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All Outputs (49)

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

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

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

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

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

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

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review (2023)
Journal Article
Rashid, A., Al-Obeidat, F., Kanthimathinathan, H. K., Benakatti, G., Hafez, W., Ramaiah, R., …Hussain, A. (2024). Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review. Informatics in

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

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

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?.

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.