Prof Amir Hussain

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

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, 1

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.

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

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., …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/

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.

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.

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., …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/s41467-022-3223

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/s12559-02

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.

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.en

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/s10639-022-11

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/css

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.or

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

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

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

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:

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 Intelligen

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.

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

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., …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:

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

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., …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. http

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 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. h

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., …Aguglia, U. (2022). A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls

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

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.

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. htt

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.

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., …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 e0

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

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., …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/s2116528

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.2021.07.0

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

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 Communic

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.

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.

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/s00521-021-0607

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., …Hussain, A. (2021). Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence–Enabled Social Media Analysis. Journal of Medic

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.

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., …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/diagnosti

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.

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

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., …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.

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/s00521-020-0562

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 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., …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. ht

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., …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.

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., …Hussain, A. (2021). iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings. IEEE Access, 9, 13814-13828. htt

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. ht

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.

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., …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

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.

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.o

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 Autom

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/10.339

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. h

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,

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/

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.

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 Ac

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.

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.

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.

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.

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.

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.

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.

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/10.110

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.

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/electronics80505

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.

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,

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).

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

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.

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.or

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 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.

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.

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

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

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.

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.neuc

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),

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.

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,

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.

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

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.2018.2

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/10.10

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].

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., …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.2017.278311

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.

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.

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.

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.

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.2016.0

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.

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.

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.

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., …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/AC

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

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

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/10.10

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/s

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.

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

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., & 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.

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.

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.

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.

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.

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/s12559-014-931

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. (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.

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. (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 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.

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.

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.compbio

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.

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

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.

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.

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

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., …Lopez-de-Ipiña, K. (2013). Biometric Applications Related to Human Beings: There Is Life beyond Security. Cognitive Computation, 5(1), 136-151. https://doi

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.

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.or

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.

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.

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 Ubertragungstec

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.

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), 26

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.

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.

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.

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.

Special issue on non-linear and non-conventional speech processing (2009)
Journal Article
Chetouani, M., Faundez-Zanuy, M., Hussain, A., Gas, B., Zarader, J., & Paliwal, K. (2009). Special issue on non-linear and non-conventional speech processing. Speech Communication, 51(9), 713-830

Special issue on non-linear and non-conventional speech processing.

Agent-based tools for modeling and simulation of self-organization in peer-to-peer, ad hoc, and other complex networks (2009)
Journal Article
Niazi, M., & Hussain, A. (2009). Agent-based tools for modeling and simulation of self-organization in peer-to-peer, ad hoc, and other complex networks. IEEE Communications Magazine, 47(3), 166-173. https://doi.org/10.1109/MCOM.2009.4804403

Agent-based modeling and simulation tools provide a mature platform for development of complex simulations. They however, have not been applied much in the domain of mainstream modeling and simulation of computer networks. In this article, we evaluat... Read More about Agent-based tools for modeling and simulation of self-organization in peer-to-peer, ad hoc, and other complex networks.

Cognitive Computation: An introduction (2009)
Journal Article
Hussain, A. (2009). Cognitive Computation: An introduction. Cognitive Computation, 1(1), 1-3. https://doi.org/10.1007/s12559-009-9013-z

Brain inspired cognitive systems (BICS) (2008)
Journal Article
Hussain, A., Aleksander, I., Smith, L., & Chrisley, R. (2009). Brain inspired cognitive systems (BICS). Neurocomputing, 72(4-6), 683-684. https://doi.org/10.1016/j.neucom.2008.10.007

Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning (2008)
Journal Article
Abdullah, R., Hussain, A., Warwick, K., & Zayed, A. (2008). Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning. Neurocomputing, 71(13-15), 2727-2741. https://doi.org/10.10

This paper presents a novel intelligent multiple-controller framework incorporating a fuzzy-logic-based switching and tuning supervisor along with a generalised learning model (GLM) for an autonomous cruise control application. The proposed methodolo... Read More about Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning.

Engineering of intelligent systems (ICEIS 2006) (2008)
Journal Article
Hussain, A., Fiori, S., Qureshi, I., Durrani, T., Ahmed, M., & Fukushima, K. (2008). Engineering of intelligent systems (ICEIS 2006). Neurocomputing, 71(13-15), 2616-2618. https://doi.org/10.1016/j.neucom.2008.03.014

Exponential stabilization of the inertia wheel pendulum using dynamic surface control (2007)
Journal Article
Qaiser, N., Iqbal, N., Hussain, A., & Qaiser, N. (2007). Exponential stabilization of the inertia wheel pendulum using dynamic surface control. Journal of Circuits, Systems, and Computers, 16(1), 81-92. https://doi.org/10.1142/S0218126607003514

This paper considers the stabilization problem of Inertia Wheel Pendulum, a widely studied benchmark nonlinear system. It is a classical example of a flat underactuated mechanical system, for which the design of control law becomes a challenging task... Read More about Exponential stabilization of the inertia wheel pendulum using dynamic surface control.

Exponential stabilization of a class of underactuated mechanical systems using dynamic surface control (2007)
Journal Article
Qaiser, N., Iqbal, N., Hussain, A., & Qaiser, N. (2007). Exponential stabilization of a class of underactuated mechanical systems using dynamic surface control. International Journal of Control, Automation and Systems, 5(5), 547-558

This paper proposes a simpler solution to the stabilization problem of a special class of nonlinear underactuated mechanical systems which includes widely studied benchmark systems like Inertia Wheel Pendulum, TORA and Acrobot. Complex internal dynam... Read More about Exponential stabilization of a class of underactuated mechanical systems using dynamic surface control.

Dynamic surface control for stabilization of the oscillating eccentric rotor (2007)
Journal Article
Qaiser, N., Hussain, A., Iqbal, N., & Qaiser, N. (2007). Dynamic surface control for stabilization of the oscillating eccentric rotor. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 221(3), 311-

The present paper presents a new solution for the stabilization and disturbance attenuation problems of the oscillating eccentric rotor (OER), an extensively studied non-linear under-actuated mechanical system. Designing control law for such systems... Read More about Dynamic surface control for stabilization of the oscillating eccentric rotor.

A nonlinear PID-based multiple controller incorporating a multilayered neural network learning submodel (2006)
Journal Article
Zayed, A., Hussain, A., & Grimble, M. (2006). A nonlinear PID-based multiple controller incorporating a multilayered neural network learning submodel. Control and Intelligent Systems, 34, 177-184. https://doi.org/10.2316/Journal.201.2006.3.201-1499

A new nonlinear minimum-variance adaptive proportional integral derivative (PID) based multiple controller, incorporating a multi- layered neural network learning submodel, is presented. The unknown non-linear plant is represented by an equivalent st... Read More about A nonlinear PID-based multiple controller incorporating a multilayered neural network learning submodel.

Editorial: Nonlinear adaptive PID control - Part II (2006)
Journal Article
Hussain, A., Grimble, M., & Zayed, A. (2006). Editorial: Nonlinear adaptive PID control - Part II. Control and Intelligent Systems, 34(3), 175

The article provides an overview of papers about nonlinear adaptive proportional integral derivative (PID) control, published in the August 2006 issue of "Control and Intelligent Systems." Zayed and colleagues wrote an editorial describing a nonlinea... Read More about Editorial: Nonlinear adaptive PID control - Part II.

Data mining a new pilot agriculture extension data warehouse (2006)
Journal Article
Abdullah, A., & Hussain, A. (2006). Data mining a new pilot agriculture extension data warehouse. Journal of Research and Practice in Information Technology, 38(3), 229-248

Pakistan is the world’s fifth largest cotton producer. To monitor cotton growth, different government departments and agencies in Pakistan have been recording pest scouting, agriculture and metrological data for decades. Coarse estimates of just the... Read More about Data mining a new pilot agriculture extension data warehouse.

A new biclustering technique based on crossing minimization (2006)
Journal Article
Abdullah, A., & Hussain, A. (2006). A new biclustering technique based on crossing minimization. Neurocomputing, 69(16-18), 1882-1896. https://doi.org/10.1016/j.neucom.2006.02.018

Clustering only the records in a database (or data matrix) gives a global view of the data. For a detailed analysis or a local view, biclustering or co-clustering is required, involving the clustering of the records and the attributes simultaneously.... Read More about A new biclustering technique based on crossing minimization.

A novel multiple-controller incorporating a radial basis function neural network based generalized learning model (2006)
Journal Article
Zayed, A. S., Hussain, A., & Abdullah, R. A. (2006). A novel multiple-controller incorporating a radial basis function neural network based generalized learning model. Neurocomputing, 69(16-18), 1868-1881. https://doi.org/10.1016/j.neucom.2006.02.017

A new adaptive multiple-controller is proposed incorporating a radial basis function (RBF) neural network based generalized learning model (GLM). The GLM assumes that the unknown complex plant is represented by an equivalent stochastic model consisti... Read More about A novel multiple-controller incorporating a radial basis function neural network based generalized learning model.

Brain inspired cognitive systems (BICS 2004) (2006)
Journal Article
Hussain, A., Smith, L., & Alexander, I. (2006). Brain inspired cognitive systems (BICS 2004). Neurocomputing, 69(16-18), 1825-1826. https://doi.org/10.1016/j.neucom.2006.05.005

Blind equalisation of communication channels for equal energy sources: Energy matching approach (2006)
Journal Article
Naveed, A., Qureshi, I., Hussain, A., & Fiori, S. (2006). Blind equalisation of communication channels for equal energy sources: Energy matching approach. Electronics Letters, 42(4), 247-248. https://doi.org/10.1049/el%3A20062983

A new energy-matching approach for blind equalisation of possibly non-minimum phase channels is presented. The scheme exploits second order statistics and uses oversampling, but does not use the matching of statistics of the input with that of the ou... Read More about Blind equalisation of communication channels for equal energy sources: Energy matching approach.

Blind image deconvolution using space-variant neural network approach (2005)
Journal Article
Cheema, T., Qureshi, I., & Hussain, A. (2005). Blind image deconvolution using space-variant neural network approach. Electronics Letters, 41(6), 308-309

A novel space-variant neural network based on an autoregressive moving average process is proposed for blind image deconvolution. An extended cost function motivated by human visual perception is developed simultaneously to identify the blur and to r... Read More about Blind image deconvolution using space-variant neural network approach.

Hybrid HOS-SOS approach for blind equalisation of communication channels (2005)
Journal Article
Hussain, A., Naveed, A., & Qureshi, I. (2005). Hybrid HOS-SOS approach for blind equalisation of communication channels. Electronics Letters, 41(6), 376-377. https://doi.org/10.1049/el%3A20057272

A new hybrid higher-order statistics (HOS) and second-order statistics (SOS) based approach to improve the performance of the standard Bussgang algorithm for blind equalisation of digital communication channels is presented. An additional term, based... Read More about Hybrid HOS-SOS approach for blind equalisation of communication channels.

A new multivariable generalized minimum-variance controller with pole-zero placement (2004)
Journal Article
Zayed, A. S., Hussain, A., & Smith, L. (2004). A new multivariable generalized minimum-variance controller with pole-zero placement. Control and Intelligent Systems, 32(1), 35-44. https://doi.org/10.2316/Journal.201.2004.1.201-1307

This article presents the derivation of a new robust multivariable adaptive controller, which minimizes a cost function, incorporating system input, system output, and set point. It provides an adaptive mechanism that ensures that both the closed-loo... Read More about A new multivariable generalized minimum-variance controller with pole-zero placement.

Higher-order statistics-based nonlinear speech analysis (2002)
Journal Article
Soraghan, J. J., Hussain, A., Alkulabi, A., & Durrani, T. (2002). Higher-order statistics-based nonlinear speech analysis. Control and Intelligent Systems, 30, 11-18

A fast and robust three-level binary higher order statistics (HOS) based algorithm for simultaneous voiced/unvoiced detection and pitch estimation of speech signals in coloured noise environments with low SNR is presented. The use of the three-level... Read More about Higher-order statistics-based nonlinear speech analysis.

Introduction (2002)
Journal Article
Hussain, A. (2002). Introduction. Control and Intelligent Systems, 30,

Introduction to Special Issue Number 1 of the international journal: Control and intelligent systems, nonlinear speech processing.

Nonlinear speech processing: Overview and applications (2002)
Journal Article
Faúndez-Zanuy, M., McLaughlin, S., Esposito, A., Hussain, A., Schoentgen, J., Kubin, G., …Maragos, P. (2002). Nonlinear speech processing: Overview and applications. Control and Intelligent Systems, 30, 1-10

This article presents an overview of various nonlinear processing techniques applied to speech signals. Evidence relating to the existence of nonlinearities in speech is presented, and the main differences between linear and nonlinear analysis are su... Read More about Nonlinear speech processing: Overview and applications.

Intelligibility improvements using binaural diverse sub-band processing applied to speech corrupted with automobile noise (2001)
Journal Article
Hussain, A., & Campbell, D. (2001). Intelligibility improvements using binaural diverse sub-band processing applied to speech corrupted with automobile noise. IEE Proceedings: Vision, Image and Signal Processing, 148(2), 127-132. https://doi.org/10.1049/i

The paper reports on experiments assessing the capability of a diverse processing, multi-microphone sub-band adaptive signal processing scheme for improving the intelligibility of speech corrupted with automobile noise. Results from formal listening... Read More about Intelligibility improvements using binaural diverse sub-band processing applied to speech corrupted with automobile noise.

Locally-recurrent neural-networks for real-time adaptive nonlinear prediction of non-stationary signals (2000)
Journal Article
Hussain, A. (2000). Locally-recurrent neural-networks for real-time adaptive nonlinear prediction of non-stationary signals. Control and Intelligent Systems, 28(2), 65-71

Outputs (237)