All Outputs (329)

A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks (2024)
Journal Article
Aldosary, A., Tanveer, M., Ahmad, M., Maghrabi, L. A., Ahmed, E. A., Hussain, A., & El-Latif, A. A. A. (2024). A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/tce.2024.3473930

The Mobile crowdsourcing network (MCN) leverages collaborative intelligence to solve complex tasks through group cooperation. It comprises three main components: the end-user, the service provider, and the mobile user. The end-user requests crowd-sen... Read More about A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks.

RI-L1Approx: A novel Resnet-Inception-based Fast L1-approximation method for face recognition (2024)
Journal Article
Bajpai, S., Mishra, G., Jain, R., Jain, D. K., Saini, D., & Hussain, A. (online). RI-L1Approx: A novel Resnet-Inception-based Fast L1-approximation method for face recognition. Neurocomputing, 128708. https://doi.org/10.1016/j.neucom.2024.128708

Performance of deep learning methods for face recognition often relies on abundant data, posing challenges in surveillance and security where data availability is limited and environments are unconstrained. To address this challenge, we propose a nov... Read More about RI-L1Approx: A novel Resnet-Inception-based Fast L1-approximation method for face recognition.

MTFDN: An image copy‐move forgery detection method based on multi‐task learning (2024)
Journal Article
Liang, P., Tu, H., Hussain, A., & Li, Z. (online). MTFDN: An image copy‐move forgery detection method based on multi‐task learning. Expert Systems, https://doi.org/10.1111/exsy.13729

Image copy-move forgery, where an image region is copied and pasted within the same image, is a simple yet widely employed manipulation. In this paper, we rethink copy-move forgery detection from the perspective of multi-task learning and summarize t... Read More about MTFDN: An image copy‐move forgery detection method based on multi‐task learning.

Transition-aware human activity recognition using an ensemble deep learning framework (2024)
Journal Article
Khan, S. I., Dawood, H., Khan, M., F. Issa, G., Hussain, A., Alnfiai, M. M., & Adnan, K. M. (2025). Transition-aware human activity recognition using an ensemble deep learning framework. Computers in Human Behavior, 162, Article 108435. https://doi.org/10.1016/j.chb.2024.108435

Understanding human activities in daily life is of utmost importance, especially in the context of personalized and adaptive ubiquitous learning. Although existing HAR systems perform well-identifying activities based on their inter-spatial and tempo... Read More about Transition-aware human activity recognition using an ensemble deep learning framework.

Federated learning‐driven dual blockchain for data sharing and reputation management in Internet of medical things (2024)
Journal Article
Gan, C., Xiao, X., Zhu, Q., Jain, D. K., Saini, A., & Hussain, A. (online). Federated learning‐driven dual blockchain for data sharing and reputation management in Internet of medical things. Expert Systems, https://doi.org/10.1111/exsy.13714

In the Internet of Medical Things (IoMT), the vulnerability of federated learning (FL) to single points of failure, low-quality nodes, and poisoning attacks necessitates innovative solutions. This article introduces a FL-driven dual-blockchain approa... Read More about Federated learning‐driven dual blockchain for data sharing and reputation management in Internet of medical things.

A Comprehensive Survey on Generative AI for Metaverse: Enabling Immersive Experience (2024)
Journal Article
Chamola, V., Sai, S., Bhargava, A., Sahu, A., Jiang, W., Xiong, Z., Niyato, D., & Hussain, A. (online). A Comprehensive Survey on Generative AI for Metaverse: Enabling Immersive Experience. Cognitive Computation, https://doi.org/10.1007/s12559-024-10342-9

Generative Artificial Intelligence models are Artificial Intelligence models that generate new content based on a prompt or input. The output content can be in various forms, including text, images, and video. Metaverse refers to a virtual world wher... Read More about A Comprehensive Survey on Generative AI for Metaverse: Enabling Immersive Experience.

Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition (2024)
Journal Article
Gao, F., Luo, X., Lang, R., Wang, J., Sun, J., & Hussain, A. (2024). Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition. Remote Sensing, 16(17), Article 3277. https://doi.org/10.3390/rs16173277

Current synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenario... Read More about Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition.

