (2017)
Journal Article
Hussain, A., & Cambria, E. (2018). Semi-supervised learning for big social data analysis. Neurocomputing, 275, 1662-1673. https://doi.org/10.1016/j.neucom.2017.10.010

In an era of social media and connectivity, web users are becoming increasingly enthusiastic about interacting, sharing, and working together through online collaborative media. More recently, this collective intelligence has spread to many different... Read More about Semi-supervised learning for big social data analysis.

Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution (2017)
Journal Article
Ren, P., Sun, W., Luo, C., & Hussain, A. (2018). Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution. Cognitive Computation, 10(1), 165-178. https://doi.org/10.1007/s12559-017-9512-2

In contrast to the human visual system (HVS) that applies different processing schemes to visual information of different textural categories, most existing deep learning models for image super-resolution tend to exploit an indiscriminate scheme for... Read More about Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution.

A Bayesian Assessment of Real-World Behavior During Multitasking (2017)
Journal Article
Bergmann, J., Fei, J., Green, D., Hussain, A., & Howard, N. (2017). A Bayesian Assessment of Real-World Behavior During Multitasking. Cognitive Computation, 9, 749-757. https://doi.org/10.1007/s12559-017-9500-6

Multitasking is common in everyday life, but its effect on activities of daily living is not well understood. Critical appraisal of performance for both healthy individuals and patients is required. Motor activities during meal preparation were monit... Read More about A Bayesian Assessment of Real-World Behavior During Multitasking.

A novel decision support system for the interpretation of remote sensing big data (2017)
Journal Article
Boulila, W., Farah, I. R., & Hussain, A. (2018). A novel decision support system for the interpretation of remote sensing big data. Earth Science Informatics, 11(1), 31-45. https://doi.org/10.1007/s12145-017-0313-7

Applications of remote sensing (RS) data cover several fields such as: cartography, surveillance, land-use planning, archaeology, environmental studies, resources management, etc. However, the amount of RS data has grown considerably due to the incre... Read More about A novel decision support system for the interpretation of remote sensing big data.

A Review of Sentiment Analysis Research in Chinese Language (2017)
Journal Article
Peng, H., Cambria, E., & Hussain, A. (2017). A Review of Sentiment Analysis Research in Chinese Language. Cognitive Computation, 9(4), 423-435. https://doi.org/10.1007/s12559-017-9470-8

Research on sentiment analysis in English language has undergone major developments in recent years. Chinese sentiment analysis research, however, has not evolved significantly despite the exponential growth of Chinese e-business and e-markets. This... Read More about A Review of Sentiment Analysis Research in Chinese Language.

Dual-branch deep convolution neural network for polarimetric SAR image classification (2017)
Journal Article
Gao, F., Huang, T., Wang, J., Sun, J., Hussain, A., & Yang, E. (2017). Dual-branch deep convolution neural network for polarimetric SAR image classification. Applied Sciences, 7(5), https://doi.org/10.3390/app7050447

The deep convolution neural network (CNN), which has prominent advantages in feature learning, can learn and extract features from data automatically. Existing polarimetric synthetic aperture radar (PolSAR) image classification methods based on the C... Read More about Dual-branch deep convolution neural network for polarimetric SAR image classification.

Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques (2017)
Presentation / Conference Contribution
Wajid, S., Hussain, A., Huang, K., & Boulila, W. (2017). Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques. . https://doi.org/10.1109/ICCI-CC.2016.7862060

The novel application of Local Energy-based Shape Histogram (LESH) feature extraction technique was recently proposed for breast cancer diagnosis using mammogram images [22]. This paper extends our original work to apply the LESH technique to detect... Read More about Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques.

Genetic optimization of fuzzy membership functions for cloud resource provisioning (2017)
Presentation / Conference Contribution
Ullah, A., Li, J., Hussain, A., & Shen, Y. (2017). Genetic optimization of fuzzy membership functions for cloud resource provisioning. . https://doi.org/10.1109/SSCI.2016.7850088

The successful usage of fuzzy systems can be seen in many application domains owing to their capabilities to model complex systems by exploiting knowledge of domain experts. Their accuracy and performance are, however, primarily dependent on the desi... Read More about Genetic optimization of fuzzy membership functions for cloud resource provisioning.

Group sparse regularization for deep neural networks (2017)
Journal Article
Scardapane, S., Comminiello, D., Hussain, A., & Uncini, A. (2017). Group sparse regularization for deep neural networks. Neurocomputing, 241, 81-89. https://doi.org/10.1016/j.neucom.2017.02.029

In this paper, we address the challenging task of simultaneously optimizing (i) the weights of a neural network, (ii) the number of neurons for each hidden layer, and (iii) the subset of active input features (i.e., feature selection). While these pr... Read More about Group sparse regularization for deep neural networks.

Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis (2017)
Journal Article
Poria, S., Peng, H., Hussain, A., Howard, N., & Cambria, E. (2017). Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis. Neurocomputing, 261, 217-230. https://doi.org/10.1016/j.neucom.2016.09.117

The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with a global deluge of videos from billions of computers... Read More about Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis.

A review of affective computing: From unimodal analysis to multimodal fusion (2017)
Journal Article
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37, 98-125. https://doi.org/10.1016/j.inffus.2017.02.003

Affective computing is an emerging interdisciplinary research field bringing together researchers and practitioners from various fields, ranging from artificial intelligence, natural language processing, to cognitive and social sciences. With the pro... Read More about A review of affective computing: From unimodal analysis to multimodal fusion.

Convolutional MKL based multimodal emotion recognition and sentiment analysis (2017)
Presentation / Conference Contribution
Poria, S., Chaturvedi, I., Cambria, E., & Hussain, A. (2016, December). Convolutional MKL based multimodal emotion recognition and sentiment analysis. Presented at 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain

Technology has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. Much of the content being posted and consumed online is multimodal. With billions o... Read More about Convolutional MKL based multimodal emotion recognition and sentiment analysis.

Ensemble based majority voting for point-to-point measurements of Gyrodactylus species identification (2017)
Journal Article
Ali, R., Hussain, A., & Abel, A. (2017). Ensemble based majority voting for point-to-point measurements of Gyrodactylus species identification. ARPN Journal of Engineering and Applied Sciences, 12(2), 310-316

In the 21st Century, a key challenge in both wild and cultured fish populations for control and management of disease is to securely and consistently perform pathogen identification. To provide automated accurate classification for the challeng... Read More about Ensemble based majority voting for point-to-point measurements of Gyrodactylus species identification.

Customer churn prediction in the telecommunication sector using a rough set approach (2016)
Journal Article
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, 242-254. https://doi.org/10.1016/j.neucom.2016.12.009

Customer churn is a critical and challenging problem affecting business and industry, in particular, the rapidly growing, highly competitive telecommunication sector. It is of substantial interest to both academic researchers and industrial practitio... Read More about Customer churn prediction in the telecommunication sector using a rough set approach.

An exploratory case study of interactive simulation for teaching Ecology (2016)
Presentation / Conference Contribution
Ameerbakhsh, O., Maharaj, S., Hussain, A., Paine, T., & Taiksi, S. (2016, September). An exploratory case study of interactive simulation for teaching Ecology. Presented at 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET), Istanbul, Turkey

This paper explores the effectiveness of interactive simulation for teaching a selected complex subject, Ecology, in higher education. Specifically, we carry out a lab intervention using interactive agent based simulation, to teach the complex concep... Read More about An exploratory case study of interactive simulation for teaching Ecology.

Extracting online information from dual and multiple data streams (2016)
Journal Article
Malik, Z. K., Hussain, A., & Wu, Q. M. J. (2018). Extracting online information from dual and multiple data streams. Neural Computing and Applications, 30(1), 87-98. https://doi.org/10.1007/s00521-016-2647-3

In this paper, we consider the challenging problem of finding shared information in multiple data streams simultaneously. The standard statistical method for doing this is the well-known canonical correlation analysis (CCA) approach. We begin by deve... Read More about Extracting online information from dual and multiple data streams.

A data driven approach to audiovisual speech mapping (2016)
Presentation / Conference Contribution
Abel, A., Marxer, R., Barker, J., Watt, R., Whitmer, B., Derleth, P., & Hussain, A. (2016). A data driven approach to audiovisual speech mapping. In Advances in Brain Inspired Cognitive Systems (331-342). https://doi.org/10.1007/978-3-319-49685-6_30

The concept of using visual information as part of audio speech processing has been of significant recent interest. This paper presents a data driven approach that considers estimating audio speech acoustics using only temporal visual information wit... Read More about A data driven approach to audiovisual speech mapping.

A new biologically-inspired analytical worm propagation model for mobile unstructured peer-to-peer networks (2016)
Presentation / Conference Contribution
Alharbi, H., Aloufi, K., & Hussain, A. (2016, November). A new biologically-inspired analytical worm propagation model for mobile unstructured peer-to-peer networks. Presented at 8th International Conference, BICS 2016, Beijing, China

Millions of users world-wide are sharing content using the Peer-to-Peer (P2P) client network. While new innovations bring benefits, there are nevertheless some dangers associated with them. One of the main threats is P2P worms that can penetrate the... Read More about A new biologically-inspired analytical worm propagation model for mobile unstructured peer-to-peer networks.

A novel fully automated liver and HCC tumor segmentation system using morphological operations (2016)
Presentation / Conference Contribution
Ali, L., Hussain, A., Li, J., Howard, N., Shah, A., Sudhakar, U., Shah, M., & Hussain, Z. (2016, November). A novel fully automated liver and HCC tumor segmentation system using morphological operations. Presented at 8th International Conference, BICS 2016, Beijing, China

Early detection and diagnosis of Hepatocellular Carcinoma (HCC) is the most discriminating step in liver cancer management. Image processing is primarily used, where fast and accurate Computed Tomography (CT) liver image segmentation is required for... Read More about A novel fully automated liver and HCC tumor segmentation system using morphological operations.

An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs) (2016)
Presentation / Conference Contribution
Wajid, S. K., Hussain, A., Luo, B., & Huang, K. (2016, November). An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs). Presented at BICS 2016: 8th International Conference on Brain Inspired Cognitive Systems, Beijing, China

This paper reviews the state of the art techniques for designing next generation CDSSs. CDSS can aid physicians and radiologists to better analyse and treat patients by combining their respective clinical expertise with complementary capabilities of... Read More about An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs).

Continuous time recurrent neural network model of recurrent collaterals in the hippocampus CA3 region (2016)
Presentation / Conference Contribution
Shiva, A. S., & Hussain, A. (2016, November). Continuous time recurrent neural network model of recurrent collaterals in the hippocampus CA3 region. Presented at BICS 2016: 8th International Conference on Brain Inspired Cognitive Systems, Beijing, China

Recurrent collaterals in the brain represent the recollection and execution of various monotonous activities such as breathing, brushing our teeth, chewing, walking, etc. These recurrent collaterals are found throughout the brain, each pertaining to... Read More about Continuous time recurrent neural network model of recurrent collaterals in the hippocampus CA3 region.

Deep and sparse learning in speech and language processing: An overview (2016)
Presentation / Conference Contribution
Wang, D., Zhou, Q., & Hussain, A. (2016). Deep and sparse learning in speech and language processing: An overview. In Advances in Brain Inspired Cognitive Systems (171-183). https://doi.org/10.1007/978-3-319-49685-6_16

Large-scale deep neural models, e.g., deep neural networks (DNN) and recurrent neural networks (RNN), have demonstrated significant success in solving various challenging tasks of speech and language processing (SLP), including speech recognition, sp... Read More about Deep and sparse learning in speech and language processing: An overview.

Modified cat swarm optimization for clustering (2016)
Presentation / Conference Contribution
Razzaq, S., Maqbool, F., & Hussain, A. (2016, November). Modified cat swarm optimization for clustering. Presented at BICS 2016: International Conference on Brain Inspired Cognitive Systems, Beijing, China

Clustering is one of the most challenging optimization problems. Many Swarm Intelligence techniques including Ant Colony optimization (ACO), Particle Swarm Optimization (PSO), and Honey Bee Optimization (HBO) have been used to solve clustering. Cat S... Read More about Modified cat swarm optimization for clustering.

PerSent: A freely available Persian sentiment lexicon (2016)
Presentation / Conference Contribution
Dashtipour, K., Hussain, A., Zhou, Q., Gelbukh, A., Hawalah, A. Y. A., & Cambria, E. (2016, November). PerSent: A freely available Persian sentiment lexicon. Presented at BICS 2016: International Conference on Brain Inspired Cognitive Systems, Beijing, China

People need to know other people’s opinions to make well-informed decisions to buy products or services. Companies and organizations need to understand people’s attitude towards their products and services and use feedback from the customers to impro... Read More about PerSent: A freely available Persian sentiment lexicon.

Predicting insulin resistance in children using a machine-learning-based clinical decision support system (2016)
Presentation / Conference Contribution
Hall, A. J., Hussain, A., & Shaikh, M. G. (2016, November). Predicting insulin resistance in children using a machine-learning-based clinical decision support system. Presented at BICS 2016: International Conference on Brain Inspired Cognitive Systems, Beijing, China

This study proposes a new diagnostic approach based on application of machine learning techniques to anthropometric patient features in order to create a predictive model capable of diagnosing insulin resistance (HOMA-IR).

As part of the study, a... Read More about Predicting insulin resistance in children using a machine-learning-based clinical decision support system.

Visual attention model with a novel learning strategy and its application to target detection from SAR images (2016)
Presentation / Conference Contribution
Gao, F., Xue, X., Wang, J., Sun, J., Hussain, A., & Yang, E. (2016, November). Visual attention model with a novel learning strategy and its application to target detection from SAR images. Presented at BICS 2016: International Conference on Brain Inspired Cognitive Systems, Beijing, China

The selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn lots of research attention and man... Read More about Visual attention model with a novel learning strategy and its application to target detection from SAR images.

Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study (2016)
Journal Article
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A. (2016). Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study. IEEE Access, 4, 7940-7957. https://doi.org/10.1109/ACCESS.2016.2619719

Customer retention is a major issue for various service-based organizations particularly telecom industry, wherein predictive models for observing the behavior of customers are one of the great instruments in customer retention process and inferring... Read More about Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study.

Distributed Reservoir Computing with Sparse Readouts [Research Frontier] (2016)
Journal Article
Scardapane, S., Panella, M., Comminiello, D., Hussain, A., & Uncini, A. (2016). Distributed Reservoir Computing with Sparse Readouts [Research Frontier]. IEEE Computational Intelligence Magazine, 11(4), 59-70. https://doi.org/10.1109/MCI.2016.2601759

In a network of agents, a widespread problem is the need to estimate a common underlying function starting from locally distributed measurements. Real-world scenarios may not allow the presence of centralized fusion centers, requiring the development... Read More about Distributed Reservoir Computing with Sparse Readouts [Research Frontier].

Advances in Brain-Inspired Cognitive Systems (2016)
Journal Article
Luo, B., Hussain, A., Mahmud, M., & Tang, J. (2016). Advances in Brain-Inspired Cognitive Systems. Cognitive Computation, 8(5), 795-796. https://doi.org/10.1007/s12559-016-9431-7

An Agent-Based Approach for Modelling Peer to Peer Networks (2016)
Presentation / Conference Contribution
Alharbi, H., & Hussain, A. (2016). An Agent-Based Approach for Modelling Peer to Peer Networks. In 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim) (532-537). https://doi.org/10.1109/UKSim.2015.47

A promising modelling and simulation tool is Agent-based Modelling (ABM) that has proved to be an effective and powerful tool across a wide range of fields. However, its exploitation within the Peer to Peer (P2P) paradigm has only recently attracted... Read More about An Agent-Based Approach for Modelling Peer to Peer Networks.

Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching (2016)
Journal Article
Tran, H., Cambria, E., & Hussain, A. (2016). Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching. Cognitive Computation, 8(6), 1074-1086. https://doi.org/10.1007/s12559-016-9418-4

Background/Introduction
Common-sense reasoning is concerned with simulating cognitive human ability to make presumptions about the type and essence of ordinary situations encountered every day. The most popular way to represent common-sense knowledg... Read More about Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching.

Erratum to: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques (2016)
Journal Article
Dashtipour, K., Poria, S., Hussain, A., Cambria, E., Hawalah, A., Gelbukh, A., & Zhou, Q. (2016). Erratum to: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques. Cognitive Computation, 8(4), 772-775. https://doi.org/10.1007/s12559-016-9421-9

Erratum to: Cogn Comput DOI 10.1007/s12559-016-9415-7

A novel ontology and machine learning driven hybrid cardiovascular clinical prognosis as a complex adaptive clinical system (2016)
Journal Article
Farooq, K., & Hussain, A. (2016). A novel ontology and machine learning driven hybrid cardiovascular clinical prognosis as a complex adaptive clinical system. Complex Adaptive Systems Modeling, 4, https://doi.org/10.1186/s40294-016-0023-x

Purpose
This multidisciplinary industrial research project sets out to develop a hybrid clinical decision support mechanism (inspired by ontology and machine learning driven techniques) by combining evidence, extrapolated through legacy patient data... Read More about A novel ontology and machine learning driven hybrid cardiovascular clinical prognosis as a complex adaptive clinical system.

Multilayered Echo State Machine: A Novel Architecture and Algorithm (2016)
Journal Article
Malik, Z., Hussain, A., & Wu, Q. (2017). Multilayered Echo State Machine: A Novel Architecture and Algorithm. IEEE Transactions on Cybernetics, 47(4), 946-959. https://doi.org/10.1109/TCYB.2016.2533545

In this paper, we present a novel architecture and learning algorithm for a multilayered echo state machine (ML-ESM). Traditional echo state networks (ESNs) refer to a particular type of reservoir computing (RC) architecture. They constitute an effec... Read More about Multilayered Echo State Machine: A Novel Architecture and Algorithm.

Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques (2016)
Journal Article
Dashtipour, K., Poria, S., Hussain, A., Cambria, E., Hawalah, A. Y. A., Gelbukh, A., & Zhou, Q. (2016). Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques. Cognitive Computation, 8(4), 757-771. https://doi.org/10.1007/s12559-016-9415-7

With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. How... Read More about Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques.

From Spin to Swindle: Identifying Falsification in Financial Text (2016)
Journal Article
Minhas, S., & Hussain, A. (2016). From Spin to Swindle: Identifying Falsification in Financial Text. Cognitive Computation, 8(4), 729-745. https://doi.org/10.1007/s12559-016-9413-9

Despite legislative attempts to curtail financial statement fraud, it continues unabated. This study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis with data mining on narrative sections of annual reports/10-K f... Read More about From Spin to Swindle: Identifying Falsification in Financial Text.

Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images (2016)
Journal Article
Gao, F., Ma, F., Zhang, Y., Wang, J., Sun, J., Yang, E., & Hussain, A. (2016). Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images. Cognitive Computation, 8(5), 955-966. https://doi.org/10.1007/s12559-016-9405-9

High-resolution synthetic aperture radar (SAR) can provide a rich information source for target detection and greatly increase the types and number of target characteristics. How to efficiently extract the target of interest from large amounts of SAR... Read More about Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images.

A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair (2016)
Journal Article
Kaiser, M., Chowdhury, Z., Mamun, S., Hussain, A., & Mahmud, M. (2016). A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair. Cognitive Computation, 8(5), 946-954. https://doi.org/10.1007/s12559-016-9398-4

This paper presents the design and implementation of a low-cost solar-powered wheelchair for physically challenged people. The signals necessary to maneuver the wheelchair are acquired from different muscles of the hand using surface electromyography... Read More about A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair.

A New Spatio-Temporal Saliency-Based Video Object Segmentation (2016)
Journal Article
Tu, Z., Abel, A., Zhang, L., Luo, B., & Hussain, A. (2016). A New Spatio-Temporal Saliency-Based Video Object Segmentation. Cognitive Computation, 8(4), 629-647. https://doi.org/10.1007/s12559-016-9387-7

Humans and animals are able to segment visual scenes by having the natural cognitive ability to quickly identify salient objects in both static and dynamic scenes. In this paper, we present a new spatio-temporal-based approach to video object segment... Read More about A New Spatio-Temporal Saliency-Based Video Object Segmentation.

Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning (2016)
Journal Article
Ullah, A., Li, J., Hussain, A., & Yang, E. (2016). Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning. Cognitive Computation, 8(5), 992-1005. https://doi.org/10.1007/s12559-016-9391-y

Cloud elasticity augments applications to dynamically adapt to changes in demand by acquiring or releasing computational resources on the fly. Recently, we developed a framework for cloud elasticity utilizing multiple feedback controllers simultaneou... Read More about Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning.

Unsupervised Commonsense Knowledge Enrichment for Domain-Specific Sentiment Analysis (2016)
Journal Article
Ofek, N., Poria, S., Rokach, L., Cambria, E., Hussain, A., & Shabtai, A. (2016). Unsupervised Commonsense Knowledge Enrichment for Domain-Specific Sentiment Analysis. Cognitive Computation, 8(3), 467-477. https://doi.org/10.1007/s12559-015-9375-3

Sentiment analysis in natural language text is a challenging task involving a deep understanding of both syntax and semantics. Leveraging the polarity of multiword expressions—or concepts—rather than single words can mitigate the difficulty of such a... Read More about Unsupervised Commonsense Knowledge Enrichment for Domain-Specific Sentiment Analysis.

ELM based algorithms for acoustic template matching in home automation scenarios: Advancements and performance analysis (2016)
Book Chapter
della Porta, G., Principi, E., Ferroni, G., Squartini, S., Hussain, A., & Piazza, F. (2016). ELM based algorithms for acoustic template matching in home automation scenarios: Advancements and performance analysis. In Recent Advances in Nonlinear Speech Processing (159-168). Springer. https://doi.org/10.1007/978-3-319-28109-4_16

Speech and sound recognition in home automation scenarios has been gaining an increasing interest in the last decade. One interesting approach addressed in the literature is based on the template matching paradigm, which is characterized by ease of i... Read More about ELM based algorithms for acoustic template matching in home automation scenarios: Advancements and performance analysis.

Integrating twitter traffic information with kalman filter models for public transportation vehicle arrival time prediction (2016)
Book Chapter
Abidin, A. F., Kolberg, M., & Hussain, A. (2016). Integrating twitter traffic information with kalman filter models for public transportation vehicle arrival time prediction. In M. Trovati, R. Hill, A. Anjum, S. Ying Zhu, & L. Liu (Eds.), Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications (67-82). Springer. https://doi.org/10.1007/978-3-319-25313-8_5

Accurate bus arrival time prediction is key for improving the attractiveness of public transport, as it helps users better manage their travel schedule. This paper proposes a model of bus arrival time prediction, which aims to improve arrival time ac... Read More about Integrating twitter traffic information with kalman filter models for public transportation vehicle arrival time prediction.

A novel ontology and machine learning inspired hybrid cardiovascular decision support framework (2015)
Presentation / Conference Contribution
Hussain, A., Farooq, K., Luo, B., & Slack, W. (2015, December). A novel ontology and machine learning inspired hybrid cardiovascular decision support framework. Presented at 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa

Healthcare information management systems (HIMS) have a substantial amount of limitations such as rigidity and nonconformity to complex clinical processes like Electronic Healthcare records and effective utilisation of clinical practice guidelines to... Read More about A novel ontology and machine learning inspired hybrid cardiovascular decision support framework.

