All Outputs (75)

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, May). Live Demonstration: Real-time Multi-modal Hearing Assistive Technology Prototype. Presented at 2023 IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, California

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.

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., Hussain, A., Sellathurai, M., & Ratnarajah, T. (2023, May). Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids. Presented at 2023 IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, California

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.

The P vs. NP Problem and Attempts to Settle It via Perfect Graphs State-of-the-Art Approach (2023)
Presentation / Conference Contribution
Heal, M., Dashtipour, K., & Gogate, M. (2023, March). The P vs. NP Problem and Attempts to Settle It via Perfect Graphs State-of-the-Art Approach. Presented at 2023 Future of Information and Communication Conference (FICC), San Francisco, CA

The P vs. NP problem is a major problem in computer science. It is perhaps the most celebrated outstanding problem in that domain. Its solution would have a tremendous impact on different fields such as mathematics, cryptography, algorithm research,... Read More about The P vs. NP Problem and Attempts to Settle It via Perfect Graphs State-of-the-Art Approach.

Interactive Effect of Learning Rate and Batch Size to Implement Transfer Learning for Brain Tumor Classification (2023)
Journal Article
Usmani, I. A., Qadri, M. T., Zia, R., Alrayes, F. S., Saidani, O., & Dashtipour, K. (2023). Interactive Effect of Learning Rate and Batch Size to Implement Transfer Learning for Brain Tumor Classification. Electronics, 12(4), Article 964. https://doi.org/10.3390/electronics12040964

For classifying brain tumors with small datasets, the knowledge-based transfer learning (KBTL) approach has performed very well in attaining an optimized classification model. However, its successful implementation is typically affected by different... Read More about Interactive Effect of Learning Rate and Batch Size to Implement Transfer Learning for Brain Tumor Classification.

A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting (2023)
Journal Article
Varone, G., Ieracitano, C., Çiftçioğlu, A. Ö., Hussain, T., Gogate, M., Dashtipour, K., Al-Tamimi, B. N., Almoamari, H., Akkurt, I., & Hussain, A. (2023). A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting. Entropy, 25(2), Article 253. https://doi.org/10.3390/e25020253

The development of reinforced polymer composite materials has had a significant influence on the challenging problem of shielding against high-energy photons, particularly X-rays and γ-rays in industrial and healthcare facilities. Heavy materials’ sh... Read More about A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting.

AVSE Challenge: Audio-Visual Speech Enhancement Challenge (2023)
Presentation / Conference Contribution
Aldana Blanco, A. L., Valentini-Botinhao, C., Klejch, O., Gogate, M., Dashtipour, K., Hussain, A., & Bell, P. (2023, January). AVSE Challenge: Audio-Visual Speech Enhancement Challenge. Presented at 2022 IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar

Audio-visual speech enhancement is the task of improving the quality of a speech signal when video of the speaker is available. It opens-up the opportunity of improving speech intelligibility in adverse listening scenarios that are currently too chal... Read More about AVSE Challenge: Audio-Visual Speech Enhancement Challenge.

Formulations and Algorithms to Find Maximal and Maximum Independent Sets of Graphs (2022)
Presentation / Conference Contribution
Heal, M., Dashtipour, K., & Gogate, M. (2022, December). Formulations and Algorithms to Find Maximal and Maximum Independent Sets of Graphs. Presented at 2022 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, Nevada

We propose four algorithms to find maximal and maximum independent sets of graphs. Two of the algorithms are non-polynomial in time, mainly binary programming and non-convex multi-variable polynomial programming algorithms. Two other algorithms run i... Read More about Formulations and Algorithms to Find Maximal and Maximum Independent Sets of Graphs.

A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids (2022)
Presentation / Conference Contribution
Bishnu, A., Gupta, A., Gogate, M., Dashtipour, K., Adeel, A., Hussain, A., Sellathurai, M., & Ratnarajah, T. (2022, October). A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids. Presented at 2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom), Genoa, Italy

In this paper, we design a first of its kind transceiver (PHY layer) prototype for cloud-based audio-visual (AV) speech enhancement (SE) complying with high data rate and low latency requirements of future multimodal hearing assistive technology. The... Read More about A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids.

Towards real-time privacy-preserving audio-visual speech enhancement (2022)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2022, September). Towards real-time privacy-preserving audio-visual speech enhancement. Presented at 2nd Symposium on Security and Privacy in Speech Communication, Incheon, Korea

Human auditory cortex in everyday noisy situations is known to exploit aural and visual cues that are contextually combined by the brain’s multi-level integration strategies to selectively suppress the background noise and focus on the target speaker... Read More about Towards real-time privacy-preserving audio-visual speech enhancement.