Context-Aware Audio-Visual Speech Enhancement Based on Neuro-Fuzzy Modelling and User Preference Learning (2024)
Journal Article
Chen, S., Kirton-Wingate, J., Doctor, F., Arshad, U., Dashtipour, K., Gogate, M., Halim, Z., Al-Dubai, A., Arslan, T., & Hussain, A. (online). Context-Aware Audio-Visual Speech Enhancement Based on Neuro-Fuzzy Modelling and User Preference Learning. IEEE Transactions on Fuzzy Systems, https://doi.org/10.1109/tfuzz.2024.3435050

It is estimated that by 2050 approximately one in ten individuals globally will experience disabling hearing impairment. In the presence of everyday reverberant noise, a substantial proportion of individual users encounter challenges in speech compre... Read More about Context-Aware Audio-Visual Speech Enhancement Based on Neuro-Fuzzy Modelling and User Preference Learning.

Utilizing ubiquitous learning to foster sustainable development in rural areas: Insights from 6G technology (2024)
Journal Article
Liu, Y., Razman, M. R., Syed Zakaria, S. Z., Ern, L. K., Hussain, A., & Chamola, V. (2024). Utilizing ubiquitous learning to foster sustainable development in rural areas: Insights from 6G technology. Computers in Human Behavior, 161, Article 108418. https://doi.org/10.1016/j.chb.2024.108418

Rural education frequently grapples with demanding situations such as isolation, confined assets, and a virtual divide. The emergence of the sixth generation (6G) era, characterized by its speedy connectivity, minimal latency, and robust reliability,... Read More about Utilizing ubiquitous learning to foster sustainable development in rural areas: Insights from 6G technology.

A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs (2024)
Journal Article
Tmamna, J., Fourati, R., Ayed, E. B., Passos, L. A., Papa, J. P., Ayed, M. B., & Hussain, A. (2024). A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs. Neurocomputing, 608, Article 128378. https://doi.org/10.1016/j.neucom.2024.128378

Deep Convolutional Neural Networks (CNNs), continue to demonstrate remarkable performance across various tasks. However, their computational demands and energy consumption present significant drawbacks, restricting their practical deployment and cont... Read More about A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs.

A CNN pruning approach using constrained binary particle swarm optimization with a reduced search space for image classification (2024)
Journal Article
Tmamna, J., Ayed, E. B., Fourati, R., Hussain, A., & Ayed, M. B. (2024). A CNN pruning approach using constrained binary particle swarm optimization with a reduced search space for image classification. Applied Soft Computing, 164, Article 111978. https://doi.org/10.1016/j.asoc.2024.111978

Deep convolutional neural networks (CNNs) have exhibited exceptional performance in a range of computer vision tasks. However, these deep CNNs typically demand significant computational resources, which not only hinders their practical deployment but... Read More about A CNN pruning approach using constrained binary particle swarm optimization with a reduced search space for image classification.

Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey (2024)
Journal Article
Tmamna, J., Ayed, E. B., Fourati, R., Gogate, M., Arslan, T., Hussain, A., & Ayed, M. B. (online). Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey. Cognitive Computation, https://doi.org/10.1007/s12559-024-10313-0

Over the past few years, larger and deeper neural network models, particularly convolutional neural networks (CNNs), have consistently advanced state-of-the-art performance across various disciplines. Yet, the computational demands of these models ha... Read More about Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey.

Arabic text classification based on analogical proportions (2024)
Journal Article
Bounhas, M., Elayeb, B., Chouigui, A., Hussain, A., & Cambria, E. (2024). Arabic text classification based on analogical proportions. Expert Systems, 41(10), Article e13609. https://doi.org/10.1111/exsy.13609

Text classification is the process of labelling a given set of text documents with predefined classes or categories. Existing Arabic text classifiers are either applying classic Machine Learning algorithms such as k-NN and SVM or using modern deep le... Read More about Arabic text classification based on analogical proportions.

Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey (2024)
Journal Article
Ali, A. H., Charfeddine, M., Ammar, B., Hamed, B. B., Albalwy, F., Alqarafi, A., & Hussain, A. (2024). Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey. Frontiers in Computer Science, 6, Article 1387354. https://doi.org/10.3389/fcomp.2024.1387354

The advancement of communication and internet technology has brought risks to network security. Thus, Intrusion Detection Systems (IDS) was developed to combat malicious network attacks. However, IDSs still struggle with accuracy, false alarms, and d... Read More about Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey.

Multi‐model deep learning system for screening human monkeypox using skin images (2024)
Journal Article
Gupta, K., Bajaj, V., Jain, D. K., & Hussain, A. (2024). Multi‐model deep learning system for screening human monkeypox using skin images. Expert Systems, 41(10), Article e13651. https://doi.org/10.1111/exsy.13651

Purpose
Human monkeypox (MPX) is a viral infection that transmits between individuals via direct contact with animals, bodily fluids, respiratory droplets, and contaminated objects like bedding. Traditional manual screening for the MPX infection is... Read More about Multi‐model deep learning system for screening human monkeypox using skin images.

A Change Severity Degree-based Dynamic Multi-Objective Optimization Algorithm with Adaptive Response Strategy (2024)
Journal Article
Kouka, N., Fourati, R., Fdhila, R., Hussain, A., & Alimi, A. M. (2024). A Change Severity Degree-based Dynamic Multi-Objective Optimization Algorithm with Adaptive Response Strategy. Information Sciences, 677, Article 120794. https://doi.org/10.1016/j.ins.2024.120794

Many real-world optimization problems are dynamic by nature, exhibiting temporal variations in objective functions, constraints, and parameters. These problems present significant challenges for algorithm convergence and diversity,... Read More about A Change Severity Degree-based Dynamic Multi-Objective Optimization Algorithm with Adaptive Response Strategy.

DDformer: Dimension decomposition transformer with semi-supervised learning for underwater image enhancement (2024)
Journal Article
Gao, Z., Yang, J., Jiang, F., Jiao, X., Dashtipour, K., Gogate, M., & Hussain, A. (2024). DDformer: Dimension decomposition transformer with semi-supervised learning for underwater image enhancement. Knowledge-Based Systems, 297, Article 111977. https://doi.org/10.1016/j.knosys.2024.111977

Vision-guided Autonomous Underwater Vehicles (AUVs) have gradually become significant tools for human exploration of the ocean. However, distorted images severely limit the visual ability, making it difficult to meet the needs of complex underwater e... Read More about DDformer: Dimension decomposition transformer with semi-supervised learning for underwater image enhancement.

Impact of the Covid-19 pandemic on audiology service delivery: Observational study of the role of social media in patient communication (2024)
Journal Article
Hussain, A., Hussain, Z., Gogate, M., Dashtipour, K., Ng, D., Riaz, M. S., Goman, A., Sheikh, A., & Hussain, A. (2024). Impact of the Covid-19 pandemic on audiology service delivery: Observational study of the role of social media in patient communication. PLOS ONE, 19(4), Article e0288223. https://doi.org/10.1371/journal.pone.0288223

The Covid-19 pandemic has highlighted an era in hearing health care that necessitates a comprehensive rethinking of audiology service delivery. There has been a significant increase in the number of individuals with hearing loss who seek information... Read More about Impact of the Covid-19 pandemic on audiology service delivery: Observational study of the role of social media in patient communication.

Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare (2024)
Journal Article
Naseem, U., Thapa, S., Zhang, Q., Wang, S., Rashid, J., Hu, L., & Hussain, A. (2024). Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare. Neurocomputing, 592, Article 127736. https://doi.org/10.1016/j.neucom.2024.127736

The digitization of healthcare systems has led to the proliferation of electronic health records (EHRs), serving as comprehensive repositories of patient information. However, the vast volume and complexity of EHR data present challenges in extractin... Read More about Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare.

SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement (2024)
Journal Article
Gao, F., Han, X., Wang, J., Sun, J., Hussain, A., & Zhou, H. (2024). SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 11365-11385. https://doi.org/10.1109/jstars.2024.3383779

There are several unresolved issues in the field of ship instance segmentation in synthetic aperture radar (SAR) images. First, in inshore dense ship area, the problems of missed detections and mask overlap frequently occur. Second, in inshore scenes... Read More about SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement.