All Outputs (518)

Artificial intelligence enabled smart mask for speech recognition for future hearing devices (2024)
Journal Article
Hameed, H., Lubna, Usman, M., Kazim, J. U. R., Assaleh, K., Arshad, K., Hussain, A., Imran, M., & Abbasi, Q. H. (2024). Artificial intelligence enabled smart mask for speech recognition for future hearing devices. Scientific Reports, 14(1), Article 30112. https://doi.org/10.1038/s41598-024-81904-y

In recent years, Lip-reading has emerged as a significant research challenge. The aim is to recognise speech by analysing Lip movements. The majority of Lip-reading technologies are based on cameras and wearable devices. However, these technologies h... Read More about Artificial intelligence enabled smart mask for speech recognition for future hearing devices.

Are Foundation Models the Next-Generation Social Media Content Moderators? (2024)
Journal Article
Nadeem, M., Javed, L., Sohail, S. S., Cambria, E., & Hussain, A. (2024). Are Foundation Models the Next-Generation Social Media Content Moderators?. IEEE Intelligent Systems, 39(6), 70-80. https://doi.org/10.1109/mis.2024.3477109

Recent progress in artificial intelligence (AI) tools and systems has been significant, especially in their reasoning and efficiency. Notable examples include generative AI-based large language models (LLMs) like Generative Pre-trained Transformer 3.... Read More about Are Foundation Models the Next-Generation Social Media Content Moderators?.

An Attention‐Driven Hybrid Deep Neural Network for Enhanced Heart Disease Classification (2024)
Journal Article
Lilhore, U. K., Simaiya, S., Alhussein, M., Dalal, S., Aurangzeb, K., & Hussain, A. (2025). An Attention‐Driven Hybrid Deep Neural Network for Enhanced Heart Disease Classification. Expert Systems, 42(2), Article e13791. https://doi.org/10.1111/exsy.13791

Heart disease continues to be a primary cause of mortality globally, highlighting the critical necessity for efficient early prediction and classification techniques. This study presents a new hybrid model attention-based CNN-Bi-LSTM that integrates... Read More about An Attention‐Driven Hybrid Deep Neural Network for Enhanced Heart Disease Classification.

A Novel Multi-Sensor Nonlinear Tightly-Coupled Framework for Composite Robot Localization and Mapping (2024)
Journal Article
Chen, L., Hussain, A., Liu, Y., Tan, J., Li, Y., Yang, Y., Ma, H., Fu, S., & Li, G. (2024). A Novel Multi-Sensor Nonlinear Tightly-Coupled Framework for Composite Robot Localization and Mapping. Sensors, 24(22), Article 7381. https://doi.org/10.3390/s24227381

Composite robots often encounter difficulties due to changes in illumination, external disturbances, reflective surface effects, and cumulative errors. These challenges significantly hinder their capabilities in environmental perception and the accur... Read More about A Novel Multi-Sensor Nonlinear Tightly-Coupled Framework for Composite Robot Localization and Mapping.

RF sensing enabled tracking of human facial expressions using machine learning algorithms (2024)
Journal Article
Hameed, H., Elsayed, M., Kaur, J., Usman, M., Tang, C., Ghadban, N., Kernec, J. L., Hussain, A., Imran, M., & Abbasi, Q. H. (2024). RF sensing enabled tracking of human facial expressions using machine learning algorithms. Scientific Reports, 14(1), Article 27800. https://doi.org/10.1038/s41598-024-75909-w

Automatic analysis of facial expressions has emerged as a prominent research area in the past decade. Facial expressions serve as crucial indicators for understanding human behavior, enabling the identification and assessment of positive and negative... Read More about RF sensing enabled tracking of human facial expressions using machine learning algorithms.

Open-Pose 3D zero-shot learning: Benchmark and challenges (2024)
Journal Article
Zhao, W., Yang, G., Zhang, R., Jiang, C., Yang, C., Yan, Y., Hussain, A., & Huang, K. (2025). Open-Pose 3D zero-shot learning: Benchmark and challenges. Neural Networks, 181, Article 106775. https://doi.org/10.1016/j.neunet.2024.106775

With the explosive 3D data growth, the urgency of utilizing zero-shot learning to facilitate data labeling becomes evident. Recently, methods transferring language or language-image pre-training models like Contrastive Language-Image Pre-training (CL... Read More about Open-Pose 3D zero-shot learning: Benchmark and challenges.

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.

Development of multi-modal hearing aids to enhance speech perception in noise (2024)
Presentation / Conference Contribution
Goman, A., Gogate, M., Hussain, A., Dashtipour, K., Buck, B., Akeroyd, M., Anwar, U., Arslan, T., Hardy, D., & Hussain, A. (2024, September). Development of multi-modal hearing aids to enhance speech perception in noise. Presented at World Congress of Audiology, Paris, France

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. (2024). A Comprehensive Survey on Generative AI for Metaverse: Enabling Immersive Experience. Cognitive Computation, 16, 3286–3315. 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. (2024). Context-Aware Audio-Visual Speech Enhancement Based on Neuro-Fuzzy Modelling and User Preference Learning. IEEE Transactions on Fuzzy Systems, 32(10), 5400-5412. 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. (2024). Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey. Cognitive Computation, 16, 2931–2952. 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.