Outputs (41)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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