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Application of machine learning in predicting frailty syndrome in patients with heart failure (2024)
Journal Article
Szczepanowski, R., Uchmanowicz, I., Pasieczna-Dixit, A. H., Sobecki, J., Katarzyniak, R., Kołaczek, G., …Kahsin, A. (2024). Application of machine learning in predicting frailty syndrome in patients with heart failure. Advances in Clinical and Experimental Medicine, 33(3), 309-315. https://doi.org/10.17219/acem/184040

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.2023.123056

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

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review (2023)
Journal Article
Rashid, A., Al-Obeidat, F., Kanthimathinathan, H. K., Benakatti, G., Hafez, W., Ramaiah, R., …Hussain, A. (2024). Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review. Informatics in Medicine Unlocked, 44, Article 101419. https://doi.org/10.1016/j.imu.2023.101419

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Resolving the Decreased Rank Attack in RPL’s IoT Networks (2023)
Conference Proceeding
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-IoT) (65-68). https://doi.org/10.1109/DCOSS-IoT58021.2023.00018

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://doi.org/10.1097/shk.0000000000002227

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