Prof Amir Hussain's Outputs (527)

A Novel Continual Learning and Adaptive Sensing State Response‐Based Target Recognition and Long‐Term Tracking Framework for Smart Industrial Applications (2025)
Journal Article
Chen, L., Li, G., Tan, J., Li, Y., Fu, S., Ma, H., Liu, Y., Yang, Y., Qian, W., Zhu, Q., & Hussain, A. (2025). A Novel Continual Learning and Adaptive Sensing State Response‐Based Target Recognition and Long‐Term Tracking Framework for Smart Industrial Applications. Expert Systems, 42(5), Article e70037. https://doi.org/10.1111/exsy.70037

Purpose
With the rapid development of artificial intelligence technology, highly intelligent and unmanned factories have become an important trend. In the complex environments of smart factories, the long-term tracking and inspection of specified ta... Read More about A Novel Continual Learning and Adaptive Sensing State Response‐Based Target Recognition and Long‐Term Tracking Framework for Smart Industrial Applications.

Arabic Short-text Dataset for Sentiment Analysis of Tourism and Leisure Events (2025)
Journal Article
Basabain, S., Al‐Dubai, A., Cambria, E., Alomar, K., & Hussain, A. (2025). Arabic Short-text Dataset for Sentiment Analysis of Tourism and Leisure Events. Expert Systems, 42(5), Article e70030. https://doi.org/10.1111/exsy.70030

The focus of this study is to present the detailed process of collecting a dataset of Arabic short-text in the tourism context and annotating this dataset for the task of sentiment analysis using an automatic zero-shot labelling technique utilising t... Read More about Arabic Short-text Dataset for Sentiment Analysis of Tourism and Leisure Events.

TIxAI: A Trustworthiness Index for eXplainable AI in skin lesions classification (2025)
Journal Article
Ieracitano, C., Morabito, F. C., Hussain, A., Suffian, M., & Mammone, N. (2025). TIxAI: A Trustworthiness Index for eXplainable AI in skin lesions classification. Neurocomputing, 630, Article 129701. https://doi.org/10.1016/j.neucom.2025.129701

Skin cancer is one of the leading causes of mortality worldwide. Early diagnosis can ensure more effective patient treatment and outcomes, but, this is challenging due to the high similarity between different skin lesion types. There is a growing int... Read More about TIxAI: A Trustworthiness Index for eXplainable AI in skin lesions classification.

Generative AI for Finance: Applications, Case Studies and Challenges (2025)
Journal Article
Sai, S., Arunakar, K., Chamola, V., Hussain, A., Bisht, P., & Kumar, S. (2025). Generative AI for Finance: Applications, Case Studies and Challenges. Expert Systems, 42(3), Article e70018. https://doi.org/10.1111/exsy.70018

Generative AI (GAI), which has become increasingly popular nowadays, can be considered a brilliant computational machine that can not only assist with simple searching and organising tasks but also possesses the capability to propose new ideas, make... Read More about Generative AI for Finance: Applications, Case Studies and Challenges.

Unleashing the Power of Generative AI in Agriculture 4.0 for Smart and Sustainable Farming (2025)
Journal Article
Sai, S., Kumar, S., Gaur, A., Goyal, S., Chamola, V., & Hussain, A. (2025). Unleashing the Power of Generative AI in Agriculture 4.0 for Smart and Sustainable Farming. Cognitive Computation, 17(1), Article 63. https://doi.org/10.1007/s12559-025-10420-6

Generative artificial intelligence (GAI) represents a pioneering class of artificial intelligence systems renowned for producing diverse media, such as text and images. Agriculture 4.0 (AG-4.0) is a concept that integrates advanced technologies such... Read More about Unleashing the Power of Generative AI in Agriculture 4.0 for Smart and Sustainable Farming.

A Novel Reciprocal Domain Adaptation Neural Network for Enhanced Diagnosis of Chronic Kidney Disease (2025)
Journal Article
Iqbal, S., Qureshi, A. N., Alhussein, M., Aurangzeb, K., Zubair, M., & Hussain, A. (2025). A Novel Reciprocal Domain Adaptation Neural Network for Enhanced Diagnosis of Chronic Kidney Disease. Expert Systems, 42(2), Article e13825. https://doi.org/10.1111/exsy.13825

Chronic kidney disease (CKD) is a major global health concern caused mostly by high blood pressure and glucose levels. Detecting CKD early is critical for reducing its negative consequences since it can lead to increased mortality rates. With CKD's r... Read More about A Novel Reciprocal Domain Adaptation Neural Network for Enhanced Diagnosis of Chronic Kidney Disease.

MA-Net: Resource-efficient multi-attentional network for end-to-end speech enhancement (2024)
Journal Article
Wahab, F. E., Ye, Z., Saleem, N., Ullah, R., & Hussain, A. (2025). MA-Net: Resource-efficient multi-attentional network for end-to-end speech enhancement. Neurocomputing, 619, Article 129150. https://doi.org/10.1016/j.neucom.2024.129150

Deep Neural Networks (DNNs) have transformed speech enhancement (SE) by solving the complex relationships within speech signals through their multi-layered hierarchical representations. However, their computational demands remain a challenging proble... Read More about MA-Net: Resource-efficient multi-attentional network for end-to-end speech enhancement.

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?.

A Hybrid Semantics and Syntax-Based Graph Convolutional Network for Aspect-Level Sentiment Classification (2024)
Journal Article
Huang, C., Li, X., Du, Y., Dong, Z., Huang, D., Kumar Jain, D., & Hussain, A. (2025). A Hybrid Semantics and Syntax-Based Graph Convolutional Network for Aspect-Level Sentiment Classification. Cognitive Computation, 17(1), Article 16. https://doi.org/10.1007/s12559-024-10367-0

Aspect-level sentiment classification seeks to ascertain the sentiment polarities of individual aspects within a sentence. Most existing research in this field focuses on individually assessing the importance of contexts on individual aspects, disreg... Read More about A Hybrid Semantics and Syntax-Based Graph Convolutional Network for Aspect-Level Sentiment Classification.

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.

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.

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.

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. (2025). MTFDN: An image copy‐move forgery detection method based on multi‐task learning. Expert Systems, 42(2), Article e13729. 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. (2025). Federated learning‐driven dual blockchain for data sharing and reputation management in Internet of medical things. Expert Systems, 42(2), Article e13714. 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.