Cognitively Inspired Audiovisual Speech Filtering: Towards an Intelligent, Fuzzy Based, Multimodal, Two-Stage Speech Enhancement System (2015)
Book
Abel, A., & Hussain, A. (2015). Cognitively Inspired Audiovisual Speech Filtering: Towards an Intelligent, Fuzzy Based, Multimodal, Two-Stage Speech Enhancement System. Cham: Springer. https://doi.org/10.1007/978-3-319-13509-0

This book presents a summary of the cognitively inspired basis behind multimodal speech enhancement, covering the relationship between audio and visual modalities in speech, as well as recent research into audiovisual speech correlation. A number of... Read More about Cognitively Inspired Audiovisual Speech Filtering: Towards an Intelligent, Fuzzy Based, Multimodal, Two-Stage Speech Enhancement System.

Electronics, communications and networks IV: Proceedings of the 4th international conference on electronics, communications and networks, 12-15 December 2014, Beijing, China (2015)
Presentation / Conference Contribution
Hussain, A., & Ivanovic, M. (2015). Electronics, communications and networks IV: Proceedings of the 4th international conference on electronics, communications and networks, 12-15 December 2014, Beijing, China.

The 4th International Conference on Electronic, Communications and Networks (CECNet2014) inherits the fruitfulness of the past three conferences and lays a foundation for the forthcoming next year in Shanghai. CECNet2014 was hosted by Hubei Universit... Read More about Electronics, communications and networks IV: Proceedings of the 4th international conference on electronics, communications and networks, 12-15 December 2014, Beijing, China.

Sentic Computing (2015)
Journal Article
Cambria, E., & Hussain, A. (2015). Sentic Computing. Cognitive Computation, 7, 183-185. https://doi.org/10.1007/s12559-015-9325-0

No abstract available

Solar powered wheel chair for physically challenged people using surface EMG signal (2015)
Presentation / Conference Contribution
Kaiser, S., Chowdhury, Z. I., Mamun, S., Hussain, A., & Mahmud, M. (2015, December). Solar powered wheel chair for physically challenged people using surface EMG signal. Presented at 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa

This paper presents the design of low cost solar powered wheel chair for physically challenged people. The signals necessary to maneuver the wheel chair are acquired from different muscles of the hand using surface Electromyography (sEMG) technique.... Read More about Solar powered wheel chair for physically challenged people using surface EMG signal.

Efficient text localization in born-digital images by local contrast-based segmentation (2015)
Presentation / Conference Contribution
Chen, K., Yin, F., Hussain, A., & Liu, C. (2015, August). Efficient text localization in born-digital images by local contrast-based segmentation. Presented at 2015 13th International Conference on Document Analysis and Recognition (ICDAR), Tunis, Tunisia

Text localization in born-digital images is usually performed using methods designed for scene text images. Based on the observation that text strokes in born-digital images mostly have complete contours and the pixels on the contours have high contr... Read More about Efficient text localization in born-digital images by local contrast-based segmentation.

Introduction: Dealing with Big Data-Lessons from Cognitive Computing (2015)
Journal Article
Abdullah, A., Hussain, A., & Khan, I. H. (2015). Introduction: Dealing with Big Data-Lessons from Cognitive Computing. Cognitive Computation, 7(6), 635-636. https://doi.org/10.1007/s12559-015-9364-6

No abstract available

Computational Intelligence for Changing Environments [Guest Editorial] (2015)
Journal Article
Hussain, A., Tao, D., Wu, J., & Zhao, D. (2015). Computational Intelligence for Changing Environments [Guest Editorial]. IEEE Computational Intelligence Magazine, 10(4), 10-11. https://doi.org/10.1109/MCI.2015.2472119

The articles in this special section focus on the growing interest in biologically inspired learning (BIL), which refers to a wide range of learning techniques, motivated by biology, that try to mimic specific biological functions or behaviors.

Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns (2015)
Journal Article
Poria, S., Cambria, E., Gelbukh, A., Bisio, F., & Hussain, A. (2015). Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns. IEEE Computational Intelligence Magazine, 10(4), 26-36. https://doi.org/10.1109/MCI.2015.2471215

Emulating the human brain is one of the core challenges of computational intelligence, which entails many key problems of artificial intelligence, including understanding human language, reasoning, and emotions. In this work, computational intelligen... Read More about Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns.

A Novel Near-Infrared Spectroscopy Based Spatiotemporal Cognition Study of the Human Brain Using Clustering (2015)
Journal Article
Abdullah, A., Khan, I. H., Basuhail, A., & Hussain, A. (2015). A Novel Near-Infrared Spectroscopy Based Spatiotemporal Cognition Study of the Human Brain Using Clustering. Cognitive Computation, 7(6), 693-705. https://doi.org/10.1007/s12559-015-9358-4

In this study, we investigate how the two hemispheres of the brain are involved spatiotemporally in a cognitive-based setup when people relate different colors with different concepts (for example, the color ‘blue’ associated with the word ‘dependabl... Read More about A Novel Near-Infrared Spectroscopy Based Spatiotemporal Cognition Study of the Human Brain Using Clustering.

An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data (2015)
Journal Article
Malik, Z. K., Hussain, A., & Wu, J. (2016). An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data. Neurocomputing, 173(2), 127-136. https://doi.org/10.1016/j.neucom.2014.12.119

This paper presents a generalized incremental Laplacian Eigenmaps (GENILE), a novel online version of the Laplacian Eigenmaps, one of the most popular manifold-based dimensionality reduction techniques which solves the generalized eigenvalue problem.... Read More about An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data.

Fusing audio, visual and textual clues for sentiment analysis from multimodal content (2015)
Journal Article
Poria, S., Cambria, E., Howard, N., Huang, G.-B., & Hussain, A. (2016). Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing, 174(Part A), 50-59. https://doi.org/10.1016/j.neucom.2015.01.095

A huge number of videos are posted every day on social media platforms such as Facebook and YouTube. This makes the Internet an unlimited source of information. In the coming decades, coping with such information and mining useful knowledge from it w... Read More about Fusing audio, visual and textual clues for sentiment analysis from multimodal content.

A novel classification algorithm based on incremental semi-supervised support vector machine (2015)
Journal Article
Gao, F., Mei, J., Sun, J., Wang, J., Yang, E., & Hussain, A. (2015). A novel classification algorithm based on incremental semi-supervised support vector machine. PLOS ONE, 10(8), Article e0135709. https://doi.org/10.1371/journal.pone.0135709

For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In th... Read More about A novel classification algorithm based on incremental semi-supervised support vector machine.

Integrating SUMO and kalman filter models towards a social network based approach of public transport arrival time prediction (2015)
Journal Article
Abidin, A. F., Kolberg, M., & Hussain, A. (2015). Integrating SUMO and kalman filter models towards a social network based approach of public transport arrival time prediction. International Journal of Simulation: Systems, Science & Technology, 16(3), 5.1-5.9. https://doi.org/10.5013/IJSSST.a.16.03.05

Bus arrival time is a key service for improving public transport attractiveness by providing users with an accurate arrival time. In this research, a model of bus arrival time prediction, which aims to improve arrival time accuracy, is proposed. The... Read More about Integrating SUMO and kalman filter models towards a social network based approach of public transport arrival time prediction.

Local energy-based shape histogram feature extraction technique for breast cancer diagnosis (2015)
Journal Article
Wajid, S. K., & Hussain, A. (2015). Local energy-based shape histogram feature extraction technique for breast cancer diagnosis. Expert Systems with Applications, 42(20), 6990-6999. https://doi.org/10.1016/j.eswa.2015.04.057

This paper proposes a novel local energy-based shape histogram (LESH) as the feature set for recognition of abnormalities in mammograms. It investigates the implication of this technique on mammogram datasets of the Mammographic Image Analysis Societ... Read More about Local energy-based shape histogram feature extraction technique for breast cancer diagnosis.

Discriminative bi-term topic model for headline-based social news clustering (2015)
Presentation / Conference Contribution
Xia, Y., Tang, N., Hussain, A., & Cambria, E. (2015, May). Discriminative bi-term topic model for headline-based social news clustering. Presented at The Twenty-Eighth International Florida Artificial Intelligence Research Society Conference (FLAIRS), Hollywood, Florida

Social news are becoming increasingly popular. News organizations and popular journalists are starting to use social media more and more heavily for broadcasting news. The major challenge in social news clustering lies in the fact that textual conten... Read More about Discriminative bi-term topic model for headline-based social news clustering.

A Biologically Inspired Vision-Based Approach for Detecting Multiple Moving Objects in Complex Outdoor Scenes (2015)
Journal Article
Tu, Z., Zheng, A., Yang, E., Luo, B., & Hussain, A. (2015). A Biologically Inspired Vision-Based Approach for Detecting Multiple Moving Objects in Complex Outdoor Scenes. Cognitive Computation, 7(5), 539-551. https://doi.org/10.1007/s12559-015-9318-z

In the human brain, independent components of optical flows from the medial superior temporal area are speculated for motion cognition. Inspired by this hypothesis, a novel approach combining independent component analysis (ICA) with principal compon... Read More about A Biologically Inspired Vision-Based Approach for Detecting Multiple Moving Objects in Complex Outdoor Scenes.

A localization toolkit for sentic net (2015)
Presentation / Conference Contribution
Xia, Y., Li, X., Cambria, E., & Hussain, A. (2015). A localization toolkit for sentic net. In 2014 IEEE International Conference on Data Mining Workshop (403-408). https://doi.org/10.1109/ICDMW.2014.179

SenticNet is a popular resource for concept-level sentiment analysis. Because SenticNet was created specifically for opinion mining in English language, however, its localization can be very laborious. In this work, a toolkit for creating non-English... Read More about A localization toolkit for sentic net.

Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach (2015)
Journal Article
Agarwal, B., Poria, S., Mittal, N., Gelbukh, A., & Hussain, A. (2015). Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach. Cognitive Computation, 7(4), 487-499. https://doi.org/10.1007/s12559-014-9316-6

Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In the frame of biologically inspired machine learning approaches, finding good feature sets is particularly challeng... Read More about Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach.

A novel cardiovascular decision support framework for effective clinical risk assessment (2015)
Presentation / Conference Contribution
Farooq, K., Karasek, J., Atassi, H., Hussain, A., Yang, P., MacRae, C., Mahmud, M., Luo, B., & Slack, W. (2014, December). A novel cardiovascular decision support framework for effective clinical risk assessment. Presented at 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), Orlando, FL, USA

The aim of this study is to help improve the diagnostic and performance capabilities of Rapid Access Chest Pain Clinics (RACPC), by reducing delay and inaccuracies in the cardiovascular risk assessment of patients with chest pain by helping clinician... Read More about A novel cardiovascular decision support framework for effective clinical risk assessment.

An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier (2015)
Presentation / Conference Contribution
Wajid, S. K., Hussain, A., & Luo, B. (2014, December). An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier. Presented at 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), Orlando, FL, USA

The paper presents Echo State Network (ESN) as classifier to diagnose the abnormalities in mammogram images. Abnormalities in mammograms can be of different types. An efficient system which can handle these abnormalities and draw correct diagnosis is... Read More about An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier.

Cognitively inspired speech processing for multimodal hearing technology (2015)
Presentation / Conference Contribution
Abel, A., Hussain, A., & Luo, B. (2014, December). Cognitively inspired speech processing for multimodal hearing technology. Presented at 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), Orlando, FL, USA

In recent years, the link between the various human communication production domains has become more widely utilised in the field of speech processing. Work by the authors and others has demonstrated that intelligently integrated audio and visual inf... Read More about Cognitively inspired speech processing for multimodal hearing technology.

Intelligent image processing techniques for cancer progression detection, recognition and prediction in the human liver (2015)
Presentation / Conference Contribution
Ali, L., Hussain, A., Li, J., Shah, A., Sudhakr, U., Mahmud, M., Zakir, U., Yan, X., Luo, B., & Rajak, M. (2014, December). Intelligent image processing techniques for cancer progression detection, recognition and prediction in the human liver. Presented at 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), Orlando, FL, USA

Clinical Decision Support (CDS) aids in early diagnosis of liver cancer, a potentially fatal disease prevalent in both developed and developing countries. Our research aims to develop a robust and intelligent clinical decision support framework for d... Read More about Intelligent image processing techniques for cancer progression detection, recognition and prediction in the human liver.

The development of an intelligent tutorial system for system development (2015)
Presentation / Conference Contribution
Al-Jumeily, D., Hussain, A., Alghamdi, M., Lamb, D., & Hamdan, H. (2014, November). The development of an intelligent tutorial system for system development. Presented at 2014 International Conference on Web and Open Access to Learning (ICWOAL), Dubai, United Arab Emirates

Educational software has frequently been criticized as it has not been explicitly planned to meet the demands of educational environment. Therefore, there is an increasing demand for an intelligent computer technology to become used in the environmen... Read More about The development of an intelligent tutorial system for system development.

Convergence Analysis of a New Self Organizing Map Based Optimization (SOMO) Algorithm (2015)
Journal Article
Khan, A., Xue, L. Z., Wei, W., Qu, Y. P., Hussain, A., & Vencio, R. Z. N. (2015). Convergence Analysis of a New Self Organizing Map Based Optimization (SOMO) Algorithm. Cognitive Computation, 7(4), 477-486. https://doi.org/10.1007/s12559-014-9315-7

The self-organizing map (SOM) approach has been used to perform cognitive and biologically inspired computing in a growing range of cross-disciplinary fields. Recently, the SOM based neural network framework was adapted to solve continuous derivative... Read More about Convergence Analysis of a New Self Organizing Map Based Optimization (SOMO) Algorithm.

A novel refined track initiation algorithm for group targets based on group model (2014)
Journal Article
Gao, F., Ren, H., Wang, J., Hussain, A., & Durrani, T. S. (2014). A novel refined track initiation algorithm for group targets based on group model. Chinese Journal of Electronics, 23(4), 851-856

Traditional refined track initiation methods for group targets have mistakes or loss of tracks when tracking irregular motions, for the reason that they rely on a stable relative position of group members. To solve the problem, a group dynamic model... Read More about A novel refined track initiation algorithm for group targets based on group model.

Classification of Fish Ectoparasite Genus Gyrodactylus SEM Images Using ASM and Complex Network Model (2014)
Presentation / Conference Contribution
Ali, R., Jiang, B., Man, M., Hussain, A., & Luo, B. (2014, November). Classification of Fish Ectoparasite Genus Gyrodactylus SEM Images Using ASM and Complex Network Model. Presented at 21st International Conference on Neural Information Processing, ICONIP 2014, Kuching, Malaysia

Active Shape Models and Complex Network method are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to classify each species to their true species type. ASM is used as a feature extract... Read More about Classification of Fish Ectoparasite Genus Gyrodactylus SEM Images Using ASM and Complex Network Model.

Dependency-based semantic parsing for concept-level text analysis (2014)
Presentation / Conference Contribution
Poria, S., Agarwal, B., Gelbukh, A., Hussain, A., & Howard, N. (2014, April). Dependency-based semantic parsing for concept-level text analysis. Presented at 15th International Conference, CICLing 2014, Kathmandu, Nepal

Concept-level text analysis is superior to word-level analysis as it preserves the semantics associated with multi-word expressions. It offers a better understanding of text and helps to significantly increase the accuracy of many text mining tasks.... Read More about Dependency-based semantic parsing for concept-level text analysis.

Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach (2014)
Journal Article
Gao, F., Zhang, Y., Wang, J., Sun, J., Yang, E., & Hussain, A. (2015). Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach. Cognitive Computation, 7(4), 434-444. https://doi.org/10.1007/s12559-014-9312-x

The human visual system (HVS) possesses a remarkable ability of real-time complex scene analysis despite the limited neuronal hardware available for such tasks. The HVS successfully overcomes the problem of information bottleneck by selecting potenti... Read More about Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach.

Towards an intelligent framework for multimodal affective data analysis (2014)
Journal Article
Poria, S., Cambria, E., Hussain, A., & Huang, G.-B. (2015). Towards an intelligent framework for multimodal affective data analysis. Neural Networks, 63, 104-116. https://doi.org/10.1016/j.neunet.2014.10.005

An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-mod... Read More about Towards an intelligent framework for multimodal affective data analysis.

Dependency tree-based rules for concept-level aspect-based sentiment analysis (2014)
Presentation / Conference Contribution
Poria, S., Ofek, N., Gelbukh, A., Hussain, A., & Rokach, L. (2014, May). Dependency tree-based rules for concept-level aspect-based sentiment analysis. Presented at SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece

Over the last few years, the way people express their opinions has changed dramatically with the progress of social networks, web communities, blogs, wikis, and other online collaborative media. Now, people buy a product and express their opinion in... Read More about Dependency tree-based rules for concept-level aspect-based sentiment analysis.

AspNet: Aspect Extraction by Bootstrapping Generalization and Propagation Using an Aspect Network (2014)
Journal Article
Xia, Y., Cambria, E., & Hussain, A. (2015). AspNet: Aspect Extraction by Bootstrapping Generalization and Propagation Using an Aspect Network. Cognitive Computation, 7(2), 241-253. https://doi.org/10.1007/s12559-014-9305-9

Aspect-level opinion mining systems suffer from concept coverage problem due to the richness and ambiguity of natural language opinions. Aspects mentioned by review authors can be expressed in various forms, resulting in a potentially large number of... Read More about AspNet: Aspect Extraction by Bootstrapping Generalization and Propagation Using an Aspect Network.

Affective neural networks and cognitive learning systems for big data analysis (2014)
Journal Article
Hussain, A., Cambria, E., Schuller, B., & Howard, N. (2014). Affective neural networks and cognitive learning systems for big data analysis. Neural Networks, 58, 1-3. https://doi.org/10.1016/j.neunet.2014.07.010

Word Polarity Disambiguation Using Bayesian Model and Opinion-Level Features (2014)
Journal Article
Xia, Y., Cambria, E., Hussain, A., & Zhao, H. (2015). Word Polarity Disambiguation Using Bayesian Model and Opinion-Level Features. Cognitive Computation, 7(3), 369-380. https://doi.org/10.1007/s12559-014-9298-4

Contextual polarity ambiguity is an important problem in sentiment analysis. Many opinion keywords carry varying polarities in different contexts, posing huge challenges for sentiment analysis research. Previous work on contextual polarity disambigua... Read More about Word Polarity Disambiguation Using Bayesian Model and Opinion-Level Features.

EmoSenticSpace: A novel framework for affective common-sense reasoning (2014)
Journal Article
Poria, S., Gelbukh, A., Cambria, E., Hussain, A., & Huang, G.-B. (2014). EmoSenticSpace: A novel framework for affective common-sense reasoning. Knowledge-Based Systems, 69, 108-123. https://doi.org/10.1016/j.knosys.2014.06.011

Emotions play a key role in natural language understanding and sensemaking. Pure machine learning usually fails to recognize and interpret emotions in text accurately. The need for knowledge bases that give access to semantics and sentics (the concep... Read More about EmoSenticSpace: A novel framework for affective common-sense reasoning.

A Cognitively Inspired Approach to Two-Way Cluster Extraction from One-Way Clustered Data (2014)
Journal Article
Abdullah, A., & Hussain, A. (2015). A Cognitively Inspired Approach to Two-Way Cluster Extraction from One-Way Clustered Data. Cognitive Computation, 7(1), 161-182. https://doi.org/10.1007/s12559-014-9281-0

Cluster extraction is a vital part of data mining; however, humans and computers perform it very differently. Humans tend to estimate, perceive or visualize clusters cognitively, while digital computers either perform an exact extraction, follow a fu... Read More about A Cognitively Inspired Approach to Two-Way Cluster Extraction from One-Way Clustered Data.

Novel Biologically Inspired Approaches to Extracting Online Information from Temporal Data (2014)
Journal Article
Malik, Z. K., Hussain, A., & Wu, J. (2014). Novel Biologically Inspired Approaches to Extracting Online Information from Temporal Data. Cognitive Computation, 6(3), 595-607. https://doi.org/10.1007/s12559-014-9257-0

In this paper, we aim to develop novel learning approaches for extracting invariant features from time series. Specifically, we implement an existing method of solving the generalized eigenproblem and use this to firstly implement the biologically in... Read More about Novel Biologically Inspired Approaches to Extracting Online Information from Temporal Data.

Special issue on International Symposium on Neural Networks (2014)
Journal Article
Zeng, Z., Hussain, A., & Wei, Q. (2014). Special issue on International Symposium on Neural Networks. Neurocomputing, 125, 1-2. https://doi.org/10.1016/j.neucom.2013.01.004

No abstract available

A basal ganglia inspired soft switching approach to the motion control of a car-like autonomous vehicle (2013)
Presentation / Conference Contribution
Yang, E., Hussain, A., & Gurney, K. (2013, June). A basal ganglia inspired soft switching approach to the motion control of a car-like autonomous vehicle. Presented at BICS 2013: 6th International Conference on Brain Inspired Cognitive Systems, Beijing, China

This paper presents a new brain-inspired, switching control approach for a car-like autonomous vehicle using a basal ganglia (BG) model as an action selection mechanism. The problem domain has challenging nonholonomic and state constraints which impl... Read More about A basal ganglia inspired soft switching approach to the motion control of a car-like autonomous vehicle.

A novel SAR target detection algorithm based on contextual knowledge (2013)
Journal Article
Gao, F., Ru, A., Sun, J., & Hussain, A. (2013). A novel SAR target detection algorithm based on contextual knowledge. Progress In Electromagnetics Research, 142, 123-140. https://doi.org/10.2528/PIER13062403

This paper proposes a Synthetic Aperture Radar (SAR) vehicle target detection algorithm based on contextual knowledge. The proposed algorithm firstly obtains the general classification of SAR image with a Markov Random Field (MRF)-based segmentation... Read More about A novel SAR target detection algorithm based on contextual knowledge.

A novel clinical expert system for chest pain risk assessment (2013)
Presentation / Conference Contribution
Farooq, K., Hussain, A., Atassi, H., Leslie, S., Eckl, C., MacRae, C., & Slack, W. (2013, June). A novel clinical expert system for chest pain risk assessment. Presented at BICS 2013: 6th International Conference on Brain Inspired Cognitive Systems, Beijing, China

Rapid access chest pain clinics (RACPC) enable clinical risk assessment, investigation and arrangement of a treatment plan for chest pain patients without a long waiting list. RACPC Clinicians often experience difficulties in the diagnosis of chest p... Read More about A novel clinical expert system for chest pain risk assessment.

A review of artificial intelligence and biologically inspired computational approaches to solving issues in narrative financial disclosure (2013)
Presentation / Conference Contribution
Minhas, S., Poria, S., Hussain, A., & Hussainey, K. (2013, June). A review of artificial intelligence and biologically inspired computational approaches to solving issues in narrative financial disclosure. Presented at BICS 2013: 6th International Conference on Brain Inspired Cognitive Systems, Beijing, China

Indisputably, financial reporting has a key role to play in the efficient workings of capitalist economies. Problems related to agency and asymmetric information (Jensen and Meckling, 1976) would abound and cripple financial markets, as it has done w... Read More about A review of artificial intelligence and biologically inspired computational approaches to solving issues in narrative financial disclosure.

Cognitive computation: A case study in cognitive control of autonomous systems and some future directions (2013)
Presentation / Conference Contribution
Hussain, A. (2013). Cognitive computation: A case study in cognitive control of autonomous systems and some future directions. In The 2013 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN.2013.6706716

Cognitive computation is an emerging discipline linking together neurobiology, cognitive psychology and artificial intelligence. Springer Neuroscience has launched a journal in this exciting multidisciplinary topic, which seeks to publish biologicall... Read More about Cognitive computation: A case study in cognitive control of autonomous systems and some future directions.

Conceptual clustering of documents for automatic ontology generation (2013)
Presentation / Conference Contribution
Krishnan, R., Hussain, A., & Sherimon, S. P. C. (2013, June). Conceptual clustering of documents for automatic ontology generation. Presented at BICS 2013: International Conference on Brain Inspired Cognitive Systems, Beijing, China

In Information retrieval, Keyword based retrieval is unsatisfactory for user needs since it can’t always retrieve relevant words according to the concept. Since different words can represent the same concept (polysemy) and one word can represent diff... Read More about Conceptual clustering of documents for automatic ontology generation.

Efficient clinical decision making by learning from missing clinical data (2013)
Presentation / Conference Contribution
Farooq, K., Yang, P., Hussain, A., Huang, K., MacRae, C., Eckl, C., & Slack, W. (2013, April). Efficient clinical decision making by learning from missing clinical data. Presented at 2013 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), Singapore, Singapore

Clinical decision making frequently involves making decisions under uncertainty because of missing key patient data (e.g, demographics, episodic and clinical diagnosis details) - this information is essential for modern clinical decision support syst... Read More about Efficient clinical decision making by learning from missing clinical data.

Improved efficiency of road sign detection and recognition by employing Kalman filter (2013)
Presentation / Conference Contribution
Zakir, U., Hussain, A., Ali, L., & Luo, B. (2013, June). Improved efficiency of road sign detection and recognition by employing Kalman filter. Presented at BICS 2013: 6th International Conference on Brain Inspired Cognitive Systems, Beijing, China

This paper describes an efficient approach towards road sign detection, and recognition. The proposed system is divided into three sections namely: Road Sign Detection where Colour Segmentation of the road traffic signs is carried out using HSV colou... Read More about Improved efficiency of road sign detection and recognition by employing Kalman filter.

Advances in Brain Inspired Cognitive Systems: Preface (2013)
Presentation / Conference Contribution
Liu, D., Alippi, C., Zhao, D., & Hussain, A. (2013). Advances in Brain Inspired Cognitive Systems: Preface. . https://doi.org/10.1007/978-3-642-38786-9

This book constitutes the refereed proceedings of the 6th International Conference on Brain Inspired Cognitive Systems, BICS 2013, held in Beijing, China in June 2013. The 45 high-quality papers presented were carefully reviewed and selected from 68... Read More about Advances in Brain Inspired Cognitive Systems: Preface.

Music genre classification: A semi-supervised approach (2013)
Presentation / Conference Contribution
Poria, S., Gelbukh, A., Hussain, A., Bandyopadhyay, S., & Howard, N. (2013, June). Music genre classification: A semi-supervised approach. Presented at MCPR 2013: 5th Mexican Conference on Pattern Recognition, Querétaro, Mexico

Music genres can be seen as categorical descriptions used to classify music basing on various characteristics such as instrumentation, pitch, rhythmic structure, and harmonic contents. Automatic music genre classification is important for music retri... Read More about Music genre classification: A semi-supervised approach.

Tables and chairs go au naturel (2013)
Journal Article
Hussain, A. (2013). Tables and chairs go au naturel. InTents, 20,

Functional and aesthetic—tables and chairs provide structure and warmth to an event. Tables and chairs are the bones of an event. They provide the foundation upon which the rest of the event can be built. While glitz and glam still have their place,... Read More about Tables and chairs go au naturel.

Towards reduced EEG based brain-computer interfacing for mobile robot navigation (2013)
Presentation / Conference Contribution
Mahmud, M., & Hussain, A. (2013, November). Towards reduced EEG based brain-computer interfacing for mobile robot navigation. Presented at 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, Mexico City, Mexico

Rapid development in highly parallel neurophysiological recording techniques along with sophisticated signal processing tools allow direct communication with neuronal processes at different levels. One important level from the point of view of Rehabi... Read More about Towards reduced EEG based brain-computer interfacing for mobile robot navigation.

A novel mortality model for acute alcoholic hepatitis including variables recorded after admission to hospital (2013)
Journal Article
Mazzocco, T., Hussain, A., Hussain, S., & Shah, A. A. (2014). A novel mortality model for acute alcoholic hepatitis including variables recorded after admission to hospital. Computers in Biology and Medicine, 44, 132-135. https://doi.org/10.1016/j.compbiomed.2013.11.005

Severe forms of alcoholic hepatitis in patients with alcoholic liver disease are associated with high mortality; it is therefore vital to identify those patients at greatest risk of mortality in 28 days as they may benefit from aggressive interventio... Read More about A novel mortality model for acute alcoholic hepatitis including variables recorded after admission to hospital.

Novel Two-Stage Audiovisual Speech Filtering in Noisy Environments (2013)
Journal Article
Abel, A., & Hussain, A. (2014). Novel Two-Stage Audiovisual Speech Filtering in Noisy Environments. Cognitive Computation, 6(2), 200-217. https://doi.org/10.1007/s12559-013-9231-2

In recent years, the established link between the various human communication production domains has become more widely utilised in the field of speech processing. In this work, we build on previous work by the authors and present a novel two-stage a... Read More about Novel Two-Stage Audiovisual Speech Filtering in Noisy Environments.

Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics (2013)
Presentation / Conference Contribution
Cambria, E., Howard, N., Hsu, J., & Hussain, A. (2013, April). Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics. Presented at 2013 IEEE Symposium on Computational Intelligence for Human-like Intelligence (CIHLI), Singapore, Singapore

The capability of interpreting the conceptual and affective information associated with natural language through different modalities is a key issue for the enhancement of human-agent interaction. The proposed methodology, termed sentic blending, ena... Read More about Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics.

A brain-computer interface test-bench based on EEG signals for research and student training (2013)
Presentation / Conference Contribution
Raif, P., Mahmud, M., Hussain, A., Klos-Witkowska, A., & Suchanek, R. (2013, April). A brain-computer interface test-bench based on EEG signals for research and student training. Presented at 2013 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)

The paper describes a test-bench model for braincomputer interface research based on EEG signals. The test-bench is going to be used for students training and education. The goal is to prepare modern Brain-Computer Interface development environment i... Read More about A brain-computer interface test-bench based on EEG signals for research and student training.

In Memory of John G Taylor: A Polymath Scholar (2013)
Journal Article
Cutsuridis, V., & Hussain, A. (2013). In Memory of John G Taylor: A Polymath Scholar. Cognitive Computation, 5(3), 279-280. https://doi.org/10.1007/s12559-013-9226-z

The scope of this special issue is to celebrate the work of the late Professor John Taylor. John began his career in 1956 as a theoretical physicist and has contributed many seminal papers and books to high-energy physics, black holes, quantum gravit... Read More about In Memory of John G Taylor: A Polymath Scholar.

Complex adaptive communication networks and environments: Part 2 (2013)
Journal Article
Niazi, M., & Hussain, A. (2013). Complex adaptive communication networks and environments: Part 2. SIMULATION, 89(7), 787-789. https://doi.org/10.1177/0037549713497714

Toward a Formal, Visual Framework of Emergent Cognitive Development of Scholars (2013)
Journal Article
Hussain, A., & Niazi, M. (2014). Toward a Formal, Visual Framework of Emergent Cognitive Development of Scholars. Cognitive Computation, 6(1), 113-124. https://doi.org/10.1007/s12559-013-9219-y

Understanding the cognitive evolution of researchers as they progress in academia is an important but complex problem; one that belongs to a class of problems, which often require the development of models to gain further understanding of the intrica... Read More about Toward a Formal, Visual Framework of Emergent Cognitive Development of Scholars.

Complex adaptive communication networks and environments: Part 1 (2013)
Journal Article
Niazi, M., & Hussain, A. (2013). Complex adaptive communication networks and environments: Part 1. SIMULATION, 89(5), 559-561. https://doi.org/10.1177/0037549713492122

Application of multi-dimensional scaling and artificial neural networks for biologically inspired opinion mining (2013)
Journal Article
Cambria, E., Mazzocco, T., & Hussain, A. (2013). Application of multi-dimensional scaling and artificial neural networks for biologically inspired opinion mining. Biologically Inspired Cognitive Architectures, 4, 41-53. https://doi.org/10.1016/j.bica.2013.02.003

The way people express their opinions has radically changed in the past few years thanks to the advent of online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers wh... Read More about Application of multi-dimensional scaling and artificial neural networks for biologically inspired opinion mining.

Enhanced SenticNet with affective labels for concept-based opinion mining (2013)
Journal Article
Poria, S., Gelbukh, A., Hussain, A., Howard, N., Das, D., & Bandyopadhyay, S. (2013). Enhanced SenticNet with affective labels for concept-based opinion mining. IEEE Intelligent Systems, 28(2), 2-9. https://doi.org/10.1109/MIS.2013.4

SenticNet 1.0 is one of the most widely used, publicly available resources for concept-based opinion mining. The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label.

A novel road traffic sign detection and recognition approach by introducing CCM and LESH (2012)
Presentation / Conference Contribution
Zakir, U., Usman, A., & Hussain, A. (2012). A novel road traffic sign detection and recognition approach by introducing CCM and LESH. In Neural Information Processing (629-636). https://doi.org/10.1007/978-3-642-34487-9_76

A real time road sign detection and recognition system can provide an additional level of driver assistance leading to an improved safety to passengers, road users and other vehicles. Such Advanced Driver Assistance Systems (ADAS) can be used to aler... Read More about A novel road traffic sign detection and recognition approach by introducing CCM and LESH.

Affective common sense knowledge acquisition for sentiment analysis (2012)
Presentation / Conference Contribution
Cambria, E., Xia, Y., & Hussain, A. (2012). Affective common sense knowledge acquisition for sentiment analysis.

Thanks to the advent of Web 2.0, the potential for opinion sharing today is unmatched in history. Making meaning out of the hugeamount of unstructured information available online, however, is extremely difficult as web-contents, despite being perfe... Read More about Affective common sense knowledge acquisition for sentiment analysis.

An intelligent multiple-controller framework for the integrated control of autonomous vehicles (2012)
Presentation / Conference Contribution
Hussain, A., Abdullah, R., Yang, E., & Gurney, K. (2012, July). An intelligent multiple-controller framework for the integrated control of autonomous vehicles. Presented at 5th International Conference, BICS 2012, Shenyang, China

This paper presents an intelligent multiple-controller framework for the integrated control of throttle, brake and steering subsystems of realistic validated nonlinear autonomous vehicles. In the developed multiple-controller framework, a fuzzy logic... Read More about An intelligent multiple-controller framework for the integrated control of autonomous vehicles.

An ontology driven and Bayesian Network based cardiovascular decision support framework (2012)
Presentation / Conference Contribution
Farooq, K., Hussain, A., Leslie, S., Eckl, C., MacRae, C., & Slack, W. (2012, July). An ontology driven and Bayesian Network based cardiovascular decision support framework. Presented at 5th International Conference, BICS 2012, Shenyang, China

Clinical risk assessment of chronic illnesses in the cardiovascular domain is quite a challenging and complex task which entails the utilization of standardized clinical practice guidelines and documentation procedures to ensure clinical governance,... Read More about An ontology driven and Bayesian Network based cardiovascular decision support framework.

Analysis of pesticide application practices using an intelligent Agriculture Decision Support System (ADSS) (2012)
Presentation / Conference Contribution
Abdullah, A., Barnawi, A., & Hussain, A. (2012, July). Analysis of pesticide application practices using an intelligent Agriculture Decision Support System (ADSS). Presented at 5th International Conference, BICS 2012, Shenyang, China

Pesticides are used for controlling pests, but at the same time they have impacts on the environment as well as the product itself. Although cotton covers 2.5% of the world’s cultivated land yet uses 16% of the world’s insecticides, more than any oth... Read More about Analysis of pesticide application practices using an intelligent Agriculture Decision Support System (ADSS).

