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All Outputs (71)

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.

Federated Learning for Market Surveillance (2024)
Book Chapter
Song, P., Kanwal, S., Dashtipour, K., & Gogate, M. (2024). Federated Learning for Market Surveillance. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (199-218). Springer. https://doi.org/10.1007/978-3-031-47590-0_10

The data utilized in market surveillance is highly sensitive; what may be available for machine learning is limited. In this paper, we examine how federated learning for time series data can be used to identify potential market abuse while maintainin... Read More about Federated Learning for Market Surveillance.

Statistical Downscaling Modeling for Temperature Prediction (2024)
Book Chapter
Ashraf, Z., Kanwal, B., Hussain, I., Dashtipour, K., Gogate, M., & Kanwal, S. (2024). Statistical Downscaling Modeling for Temperature Prediction. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (147-169). Springer. https://doi.org/10.1007/978-3-031-47590-0_8

The application compares the Statistical Downscaling Model (SDSM) and partial least square (PLS) to bridge the gap between (minimum and maximum) daily temperatures of 11 sites in Punjab between 1961 and 2013 with atmospheric variables. The data set w... Read More about Statistical Downscaling Modeling for Temperature Prediction.

Multivariate Procedure for Modeling and Prediction of Temperature in Punjab, Pakistan (2024)
Book Chapter
Kanwal, B., Ashraf, Z., Mehmood, T., Kanwal, S., Dashtipour, K., & Gogate, M. (2024). Multivariate Procedure for Modeling and Prediction of Temperature in Punjab, Pakistan. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (99-124). Springer. https://doi.org/10.1007/978-3-031-47590-0_6

Climate study often relies upon global climate models (GCM) to project future scenarios of change in climate behavior. This study aims to refine GCM results to fill the gap between local scale surface weather with regional atmospheric predictors. The... Read More about Multivariate Procedure for Modeling and Prediction of Temperature in Punjab, Pakistan.

Robust Real-time Audio-Visual Speech Enhancement based on DNN and GAN (2024)
Journal Article
Gogate, M., Dashtipour, K., & Hussain, A. (in press). Robust Real-time Audio-Visual Speech Enhancement based on DNN and GAN. IEEE Transactions on Artificial Intelligence, https://doi.org/10.1109/tai.2024.3366141

The human auditory cortex contextually integrates audio-visual (AV) cues to better understand speech in a cocktail party situation. Recent studies have shown that AV speech enhancement (SE) models can significantly improve speech quality and intellig... Read More about Robust Real-time Audio-Visual Speech Enhancement based on DNN and GAN.

5G-IoT Cloud based Demonstration of Real-Time Audio-Visual Speech Enhancement for Multimodal Hearing-aids (2023)
Presentation / Conference Contribution
Gupta, A., Bishnu, A., Gogate, M., Dashtipour, K., Arslan, T., Adeel, A., Hussain, A., Ratnarajah, T., & Sellathurai, M. (2023, August). 5G-IoT Cloud based Demonstration of Real-Time Audio-Visual Speech Enhancement for Multimodal Hearing-aids. Presented at Interspeech 2023, Dublin, Ireland

Over twenty percent of the world's population suffers from some form of hearing loss, making it one of the most significant public health challenges. Current hearing aids commonly amplify noises while failing to improve speech comprehension in crowde... Read More about 5G-IoT Cloud based Demonstration of Real-Time Audio-Visual Speech Enhancement for Multimodal Hearing-aids.

Application for Real-time Audio-Visual Speech Enhancement (2023)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2023, August). Application for Real-time Audio-Visual Speech Enhancement. Presented at Interspeech 2023, Dublin, Ireland

This short paper demonstrates a first of its kind audio-visual (AV) speech enhancement (SE) desktop application that isolates, in real-time, the voice of a target speaker from noisy audio input. The deep neural network model integrated in this applic... Read More about Application for Real-time Audio-Visual Speech Enhancement.

A hybrid dependency-based approach for Urdu sentiment analysis (2023)
Journal Article
Sehar, U., Kanwal, S., Allheeib, N. I., Almari, S., Khan, F., Dashtipur, K., Gogate, M., & Khashan, O. A. (2023). A hybrid dependency-based approach for Urdu sentiment analysis. Scientific Reports, 13, Article 22075. https://doi.org/10.1038/s41598-023-48817-8

In the digital age, social media has emerged as a significant platform, generating a vast amount of raw data daily. This data reflects the opinions of individuals from diverse backgrounds, races, cultures, and age groups, spanning a wide range of top... Read More about A hybrid dependency-based approach for Urdu sentiment analysis.

Fake News in Social Media: Fake News Themes and Intentional Deception in the News and on Social Media (2023)
Book Chapter
Idrees, H., Dashtipour, K., Hussain, T., & Gogate, M. (2024). Fake News in Social Media: Fake News Themes and Intentional Deception in the News and on Social Media. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (219-229). Springer. https://doi.org/10.1007/978-3-031-47590-0_11

From the start of the twenty-first century, online views and clicks have only increased. Within the last twenty years that has embedded through the use of social media. Within the last ten years news can spread on social media before it is shown on t... Read More about Fake News in Social Media: Fake News Themes and Intentional Deception in the News and on Social Media.

Intrusion Detection Systems Using Machine Learning (2023)
Book Chapter
Taylor, W., Hussain, A., Gogate, M., Dashtipour, K., & Ahmad, J. (2024). Intrusion Detection Systems Using Machine Learning. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (75-98). Springer. https://doi.org/10.1007/978-3-031-47590-0_5

Intrusion detection systems (IDS) have developed and evolved over time to form an important component in network security. The aim of an intrusion detection system is to successfully detect intrusions within a network and to trigger alerts to system... Read More about Intrusion Detection Systems Using Machine Learning.

Solving the cocktail party problem using Multi-modal Hearing Assistive Technology Prototype (2023)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2023, December). Solving the cocktail party problem using Multi-modal Hearing Assistive Technology Prototype. Presented at Acoustics 2023, Sydney, Australia

Hearing loss is a major global health problem, affecting over 1.5 billion people. According to estimations by the World Health Organization, 83% of those who could benefit from hearing assistive devices do not use them. The limited adoption of hearin... Read More about Solving the cocktail party problem using Multi-modal Hearing Assistive Technology Prototype.

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.

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.

Audio-visual speech enhancement and separation by utilizing multi-modal self-supervised embeddings (2023)
Presentation / Conference Contribution
Chern, I., Hung, K., Chen, Y., Hussain, T., Gogate, M., Hussain, A., Tsao, Y., & Hou, J. (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.

Audio-visual speech enhancement and separation by leveraging multimodal self-supervised embeddings (2023)
Presentation / Conference Contribution
Chern, I., Hung, K., Chen, Y., Hussain, T., Gogate, M., Hussain, A., Tsao, Y., & Hou, J. (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.

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. (online). Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis. IEEE Transactions on Affective Computing, 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.

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.

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.

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, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

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.

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.