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Underwater image clarifying based on human visual colour constancy using double‐opponency (2023)
Journal Article
Kong, B., Qian, J., Song, P., Yang, J., & Hussain, A. (in press). Underwater image clarifying based on human visual colour constancy using double‐opponency. CAAI Transactions on Intelligence Technology, https://doi.org/10.1049/cit2.12260

Underwater images are often with biased colours and reduced contrast because of the absorption and scattering effects when light propagates in water. Such images with degradation cannot meet the needs of underwater operations. The main problem in cla... Read More about Underwater image clarifying based on human visual colour constancy using double‐opponency.

Editorial: The new frontier in brain network physiology: from temporal dynamics to the principles of integration in physiological brain networks (2023)
Journal Article
Trenado, C., Mendez-Balbuena, I., Damborská, A., Hussain, A., Mahmud, M., & Daliri, M. R. (2023). Editorial: The new frontier in brain network physiology: from temporal dynamics to the principles of integration in physiological brain networks. Frontiers

Editorial on the Research Topic - The new frontier in brain network physiology: from temporal dynamics to the principles of integration in physiological brain networks

Steel surface defect detection based on self-supervised contrastive representation learning with matching metric (2023)
Journal Article
Hu, X., Yang, J., Jiang, F., Hussain, A., Dashtipour, K., & Gogate, M. (2023). Steel surface defect detection based on self-supervised contrastive representation learning with matching metric. Applied Soft Computing, 145, Article 110578. https://doi.org/1

Defect detection is crucial in the quality control of industrial applications. Existing supervised methods are heavily reliant on the large amounts of labeled data. However, labeled data in some specific fields are still scarce, and it requires profe... Read More about Steel surface defect detection based on self-supervised contrastive representation learning with matching metric.

Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions (2023)
Journal Article
Javed, A. R., Saadia, A., Mughal, H., Gadekallu, T. R., Rizwan, M., Maddikunta, P. K. R., …Hussain, A. (2023). Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions. Cognitive Computation, 15, 1

The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligenc... Read More about Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions.

A Hurst‐based diffusion model using time series characteristics for influence maximization in social networks (2023)
Journal Article
Saxena, B., Saxena, V., Anand, N., Hassija, V., Chamola, V., & Hussain, A. (2023). A Hurst‐based diffusion model using time series characteristics for influence maximization in social networks. Expert Systems, 40(9), Article e13375. https://doi.org/10.1

Online social networks have grown exponentially in the recent years while finding applications in real life like marketing, recommendation systems, and social awareness campaigns. An important research area in this field is Influence Maximization, wh... Read More about A Hurst‐based diffusion model using time series characteristics for influence maximization in social networks.

Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning (2023)
Journal Article
Elhassan, N., Varone, G., Ahmed, R., Gogate, M., Dashtipour, K., Almoamari, H., El-Affendi, M. A., Al-Tamimi, B. N., Albalwy, F., & Hussain, A. (2023). Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning. Computers, 12(6), Article 126. ht

Social media networks have grown exponentially over the last two decades, providing the opportunity for users of the internet to communicate and exchange ideas on a variety of topics. The outcome is that opinion mining plays a crucial role in analyzi... Read More about Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning.

Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis (2023)
Journal Article
Rashid, A., Anwary, A. R., Al-Obeidat, F., Brierley, J., Uddin, M., Alkhzaimi, H., …Hussain, A. (2023). Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal seps

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The... Read More about Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis.

Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation (2023)
Journal Article
Wani, M. A., ELAffendi, M., Shakil, K. A., Abuhaimed, I. M., Nayyar, A., Hussain, A., & El-Latif, A. A. A. (in press). Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation. IEEE Transactions on Computational Social Systems, h

The emergence of COVID-19 has led to a surge in fake news on social media, with toxic fake news having adverse effects on individuals, society, and governments. Detecting toxic fake news is crucial, but little prior research has been done in this are... Read More about Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation.

PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification (2023)
Journal Article
Yao, K., Huang, K., Sun, J., & Hussain, A. (2023). PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification. IEEE Transactions on Emerging Topics in Computational Intelligen

Automatic nuclei segmentation and classification play a vital role in digital pathology. However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or misleading in actual downstream tasks... Read More about PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification.

WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text (2023)
Journal Article
Rashid, J., Kim, J., Hussain, A., & Naseem, U. (2023). WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text. Pattern Recognition Letters, 172, 158-164. https://doi.org/10.1016/j.patrec.2023.06.007

Short texts are a common source of knowledge, and the extraction of such valuable information is beneficial for several purposes. Traditional topic models are incapable of analyzing the internal structural information of topics. They are mostly based... Read More about WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text.

Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids (2023)
Presentation / Conference Contribution
Kirton-Wingate, J., Ahmed, S., Gogate, M., Tsao, Y., & Hussain, A. (2023, June). Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids. Presented at 2023 IEEE International Conference on Acoustics, Speec

Since the advent of deep learning (DL), speech enhancement (SE) models have performed well under a variety of noise conditions. However, such systems may still introduce sonic artefacts, sound unnatural, and restrict the ability for a user to hear am... Read More about Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids.