Clustering social networks using interaction semantics and sentics (2012)
Presentation / Conference Contribution
Chandra, P., Cambria, E., & Hussain, A. (2012). Clustering social networks using interaction semantics and sentics. In Advances in Neural Networks – ISNN 2012 (379-385). https://doi.org/10.1007/978-3-642-31346-2_43

The passage from a static read-only Web to a dynamic read-write Web gave birth to a huge amount of online social networks with the ultimate goal of making communication easier between people with common interests. Unlike real world social networks, h... Read More about Clustering social networks using interaction semantics and sentics.

Decoding network activity from LFPS: A computational approach (2012)
Presentation / Conference Contribution
Mahmud, M., Travalin, D., & Hussain, A. (2012). Decoding network activity from LFPS: A computational approach. In Neural Information Processing (584-591). https://doi.org/10.1007/978-3-642-34475-6_70

Cognition is one of the main capabilities of mammal brain and understanding it thoroughly requires decoding brain’s information processing pathways which are composed of networks formed by complex connectivity between neurons. Mostly, scientists rely... Read More about Decoding network activity from LFPS: A computational approach.

Massive tenting at the Ryder Cup (2012)
Journal Article
Hussain, A. (2012). Massive tenting at the Ryder Cup. InTents, 19,

Long before the U.S. and European teams could begin their match play for the 2012 Ryder Cup, the course had to be prepared with temporary environments that could accommodate players and fans. The popular, biennial golf event—held in 2012 at Medinah C... Read More about Massive tenting at the Ryder Cup.

Merging SenticNet and WordNet-Affect emotion lists for sentiment analysis (2012)
Presentation / Conference Contribution
Poria, S., Gelbukh, A., Cambria, E., Yang, P., Hussain, A., & Durrani, T. (2012). Merging SenticNet and WordNet-Affect emotion lists for sentiment analysis. . https://doi.org/10.1109/ICoSP.2012.6491803

SenticNet is currently one of the most comprehensive freely available semantic resources for opinion mining. However, it only provides numerical polarity scores, while more detailed sentiment-related information for its concepts is often desirable. A... Read More about Merging SenticNet and WordNet-Affect emotion lists for sentiment analysis.

Neurobiologically-inspired soft switching control of autonomous vehicles (2012)
Presentation / Conference Contribution
Yang, E., Hussain, A., & Gurney, K. (2012). Neurobiologically-inspired soft switching control of autonomous vehicles. In Advances in Brain Inspired Cognitive Systems (82-91). https://doi.org/10.1007/978-3-642-31561-9_9

A novel soft switching control approach is presented in this paper for autonomous vehicles by using a new functional model for Basal Ganglia (BG). In the proposed approach, a family of fundamental controllers is treated as each of a set of basic cont... Read More about Neurobiologically-inspired soft switching control of autonomous vehicles.

Off-line handwritten Arabic word recognition using SVMs with normalized poly kernel (2012)
Presentation / Conference Contribution
Alalshekmubarak, A., Hussain, A., & Wang, Q. (2012). Off-line handwritten Arabic word recognition using SVMs with normalized poly kernel. In Neural Information Processing (85-91). https://doi.org/10.1007/978-3-642-34481-7_11

Handwriting recognition is a complicated process that many applications rely on, such as mail sorting, cheque processing, digitalisation and translation. The recognition of handwritten Arabic is still an ongoing challenge mainly due to the similarity... Read More about Off-line handwritten Arabic word recognition using SVMs with normalized poly kernel.

Retrieval of semantic concepts based on analysis of texts for automatic construction of ontology (2012)
Presentation / Conference Contribution
Krishnan, R., Hussain, A., & Sherimon, P. (2012). Retrieval of semantic concepts based on analysis of texts for automatic construction of ontology. In Neural Information Processing (524-532). https://doi.org/10.1007/978-3-642-34475-6_63

Ontology together with Semantic Web has a vital role in knowledge management on a global scale. Since manual construction of ontology leads to complex, time consuming and inconsistent results, automatic construction of ontology is more preferred. Thi... Read More about Retrieval of semantic concepts based on analysis of texts for automatic construction of ontology.

Road sign detection and recognition from video stream using HSV, contourlet transform and local energy based shape histogram (2012)
Presentation / Conference Contribution
Zakir, U., Edirishinghe, E., & Hussain, A. (2012, July). Road sign detection and recognition from video stream using HSV, contourlet transform and local energy based shape histogram. Presented at 5th International Conference, BICS 2012, Shenyang, China

This paper describes an efficient approach towards road sign detection and recognition. The proposed system is divided into three sections namely; Colour Segmentation of the road traffic signs using the HSV colour space considering varying lighting c... Read More about Road sign detection and recognition from video stream using HSV, contourlet transform and local energy based shape histogram.

Semantically inspired electronic healthcare records (2012)
Presentation / Conference Contribution
Farooq, K., Hussain, A., Leslie, S., Eckl, C., MacRae, C., & Slack, W. (2012). Semantically inspired electronic healthcare records. In Advances in Brain Inspired Cognitive Systems (42-51). https://doi.org/10.1007/978-3-642-31561-9_5

The adoption of Electronic Healthcare Records (EHRs) holds the key for the success of next generation intelligent healthcare systems to improve the quality of healthcare and patient safety by facilitating the exchange of critical patient’s episodic i... Read More about Semantically inspired electronic healthcare records.

Sentic maxine: Multimodal affective fusion and emotional paths (2012)
Presentation / Conference Contribution
Hupont, I., Cambria, E., Cerezo, E., Hussain, A., & Baldassarri, S. (2012). Sentic maxine: Multimodal affective fusion and emotional paths. In Advances in Neural Networks – ISNN 2012 (555-565). https://doi.org/10.1007/978-3-642-31362-2_61

The capability of perceiving and expressing emotions through different modalities is a key issue for the enhancement of human-agent interaction. In this paper, an architecture for the development of intelligent multimodal affective interfaces is pres... Read More about Sentic maxine: Multimodal affective fusion and emotional paths.

Sentic neural networks: A novel cognitive model for affective common sense reasoning (2012)
Presentation / Conference Contribution
Mazzocco, T., Cambria, E., Hussain, A., & Wang, Q. (2012). Sentic neural networks: A novel cognitive model for affective common sense reasoning. In Advances in Brain Inspired Cognitive Systems (12-21). https://doi.org/10.1007/978-3-642-31561-9_2

In human cognition, the capacity to reason and make decisions is strictly dependent on our common sense knowledge about the world and our inner emotional states: we call this ability affective common sense reasoning. In previous works, graph mining a... Read More about Sentic neural networks: A novel cognitive model for affective common sense reasoning.

SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis (2012)
Presentation / Conference Contribution
Cambria, E., Havasi, C., & Hussain, A. (2012, May). SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis. Presented at 25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25, Florida, US

Web 2.0 has changed the ways people communicate, collaborate, and express their opinions and sentiments. But despite social data on the Web being perfectly suitable for human consumption, they remain hardly accessible to machines. To bridge the cogni... Read More about SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis.

Single LFP sorting for high-resolution brain-chip interfacing (2012)
Presentation / Conference Contribution
Mahmud, M., Travalin, D., Hussain, A., Girardi, S., Maschietto, M., Felderer, F., & Vassanelli, S. (2012, July). Single LFP sorting for high-resolution brain-chip interfacing. Presented at BICS 2012: 5th International Conference on Brain Inspired Cognitive Systems, Shenyang, China

Understanding cognition has fascinated many neuroscientists and made them put their efforts in deciphering the brain’s information processing capabilities for cognition. Rodents perceive the environment through whisking during which tactile informati... Read More about Single LFP sorting for high-resolution brain-chip interfacing.

The hourglass of emotions (2012)
Presentation / Conference Contribution
Cambria, E., Livingstone, A., & Hussain, A. (2011, February). The hourglass of emotions. Presented at COST 2102 International Training School on Cognitive Behavioural Systems, Dresden, Germany

Human emotions and their modelling are increasingly understood to be a crucial aspect in the development of intelligent systems. Over the past years, in fact, the adoption of psychological models of emotions has become a common trend among researcher... Read More about The hourglass of emotions.

The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus (2012)
Presentation / Conference Contribution
Ali, R., Hussain, A., Bron, J. E., & Shinn, A. P. (2012, November). The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus. Presented at ICONIP 2012: 19th International Conference on Neural Information Processing, Doha, Qatar

Active Shape Models (ASM) are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to classify each species to their true species type. ASM is used as a feature extraction tool to select in... Read More about The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus.

Towards IMACA: Intelligent multimodal affective conversational agent (2012)
Presentation / Conference Contribution
Hussain, A., Cambria, E., Mazzocco, T., Grassi, M., Wang, Q., & Durrani, T. (2012, November). Towards IMACA: Intelligent multimodal affective conversational agent. Presented at International Conference on Neural Information Processing: ICONIP 2012, Doha, Qatar

A key aspect when trying to achieve natural interaction in machines is multimodality. Besides verbal communication, in fact, humans interact also through many other channels, e.g., facial expressions, gestures, eye contact, posture, and voice tone. S... Read More about Towards IMACA: Intelligent multimodal affective conversational agent.

Towards a Chinese common and common sense knowledge base for sentiment analysis (2012)
Presentation / Conference Contribution
Cambria, E., Hussain, A., Durrani, T., & Zhang, J. (2012, June). Towards a Chinese common and common sense knowledge base for sentiment analysis. Presented at 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Dalian, China

To date, the majority of sentiment analysis research has focused on English language. Recent studies, however, show that non-native English speakers heavily support the growing use of Internet. Chinese, specifically, is poised to outpace English as t... Read More about Towards a Chinese common and common sense knowledge base for sentiment analysis.

Service oriented architecture based web application model for collaborative biomedical signal analysis (2012)
Journal Article
Mahmud, M., Rahman, M. M., Travalin, D., Raif, P., & Hussain, A. (2012). Service oriented architecture based web application model for collaborative biomedical signal analysis. Biomedical Engineering / Biomedizinische Technik, 57(S1), 780-783. https://doi.org/10.1515/bmt-2012-4412

The rapid growth in availability of new biomedical systems and devices capable of acquiring biosignals for disease diagnosis and health monitoring require rigorous processing. Biomedical research by nature depends on integrated problem solving softwa... Read More about Service oriented architecture based web application model for collaborative biomedical signal analysis.

Common Sense Knowledge for Handwritten Chinese Text Recognition (2012)
Journal Article
Wang, Q., Cambria, E., Liu, C., & Hussain, A. (2013). Common Sense Knowledge for Handwritten Chinese Text Recognition. Cognitive Computation, 5(2), 234-242. https://doi.org/10.1007/s12559-012-9183-y

Compared to human intelligence, computers are far short of common sense knowledge which people normally acquire during the formative years of their lives. This paper investigates the effects of employing common sense knowledge as a new linguistic con... Read More about Common Sense Knowledge for Handwritten Chinese Text Recognition.

Cognitive and Emotional Information Processing for Human-Machine Interaction (2012)
Journal Article
Squartini, S., Schuller, B., & Hussain, A. (2012). Cognitive and Emotional Information Processing for Human-Machine Interaction. Cognitive Computation, 4(4), 383-385. https://doi.org/10.1007/s12559-012-9180-1

Biometric Applications Related to Human Beings: There Is Life beyond Security (2012)
Journal Article
Faundez-Zanuy, M., Hussain, A., Mekyska, J., Sesa-Nogueras, E., Monte-Moreno, E., Esposito, A., Chetouani, M., Garre-Olmo, J., Abel, A., Smekal, Z., & Lopez-de-Ipiña, K. (2013). Biometric Applications Related to Human Beings: There Is Life beyond Security. Cognitive Computation, 5(1), 136-151. https://doi.org/10.1007/s12559-012-9169-9

The use of biometrics has been successfully applied to security applications for some time. However, the extension of other potential applications with the use of biometric information is a very recent development. This paper summarizes the field of... Read More about Biometric Applications Related to Human Beings: There Is Life beyond Security.

Water quality reliability based design of water distribution networks (2012)
Presentation / Conference Contribution
Gupta, R., Hussain, A., & Bhave, P. R. (2012, May). Water quality reliability based design of water distribution networks. Presented at World Environmental And Water Resources Congress 2012, Albuquerque, New Mexico, United States

The performance of a water distribution network is affected by several factors, pipe failure being a major one of them. Several methodologies have been suggested for optimal design of water distribution networks (WDNs) to meet reliability criteria re... Read More about Water quality reliability based design of water distribution networks.

Sentic Album: Content-, Concept-, and Context-Based Online Personal Photo Management System (2012)
Journal Article
Cambria, E., & Hussain, A. (2012). Sentic Album: Content-, Concept-, and Context-Based Online Personal Photo Management System. Cognitive Computation, 4(4), 477-496. https://doi.org/10.1007/s12559-012-9145-4

The world of online personal photo management has come a long way in the past few years, but today, there are still huge gaps in annotating, organizing, and retrieving online pictures in such a way that they can be easily queried and visualized. Exis... Read More about Sentic Album: Content-, Concept-, and Context-Based Online Personal Photo Management System.

Sentic PROMs: Application of sentic computing to the development of a novel unified framework for measuring health-care quality (2012)
Journal Article
Cambria, E., Benson, T., Eckl, C., & Hussain, A. (2012). Sentic PROMs: Application of sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Systems with Applications, 39(12), 10533-10543. https://doi.org/10.1016/j.eswa.2012.02.120

Barriers to use health related quality of life measuring systems include the time needed to complete the forms and the need for staff to be trained to understand the results. An ideal system of health assessment needs to be clinically useful, timely,... Read More about Sentic PROMs: Application of sentic computing to the development of a novel unified framework for measuring health-care quality.

Isanette: A common and common sense knowledge base for opinion mining (2011)
Presentation / Conference Contribution
Cambria, E., Song, Y., Wang, H., & Hussain, A. (2011). Isanette: A common and common sense knowledge base for opinion mining. In 2011 IEEE 11th International Conference on Data Mining Workshops (315-322). https://doi.org/10.1109/ICDMW.2011.106

The ability to understand natural language text is far from being emulated in machines. One of the main hurdles to overcome is that computers lack both the common and the common sense knowledge humans normally acquire during the formative years of th... Read More about Isanette: A common and common sense knowledge base for opinion mining.

Multi-stage classification of Gyrodactylus species using machine learning and feature selection techniques (2011)
Presentation / Conference Contribution
Ali, R., Hussain, A., Bron, J. E., & Shinn, A. P. (2011, November). Multi-stage classification of Gyrodactylus species using machine learning and feature selection techniques. Presented at 2011 11th International Conference on Intelligent Systems Design and Applications, Cordoba, Spain

This study explores the use of multi-stage machine learning based classifiers and feature selection techniques in the classification and identification of fish parasites. Accurate identification of pathogens is a key to their control and as a proof o... Read More about Multi-stage classification of Gyrodactylus species using machine learning and feature selection techniques.

Sentic avatar: Multimodal affective conversational agent with common sense (2011)
Presentation / Conference Contribution
Cambria, E., Hupont, I., Hussain, A., Cerezo, E., & Baldassarri, S. (2010, March). Sentic avatar: Multimodal affective conversational agent with common sense. Presented at Third COST 2102 International Training School, Caserta, Italy

The capability of perceiving and expressing emotions through different modalities is a key issue for the enhancement of human-computer interaction. In this paper we present a novel architecture for the development of intelligent multimodal affective... Read More about Sentic avatar: Multimodal affective conversational agent with common sense.

Sentic medoids: Organizing affective common sense knowledge in a multi-dimensional vector space (2011)
Presentation / Conference Contribution
Cambria, E., Mazzocco, T., Hussain, A., & Eckl, C. (2011, May). Sentic medoids: Organizing affective common sense knowledge in a multi-dimensional vector space. Presented at ISNN 2011: 8th International Symposium on Neural Networks, Guilin, China

Existing approaches to opinion mining and sentiment analysis mainly rely on parts of text in which opinions and sentiments are explicitly expressed such as polarity terms and affect words. However, opinions and sentiments are often conveyed implicitl... Read More about Sentic medoids: Organizing affective common sense knowledge in a multi-dimensional vector space.

Switching between different ways to think: Multiple approaches to affective common sense reasoning (2011)
Presentation / Conference Contribution
Cambria, E., Mazzocco, T., Hussain, A., & Durrani, T. (2010, September). Switching between different ways to think: Multiple approaches to affective common sense reasoning. Presented at COST 2102 International Conference, Budapest, Hungary

Emotions are different Ways to Think that our mind triggers to deal with different situations we face in our lives. Our ability to reason and make decisions, in fact, is strictly dependent on both our common sense knowledge about the world and our in... Read More about Switching between different ways to think: Multiple approaches to affective common sense reasoning.

Guest editorial data-based control, modeling, and optimization (2011)
Journal Article
Chai, T., Hou, Z., Lewis, F. L., Hussain, A., & Zhao, D. (2011). Guest editorial data-based control, modeling, and optimization. IEEE Transactions on Neural Networks, 22(12), 2150-2153. https://doi.org/10.1109/TNN.2011.2177733

The 21 papers in this special section focus on data-based control, modeling, and optimization.

SimConnector: An approach to testing disaster-alerting systems using agent based simulation models (2011)
Presentation / Conference Contribution
Niazi, M., Siddique, Q., Hussain, A., & Fortino, G. (2011, September). SimConnector: An approach to testing disaster-alerting systems using agent based simulation models. Presented at 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), Szczecin, Poland

The design, development and testing of intelligent disaster detection and alerting systems pose a set of non-trivial problems. Not only are such systems difficult to design as they need to accurately predict real-world outcomes using a distributed se... Read More about SimConnector: An approach to testing disaster-alerting systems using agent based simulation models.

Ontology-driven cardiovascular decision support system (2011)
Presentation / Conference Contribution
Farooq, K., Hussain, A., Leslie, S., Eckl, C., & Slack, W. (2011, May). Ontology-driven cardiovascular decision support system. Presented at 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, Dublin, Ireland

We discuss work-in-progress and propose an ontology driven framework for the development of a clinical expert system for chest pain risk assessment. The framework has the following key components: adaptive questionnaire, patient medical history, risk... Read More about Ontology-driven cardiovascular decision support system.

A side-effects mapping model in patients with lung, colorectal and breast cancer receiving chemotherapy (2011)
Presentation / Conference Contribution
Mazzocco, T., & Hussain, A. (2011, June). A side-effects mapping model in patients with lung, colorectal and breast cancer receiving chemotherapy. Presented at 2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, Columbia, MO, USA

Cancer treatments are now more effective than ever and, as a consequence, cancer is becoming a chronic disease. Chemotherapy is a frequently used treatment in people with cancer and it can cause a number of side-effects which if not properly managed... Read More about A side-effects mapping model in patients with lung, colorectal and breast cancer receiving chemotherapy.

Novel logistic regression models to aid the diagnosis of dementia (2011)
Journal Article
Mazzocco, T., & Hussain, A. (2012). Novel logistic regression models to aid the diagnosis of dementia. Expert Systems with Applications, 39(3), 3356-3361. https://doi.org/10.1016/j.eswa.2011.09.023

Clinicians often experience difficulties in the diagnosis of dementia due to the intrinsic complexity of the process and lack of comprehensive diagnostic tools. Different models have been proposed to provide medical decision support in dementia diagn... Read More about Novel logistic regression models to aid the diagnosis of dementia.

Cognitive Computation Special Issue on Cognitive Behavioural Systems (2011)
Journal Article
Esposito, A., Vinciarelli, A., Haykin, S., Hussain, A., & Faundez-Zanuy, M. (2011). Cognitive Computation Special Issue on Cognitive Behavioural Systems. Cognitive Computation, 3(3), 417-418. https://doi.org/10.1007/s12559-011-9107-2

Cognitive processes, such as inference, categorization, and memory, are not independent from their physical instantiations. Individuals’ choices, perception, and actions emerge and are dynamically affected and enhanced by the interaction between sens... Read More about Cognitive Computation Special Issue on Cognitive Behavioural Systems.

Agent-based computing from multi-agent systems to agent-based models: A visual survey (2011)
Journal Article
Niazi, M., & Hussain, A. (2011). Agent-based computing from multi-agent systems to agent-based models: A visual survey. Scientometrics, 89, 479-499. https://doi.org/10.1007/s11192-011-0468-9

Agent-based computing is a diverse research domain concerned with the building of intelligent software based on the concept of “agents”. In this paper, we use Scientometric analysis to analyze all sub-domains of agent-based computing. Our data consis... Read More about Agent-based computing from multi-agent systems to agent-based models: A visual survey.

A discrete event system specification (DEVS)-based model of consanguinity (2011)
Journal Article
Akhtar, N., Niazi, M., Mustafa, F., & Hussain, A. (2011). A discrete event system specification (DEVS)-based model of consanguinity. Journal of Theoretical Biology, 285(1), 103-112. https://doi.org/10.1016/j.jtbi.2011.05.038

Consanguinity or inter-cousin marriage is a phenomenon quite prevalent in certain regions around the globe. Consanguineous parents have a higher risk of having offspring with congenital disorders. It is difficult to model large scale consanguineous p... Read More about A discrete event system specification (DEVS)-based model of consanguinity.

Sentic Web: A New Paradigm for Managing Social Media Affective Information (2011)
Journal Article
Grassi, M., Cambria, E., Hussain, A., & Piazza, F. (2011). Sentic Web: A New Paradigm for Managing Social Media Affective Information. Cognitive Computation, 3(3), 480-489. https://doi.org/10.1007/s12559-011-9101-8

The recent success of media-sharing services caused an exponential growth of community-contributed multimedia data on the Web and hence a consistent shift of the flow of information from traditional communication channels to social media ones. Retrie... Read More about Sentic Web: A New Paradigm for Managing Social Media Affective Information.

Sentic Computing for social media marketing (2011)
Journal Article
Cambria, E., Grassi, M., Hussain, A., & Havasi, C. (2012). Sentic Computing for social media marketing. Multimedia Tools and Applications, 59(2), 557-577. https://doi.org/10.1007/s11042-011-0815-0

In a world in which millions of people express their opinions about commercial products in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information can be a key factor for marketer... Read More about Sentic Computing for social media marketing.

Improved multiple description wavelet based image coding using subband uniform quantization (2011)
Journal Article
Khelil, K., Hussain, A., Bekka, R. E., & Berrezzek, F. (2011). Improved multiple description wavelet based image coding using subband uniform quantization. International journal of electronics and communications = Archiv fur Elektronik und Ubertragungstechnik : AEU, 65(11), 967-974. https://doi.org/10.1016/j.aeue.2011.03.011

The objective of multiple description coding (MDC) is to encode a source into multiple descriptions supporting different quality levels of reconstruction. In this paper, we use the multiple description transform coding (MDTC) algorithm based on the w... Read More about Improved multiple description wavelet based image coding using subband uniform quantization.

Sensing emergence in complex systems (2011)
Journal Article
Niazi, M. A., & Hussain, A. (2011). Sensing emergence in complex systems. IEEE Sensors Journal, 11(10), 2479-2480. https://doi.org/10.1109/JSEN.2011.2142303

We propose the Sensing of Emergent behavior in a Complex Adaptive System (SECAS), as an extension of our previous work Formal Agent-Based Simulation Framework (FABS). Using aggregated data from an array of proximity sensors, SECAS allows for the dete... Read More about Sensing emergence in complex systems.

Social network analysis of trends in the consumer electronics domain (2011)
Presentation / Conference Contribution
Niazi, M. A., & Hussain, A. (2011). Social network analysis of trends in the consumer electronics domain. In 2011 IEEE International Conference on Consumer Electronics (ICCE) (219-220). https://doi.org/10.1109/ICCE.2011.5722549

We present a study of the trends in the consumer electronics domain using Complex Social Network Analysis (SNA) of citation data retrieved from the Thomson Reuters Web of Knowledge. Our findings include the identification of the most influential pape... Read More about Social network analysis of trends in the consumer electronics domain.

Analysis of elevated liver enzymes in an acute medical setting: Jaundice may indicate increased survival in elderly patients with bacterial sepsis (2010)
Journal Article
Shah, A. A., Patton, M., Chishty, W. H., & Hussain, A. (2010). Analysis of elevated liver enzymes in an acute medical setting: Jaundice may indicate increased survival in elderly patients with bacterial sepsis. Saudi Journal of Gastroenterology, 16(4), 260-263. https://doi.org/10.4103/1319-3767.70609

BACKGROUND /AIM:
It has been shown previously that in primary care settings in UK abnormal liver enzymes are not adequately investigated and followed up; hence potentially treatable chronic liver diseases remain undiagnosed. No such published data... Read More about Analysis of elevated liver enzymes in an acute medical setting: Jaundice may indicate increased survival in elderly patients with bacterial sepsis.

Development of Multimodal Interfaces: Active Listening and Synchrony - Preface (2010)
Presentation / Conference Contribution
Esposito, A., Campbell, N., Vogel, C., Hussain, A., & Nijholt, A. (2009, March). Development of Multimodal Interfaces: Active Listening and Synchrony - Preface. Presented at Second COST 2102 International Training School, Dublin, Ireland

This volume brings together, through a peer-revision process, the advanced research results obtained by the European COST Action 2102: Cross-Modal Analysis of Verbal and Nonverbal Communication, primarily discussed for the first time at the Se... Read More about Development of Multimodal Interfaces: Active Listening and Synchrony - Preface.

Do not feel the trolls (2010)
Presentation / Conference Contribution
Cambria, E., Chandra, P., Sharma, A., & Hussain, A. (2010). Do not feel the trolls. In Proceedings of the 3rd International Workshop on Social Data on the Web (SDoW2010)

The passage from a read-only to a read-write Web gave people the possibility to freely interact, share and collaborate through social networks, online communities, blogs, wikis and other online collaborative media. The democracy of the Web is what ma... Read More about Do not feel the trolls.

Emotional vocal expressions recognition using the COST 2102 Italian database of emotional speech (2010)
Presentation / Conference Contribution
Atassi, H., Riviello, M. T., Smékal, Z., Hussain, A., & Esposito, A. (2009, March). Emotional vocal expressions recognition using the COST 2102 Italian database of emotional speech. Presented at Second COST 2102 International Training School, Dublin, Ireland

The present paper proposes a new speaker-independent approach to the classification of emotional vocal expressions by using the COST 2102 Italian database of emotional speech. The audio records extracted from video clips of Italian movies possess a c... Read More about Emotional vocal expressions recognition using the COST 2102 Italian database of emotional speech.

Multiple description transform image coder using correlating transforms (2010)
Journal Article
Khelil, K., Hussain, A., Berrezzek, F., & Djebbari, A. (2010). Multiple description transform image coder using correlating transforms. International Review on Computers and Software, 5(2), 150-155

The objective of multiple description coding (MDC) is to represent a source into multiple descriptions such that various reconstruction qualities are obtained from different subsets of the descriptions. In this paper, we report an application of a pr... Read More about Multiple description transform image coder using correlating transforms.

Sentic computing: Exploitation of common sense for the development of emotion-sensitive systems (2010)
Presentation / Conference Contribution
Cambria, E., Hussain, A., Havasi, C., & Eckl, C. (2009, March). Sentic computing: Exploitation of common sense for the development of emotion-sensitive systems. Presented at Second COST 2102 International Training School, Dublin, Ireland

Emotions are a fundamental component in human experience, cognition, perception, learning and communication. In this paper we explore how the use of Common Sense Computing can significantly enhance computers’ emotional intelligence i.e. their capabil... Read More about Sentic computing: Exploitation of common sense for the development of emotion-sensitive systems.

SenticNet: A publicly available semantic resource for opinion mining (2010)
Presentation / Conference Contribution
Cambria, E., Speer, R., Havasi, C., & Hussain, A. (2010). SenticNet: A publicly available semantic resource for opinion mining. In Commonsense knowledge: Papers from the AAAI Fall Symposium (14-18)

Today millions of web-users express their opinions about many topics through blogs, wikis, fora, chats and social networks. For sectors such as e-commerce and e-tourism, it is very useful to automatically analyze the huge amount of social information... Read More about SenticNet: A publicly available semantic resource for opinion mining.

SenticSpace: Visualizing opinions and sentiments in a multi-dimensional vector space (2010)
Presentation / Conference Contribution
Cambria, E., Hussain, A., Havasi, C., & Eckl, C. (2010). SenticSpace: Visualizing opinions and sentiments in a multi-dimensional vector space. . https://doi.org/10.1007/978-3-642-15384-6_41

In a world in which millions of people express their feelings and opinions about any issue in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information is a challenging task. In thi... Read More about SenticSpace: Visualizing opinions and sentiments in a multi-dimensional vector space.

Sentic computing for patient centered applications (2010)
Presentation / Conference Contribution
Cambria, E., Hussain, A., Durrani, T., Havasi, C., Eckl, C., & Munro, J. (2010, October). Sentic computing for patient centered applications. Presented at IEEE 10th International Conference on Signal Processing, Beijing, China

Next-generation patients are far from being peripheral to health-care. They are central to understanding the effectiveness and efficiency of services and how they can be improved. Today a lot of patients are used to reviewing local health services on... Read More about Sentic computing for patient centered applications.

Modeling and design of MPPT controller for a PV module using PSCAD/EMTDC (2010)
Presentation / Conference Contribution
Gupta, R., Gupta, G., Kastwar, D., Hussain, A., & Ranjan, H. (2010, October). Modeling and design of MPPT controller for a PV module using PSCAD/EMTDC. Presented at 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), Gothenberg, Sweden

This paper presents a modeling of photovoltaic (PV) module in PSCAD/EMTDC and design of maximum power point tracking (MPPT) using boost converter. The model can be used for simulation studies of grid interface applications using voltage source conver... Read More about Modeling and design of MPPT controller for a PV module using PSCAD/EMTDC.

A novel agent-based simulation framework for sensing in complex adaptive environments (2010)
Journal Article
Niazi, M., & Hussain, A. (2011). A novel agent-based simulation framework for sensing in complex adaptive environments. IEEE Sensors Journal, 11(2), 404-412. https://doi.org/10.1109/JSEN.2010.2068044

In this paper, we present a novel formal agent-based simulation framework (FABS). FABS uses formal specification as a means of clear description of wireless sensor networks (WSNs) sensing a complex adaptive environment. This specification model is th... Read More about A novel agent-based simulation framework for sensing in complex adaptive environments.

Verification & validation of an agent-based forest fire simulation model (2010)
Presentation / Conference Contribution
Niazi, M. A., Siddique, Q., Hussain, A., & Kolberg, M. (2010, April). Verification & validation of an agent-based forest fire simulation model. Presented at SpringSim '10 - Spring Simulation Multiconference, Orlando, Florida

In this paper, we present the verification and validation of an agent-based model of forest fires. We use a combination of a Virtual Overlay Multi-Agent System (VOMAS) validation scheme with Fire Weather Index (FWI) to validate the forest fire Simula... Read More about Verification & validation of an agent-based forest fire simulation model.

Editorial: Advances in complex control systems theory and applications (2010)
Journal Article
Hussain, A., Wang, H., & Nobakhti, A. (2010). Editorial: Advances in complex control systems theory and applications. IET Control Theory and Applications, 4(2), 173-175. https://doi.org/10.1049/iet-cta.2010.9006

Complex and large scale systems and networks have evolved in many fields: from communication data networks, banking, financial systems, bio-engineering and robotics to, amongst others, industrial control systems. In control engineering, the main driv... Read More about Editorial: Advances in complex control systems theory and applications.

A novel implicit adaptive pole-placement PID controller (2009)
Presentation / Conference Contribution
Zayed, A., El-Fandi, M., Hussain, A., & El-Fllah, A. (2009). A novel implicit adaptive pole-placement PID controller.

In this paper, a new computationally efficient multivariable self-tuning controller with a proportional plus integral plus derivative (PID) is derived. The algorithm features a combination of the self-tuning property, in which the controller paramete... Read More about A novel implicit adaptive pole-placement PID controller.

An investigation into audiovisual speech correlation in reverberant noisy environments (2009)
Presentation / Conference Contribution
Cifani, S., Abel, A., Hussain, A., Squartini, S., & Piazza, F. (2008, October). An investigation into audiovisual speech correlation in reverberant noisy environments. Presented at COST Action 2102 International Conference, Prague, Czech Republic

As evidence of a link between the various human communication production domains has become more prominent in the last decade, the field of multimodal speech processing has undergone significant expansion. Many different specialised processing method... Read More about An investigation into audiovisual speech correlation in reverberant noisy environments.

Common sense computing: From the society of mind to digital intuition and beyond (2009)
Presentation / Conference Contribution
Cambria, E., Hussain, A., Havasi, C., & Eckl, C. (2009, September). Common sense computing: From the society of mind to digital intuition and beyond. Presented at BioID: European Workshop on Biometrics and Identity Management, Madrid, Spain

What is Common Sense Computing? And why is it so important for the technological evolution of humankind? This paper presents an overview of past, present and future efforts of the AI community to give computers the capacity for Common Sense reasoning... Read More about Common sense computing: From the society of mind to digital intuition and beyond.

Controlled and automatic processing in animals and machines with application to autonomous vehicle control (2009)
Presentation / Conference Contribution
Gurney, K., Hussain, A., Chambers, J., & Abdullah, R. (2009, September). Controlled and automatic processing in animals and machines with application to autonomous vehicle control. Presented at ICANN: International Conference on Artificial Neural Networks, Limassol, Cyprus

There are two modes of control recognised in the cognitive psychological literature. Controlled processing is slow, requires serial attention to sub-tasks, and requires effortful memory retrieval and decision making. In contrast automatic control is... Read More about Controlled and automatic processing in animals and machines with application to autonomous vehicle control.

Maximising audiovisual correlation with automatic lip tracking and vowel based segmentation (2009)
Presentation / Conference Contribution
Abel, A., Hussain, A., Nguyen, Q., Ringeval, F., Chetouani, M., & Milgram, M. (2009, September). Maximising audiovisual correlation with automatic lip tracking and vowel based segmentation. Presented at BioID: European Workshop on Biometrics and Identity Management, Madrid, Spain

In recent years, the established link between the various human communication production domains has become more widely utilised in the field of speech processing. In this work, a state of the art Semi Adaptive Appearance Model (SAAM) approach develo... Read More about Maximising audiovisual correlation with automatic lip tracking and vowel based segmentation.

Multi-modal speech processing methods: an overview and future research directions using a MATLAB based audio-visual toolbox (2009)
Presentation / Conference Contribution
Abel, A., & Hussain, A. (2008, April). Multi-modal speech processing methods: an overview and future research directions using a MATLAB based audio-visual toolbox. Presented at COST Action 2102 and euCognition International School, Vietri sul Mare, Italy

This paper presents an overview of the main multi-modal speech enhancement methods reported to date. In particular, a new MATLAB based Toolbox developed by Barbosa et al (2007) for processing audio-visual data is reviewed and its performance potentia... Read More about Multi-modal speech processing methods: an overview and future research directions using a MATLAB based audio-visual toolbox.