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Bare‐Bones particle Swarm optimization‐based quantization for fast and energy efficient convolutional neural networks (2023)
Journal Article
Tmamna, J., Ayed, E. B., Fourati, R., Hussain, A., & Ayed, M. B. (2024). Bare‐Bones particle Swarm optimization‐based quantization for fast and energy efficient convolutional neural networks. Expert Systems, 41(4), Article e13522. https://doi.org/10.1

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

Machine Un-learning: An Overview of Techniques, Applications, and Future Directions (2023)
Journal Article
Sai, S., Mittal, U., Chamola, V., Huang, K., Spinelli, I., Scardapane, S., …Hussain, A. (2024). Machine Un-learning: An Overview of Techniques, Applications, and Future Directions. Cognitive Computation, 16, 482-506. https://doi.org/10.1007/s12559-023-1

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

Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance (2023)
Journal Article
Anwar, K., Zafar, A., Iqbal, A., Sohail, S. S., Hussain, A., Karaca, Y., …Muhammad, K. (2023). Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance. Fractals, 31(10), Article 2340149. https:

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

Multi-criteria decision making-based waste management: A bibliometric analysis (2023)
Journal Article
Sohail, S. S., Javed, Z., Nadeem, M., Anwer, F., Farhat, F., Hussain, A., …Madsen, D. Ø. (2023). Multi-criteria decision making-based waste management: A bibliometric analysis. Heliyon, 9(11), Article e21261. https://doi.org/10.1016/j.heliyon.2023.e212

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

A dual covariant biomarker approach to Kawasaki disease, using vascular endothelial growth factor A and B gene expression; implications for coronary pathogenesis (2023)
Journal Article
Rashid, A., Benakatti, G., Al-Obeidat, F., Phatak, R., Malik, Z. A., Sharief, J., …Hussain, A. (2023). A dual covariant biomarker approach to Kawasaki disease, using vascular endothelial growth factor A and B gene expression; implications for coronary p

Introduction Kawasaki disease (KD) is the most common vasculitis in young children, with coronary artery lesions (CALs) and coronary aneurysms (CAAs) being responsible for most KD-related deaths. Objective We hypothesized that Vascular Endotheli... Read More about A dual covariant biomarker approach to Kawasaki disease, using vascular endothelial growth factor A and B gene expression; implications for coronary pathogenesis.

Intrusion Detection Systems Using Machine Learning (2023)
Book Chapter
Taylor, W., Hussain, A., Gogate, M., Dashtipour, K., & Ahmad, J. (2024). Intrusion Detection Systems Using Machine Learning. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Enviro

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

VLC-Assisted Safety Message Dissemination in Roadside Infrastructure-Less IoV Systems: Modeling and Analysis (2023)
Journal Article
Xie, Y., Xu, D., Zhang, T., Yu, K., Hussain, A., & Guizani, M. (2024). VLC-Assisted Safety Message Dissemination in Roadside Infrastructure-Less IoV Systems: Modeling and Analysis. IEEE Internet of Things, 11(5), 8185-8198. https://doi.org/10.1109/jiot.20

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

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

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

CoDeS: A Deep Learning Framework for Identifying COVID-Caused Depression Symptoms (2023)
Journal Article
Wani, M. A., ELAffendi, M., Bours, P., Imran, A. S., Hussain, A., & Abd El-Latif, A. A. (2024). CoDeS: A Deep Learning Framework for Identifying COVID-Caused Depression Symptoms. Cognitive Computation, 16(1), 305-325. https://doi.org/10.1007/s12559-023-10

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

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

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

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes (2023)
Journal Article
Rashid, A., Al-Obeida, F., Hafez, W., Benakatti, G., Malik, R. A., Koutentis, C., …Hussain, A. (2024). Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes. Shock, 61(1), 4-18. https://

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

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

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

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

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

A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation? (2023)
Journal Article
Rashid, A., Brusletto, B. S., Al-Obeidat, F., Toufiq, M., Benakatti, G., Brierley, J., …Hussain, A. (2023). A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role fo

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

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

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

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

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

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

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

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

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

ESPP: Efficient Sector-based Charging Scheduling and Path Planning for WRSNs with Hexagonal Topology (2023)
Journal Article
Naji, A., Hawbani, A., Wang, X., Al-Gunid, H. M., Al-Dhabi, Y., Al-Dubai, A., …Alsamhi, S. H. (2024). ESPP: Efficient Sector-based Charging Scheduling and Path Planning for WRSNs with Hexagonal Topology. IEEE Transactions on Sustainable Computing, 9(1),

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

Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis (2023)
Journal Article
Diwali, A., Saeedi, K., Dashtipour, K., Gogate, M., Cambria, E., & Hussain, A. (in press). Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis. IEEE Transactions on Affective Computing, https://doi.org/

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