A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning (2022)
Presentation / Conference Contribution
Hussain, T., Diyan, M., Gogate, M., Dashtipour, K., Adeel, A., Tsao, Y., & Hussain, A. (2022, July). A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning. Presented at 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland

Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signals. Despite improving the speech quality, su... Read More about A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning.

DNet-CNet: A novel cascaded deep network for real-time lane detection and classification (2022)
Journal Article
Zhang, L., Jiang, F., Yang, J., Kong, B., Hussain, A., Gogate, M., & Dashtipour, K. (2023). DNet-CNet: A novel cascaded deep network for real-time lane detection and classification. Journal of Ambient Intelligence and Humanized Computing, 14, 10745-10760. https://doi.org/10.1007/s12652-022-04346-2

Robust understanding of the lane position and type is essential for changing lanes in autonomous vehicles. However, accomplishing this task in real time with high level of precision is not trivial. In this paper, we propose a novel cascaded deep neur... Read More about DNet-CNet: A novel cascaded deep network for real-time lane detection and classification.

Arabic sentiment analysis using dependency-based rules and deep neural networks (2022)
Journal Article
Diwali, A., Dashtipour, K., Saeedi, K., Gogate, M., Cambria, E., & Hussain, A. (2022). Arabic sentiment analysis using dependency-based rules and deep neural networks. Applied Soft Computing, 127, Article 109377. https://doi.org/10.1016/j.asoc.2022.109377

With the growth of social platforms in recent years and the rapid increase in the means of communication through these platforms, a significant amount of textual data is available that contains an abundance of individuals’ opinions. Sentiment analysi... Read More about Arabic sentiment analysis using dependency-based rules and deep neural networks.

Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study (2022)
Journal Article
Hussain, Z., Sheikh, Z., Tahir, A., Dashtipour, K., Gogate, M., Sheikh, A., & Hussain, A. (2022). Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study. JMIR Public Health and Surveillance, 8(5), Article e32543. https://doi.org/10.2196/32543

Background:
The roll-out of vaccines for SARS-CoV-2 in the United Kingdom, started in December 2020. Uptake has been high, and there has been a subsequent reduction in infections, hospitalisations and deaths in vaccinated individuals. However, vacci... Read More about Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study.

A novel temporal attentive-pooling based convolutional recurrent architecture for acoustic signal enhancement (2022)
Journal Article
Hussain, T., Wang, W.-C., Gogate, M., Dashtipour, K., Tsao, Y., Lu, X., Ahsan, A., & Hussain, A. (2022). A novel temporal attentive-pooling based convolutional recurrent architecture for acoustic signal enhancement. IEEE Transactions on Artificial Intelligence, 3(5), 833-842. https://doi.org/10.1109/TAI.2022.3169995

Removing background noise from acoustic observations to obtain clean signals is an important research topic regarding numerous real acoustic applications. Owing to their strong model capacity in function mapping, deep neural network-based algorithms... Read More about A novel temporal attentive-pooling based convolutional recurrent architecture for acoustic signal enhancement.

Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform (2022)
Journal Article
Abd, M. H., Al-Suhail, G. A., Tahir, F. R., Ali Ali, A. M., Abbood, H. A., Dashtipour, K., Jamal, S. S., & Ahmad, J. (2022). Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform. Remote Sensing, 14(9), Article 1984. https://doi.org/10.3390/rs14091984

There is no doubt that chaotic systems are still attractive issues in various radar applications and communication systems. In this paper, we present a new 0.3 GHz mono-static microwave chaotic radar. It includes a chaotic system based on a time-dela... Read More about Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform.

Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing (2022)
Journal Article
Liaqat, S., Dashtipour, K., Rizwan, A., Usman, M., Shah, S. A., Arshad, K., Assaleh, K., & Ramzan, N. (2022). Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing. Scientific Reports, 12(1), Article 3715. https://doi.org/10.1038/s41598-022-07754-8

Personalized hydration level monitoring play vital role in sports, health, wellbeing and safety of a person while performing particular set of activities. Clinical staff must be mindful of numerous physiological symptoms that identify the optimum hyd... Read More about Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing.

Detecting Alzheimer’s Disease Using Machine Learning Methods (2022)
Presentation / Conference Contribution
Dashtipour, K., Taylor, W., Ansari, S., Zahid, A., Gogate, M., Ahmad, J., Assaleh, K., Arshad, K., Ali Imran, M., & Abbasi, Q. (2021, October). Detecting Alzheimer’s Disease Using Machine Learning Methods. Presented at 16th EAI International Conference, BODYNETS 2021, Online

As the world is experiencing population growth, the portion of the older people, aged 65 and above, is also growing at a faster rate. As a result, the dementia with Alzheimer’s disease is expected to increase rapidly in the next few years. Currently,... Read More about Detecting Alzheimer’s Disease Using Machine Learning Methods.