All Outputs (522)

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., Sharief, J., Brierley, J., Quraishi, N., Malik, Z. A., Anwary, A., Alkhzaimi, H., Zaki, S. A., Khilnani, P., Kadwa, R., Phatak, R., Schumacher, M., Shaikh, G., Al-Dubai, A., & 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.

A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing (2023)
Journal Article
Zhao, L., Zhao, Z., Zhang, E., Hawbani, A., Al-Dubai, A., Tan, Z., & Hussain, A. (2023). A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing. IEEE Journal on Selected Areas in Communications, 41(11), 3386-3400. https://doi.org/10.1109/jsac.2023.3310062

Vehicle Edge Computing (VEC) is a promising paradigm that exposes Mobile Edge Computing (MEC) to road scenarios. In VEC, task offloading can enable vehicles to offload the computing tasks to nearby Roadside Units (RSUs) that deploy computing capabili... Read More about A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing.

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023)
Journal Article
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognitive Computation, 16(1), 45-74. https://doi.org/10.1007/s12559-023-10179-8

Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep L... Read More about Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence.

A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation? (2023)
Journal Article
Rashid, A., Brusletto, B. S., Al-Obeidat, F., Toufiq, M., Benakatti, G., Brierley, J., Malik, Z. A., Hussain, Z., Alkhazaimi, H., Sharief, J., Kadwa, R., Sarpal, A., Chaussabel, D., Malik, R. A., Quraishi, N., Khilnani, P., Zaki, S. A., Nadeem, R., Shaikh, G., Al-Dubai, A., …Hussain, A. (2023). A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation?. Shock, 60(4), 503-516. https://doi.org/10.1097/shk.0000000000002192

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudin... Read More about A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation?.

Audio-visual speech enhancement and separation by utilizing multi-modal self-supervised embeddings (2023)
Presentation / Conference Contribution
Chern, I.-C., Hung, K.-H., Chen, Y.-T., Hussain, T., Gogate, M., Hussain, A., Tsao, Y., & Hou, J.-C. (2023, June). Audio-visual speech enhancement and separation by utilizing multi-modal self-supervised embeddings. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective for categorical problems such as automatic speech recognition and lip-reading. This suggests that useful audio-visual speech representations can be obtained via u... Read More about Audio-visual speech enhancement and separation by utilizing multi-modal self-supervised embeddings.

Frequency-Domain Functional Links For Nonlinear Feedback Cancellation In Hearing Aids (2023)
Presentation / Conference Contribution
Nezamdoust, A., Gogate, M., Dashtipour, K., Hussain, A., & Comminiello, D. (2023, June). Frequency-Domain Functional Links For Nonlinear Feedback Cancellation In Hearing Aids. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

The problem of feedback cancellation can be seen as a function approximation task, which often is nonlinear in real-world hearing assistive technologies. Nonlinear methods adopted for this task must exhibit outstanding modeling performance and reduce... Read More about Frequency-Domain Functional Links For Nonlinear Feedback Cancellation In Hearing Aids.

Audio-visual speech enhancement and separation by leveraging multimodal self-supervised embeddings (2023)
Presentation / Conference Contribution
Chern, I.-C., Hung, K.-H., Chen, Y.-T., Hussain, T., Gogate, M., Hussain, A., Tsao, Y., & Hou, J.-C. (2023, June). Audio-visual speech enhancement and separation by leveraging multimodal self-supervised embeddings. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective for categorical problems such as automatic speech recognition and lip-reading. This suggests that useful audio-visual speech representations can be obtained via u... Read More about Audio-visual speech enhancement and separation by leveraging multimodal self-supervised embeddings.

Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids (2023)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2023, June). Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

Classical audio-visual (AV) speech enhancement (SE) and separation methods have been successful at operating under constrained environments; however, the speech quality and intelligibility improvement is significantly reduced in unconstrained real-wo... Read More about Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids.

ESPP: Efficient Sector-based Charging Scheduling and Path Planning for WRSNs with Hexagonal Topology (2023)
Journal Article
Naji, A., Hawbani, A., Wang, X., Al-Gunid, H. M., Al-Dhabi, Y., Al-Dubai, A., Hussain, A., Zhao, L., & Alsamhi, S. H. (2024). ESPP: Efficient Sector-based Charging Scheduling and Path Planning for WRSNs with Hexagonal Topology. IEEE Transactions on Sustainable Computing, 9(1), 31 - 45. https://doi.org/10.1109/tsusc.2023.3296607

Wireless Power Transfer (WPT) is a promising technology that can potentially mitigate the energy provisioning problem for sensor networks. In order to efficiently replenish energy for these battery-powered devices, designing appropriate scheduling an... Read More about ESPP: Efficient Sector-based Charging Scheduling and Path Planning for WRSNs with Hexagonal Topology.

Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis (2023)
Journal Article
Diwali, A., Saeedi, K., Dashtipour, K., Gogate, M., Cambria, E., & Hussain, A. (2024). Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis. IEEE Transactions on Affective Computing, 15(3), 837-846. https://doi.org/10.1109/taffc.2023.3296373

Sentiment analysis can be used to derive knowledge that is connected to emotions and opinions from textual data generated by people. As computer power has grown, and the availability of benchmark datasets has increased, deep learning models based on... Read More about Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis.

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. (2024). Underwater image clarifying based on human visual colour constancy using double‐opponency. CAAI Transactions on Intelligence Technology, 9(3), 632-648. 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 in Computational Neuroscience, 17, Article 1242834. https://doi.org/10.3389/fncom.2023.1242834

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/10.1016/j.asoc.2023.110578

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., Mahmud, M., Liyanage, M., & Hussain, A. (2023). Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions. Cognitive Computation, 15, 1767-1812. https://doi.org/10.1007/s12559-023-10153-4

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/10.1111/exsy.13375

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. https://doi.org/10.3390/computers12060126

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., Sarpal, A., Toufiq, M., Malik, Z. A., Kadwa, R., Khilnani, P., Guftar Shaikh, M., Benakatti, G., Sharief, J., Ahmed Zaki, S., Zeyada, A., Al-Dubai, A., Hafez, W., & Hussain, A. (2023). Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis. Informatics in Medicine Unlocked, 41, Article 101293. https://doi.org/10.1016/j.imu.2023.101293

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. (2024). Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation. IEEE Transactions on Computational Social Systems, 11(4), 5101 - 5118. https://doi.org/10.1109/tcss.2023.3276764

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 Intelligence, https://doi.org/10.1109/tetci.2023.3281864

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.