A real‐time lane detection network using two‐directional separation attention (2023)
Journal Article
Zhang, L., Jiang, F., Yang, J., Kong, B., & Hussain, A. (2023). A real‐time lane detection network using two‐directional separation attention. Computer-Aided Civil and Infrastructure Engineering, https://doi.org/10.1111/mice.13051

Real-time network by adopting attention mechanism is helpful for enhancing lane detection capability of autonomous vehicles. This paper proposes a real-time lane detection network (TSA-LNet) that incorporates a lightweight network (LNet) and a two-di... Read More about A real‐time lane detection network using two‐directional separation attention.

Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG (2023)
Journal Article
Shah, J., Chougule, A., Chamola, V., & Hussain, A. (2023). Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG. Neurocomputing, 549, Article 126387. https://doi.org/10.1016/j.neuco

The growing demand for semi-autonomous human–machine systems has led to an increased requirement for human fatigue detection. Direct and invasive approaches for microsleep detection include cognitive computing methods using Brain-Computer Interfaces... Read More about Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG.

A novel multimodal online news popularity prediction model based on ensemble learning (2023)
Journal Article
Arora, A., Hassija, V., Bansal, S., Yadav, S., Chamola, V., & Hussain, A. (2023). A novel multimodal online news popularity prediction model based on ensemble learning. Expert Systems, 40(8), Article e13336. https://doi.org/10.1111/exsy.13336

The prediction of news popularity is having substantial importance for the digital advertisement community in terms of selecting and engaging users. Traditional approaches are based on empirical data collected through surveys and applied statistical... Read More about A novel multimodal online news popularity prediction model based on ensemble learning.

Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids (2023)
Presentation / Conference Contribution
Bishnu, A., Gupta, A., Gogate, M., Dashtipour, K., Arslan, T., Adeel, A., …Ratnarajah, T. (2023). Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids. In IEEE ISCAS 2023 Symposium Proceedings. https://doi.org/10.1

Hearing loss is among the most serious public health problems, affecting as much as 20% of the worldwide population. Even cutting-edge multi-channel audio-only speech enhancement (SE) algorithms used in modern hearing aids face significant hurdles si... Read More about Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids.

Live Demonstration: Real-time Multi-modal Hearing Assistive Technology Prototype (2023)
Presentation / Conference Contribution
Gogate, M., Hussain, A., Dashtipour, K., & Hussain, A. (2023). Live Demonstration: Real-time Multi-modal Hearing Assistive Technology Prototype. In IEEE ISCAS 2023 Symposium Proceedings. https://doi.org/10.1109/iscas46773.2023.10182070

Hearing loss affects at least 1.5 billion people globally. The WHO estimates 83% of people who could benefit from hearing aids do not use them. Barriers to HA uptake are multifaceted but include ineffectiveness of current HA technology in noisy envir... Read More about Live Demonstration: Real-time Multi-modal Hearing Assistive Technology Prototype.

A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction (2023)
Journal Article
Huang, H., Zhao, B., Gao, F., Chen, P., Wang, J., & Hussain, A. (2023). A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction. Sensors, 23(10), Article 4828. https://doi.org/10.3390/s231048

Reconstruction-based and prediction-based approaches are widely used for video anomaly detection (VAD) in smart city surveillance applications. However, neither of these approaches can effectively utilize the rich contextual information that exists i... Read More about A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction.

Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning (2023)
Journal Article
Basabain, S., Cambria, E., Alomar, K., & Hussain, A. (2023). Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning. Expert Systems, 40(8), Article e13329. https://doi.org/10.1111/exsy.13329

A growing amount of research use pre-trained language models to address few/zero-shot text classification problems. Most of these studies neglect the semantic information hidden implicitly beneath the natural language names of class labels and develo... Read More about Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning.

An Incremental SAR Target Recognition Framework via Memory-Augmented Weight Alignment and Enhancement Discrimination (2023)
Journal Article
Huang, H., Gao, F., Wang, J., Hussain, A., & Zhou, H. (2023). An Incremental SAR Target Recognition Framework via Memory-Augmented Weight Alignment and Enhancement Discrimination. IEEE Geoscience and Remote Sensing Letters, 20, https://doi.org/10.1109/lgr

Synthetic aperture radar automatic target recognition (SAR ATR) is one of the most important research directions in SAR image interpretation. While much existing research into SAR ATR has focused on deep learning technology, an equally important yet... Read More about An Incremental SAR Target Recognition Framework via Memory-Augmented Weight Alignment and Enhancement Discrimination.

Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets (2023)
Journal Article
Adeel, A., Adetomi, A., Ahmed, K., Hussain, A., Arslan, T., & Phillips, W. A. (2023). Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets. IEEE Transactions on Emerging Topics in Computational Intelligence,

Context-sensitive two-point layer 5 pyramidal cells (L5PCs) were discovered as long ago as 1999. However, the potential of this discovery to provide useful neural computation has yet to be demonstrated. Here we show for the first time how a transform... Read More about Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets.