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All Outputs (27)

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., 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.

Comparing the Performance of Different Classifiers for Posture Detection (2022)
Presentation / Conference Contribution
Suresh Kumar, S., Dashtipour, K., Gogate, M., Ahmad, J., Assaleh, K., Arshad, K., Imran, M. A., Abbasi, Q., & Ahmad, W. (2021, October). Comparing the Performance of Different Classifiers for Posture Detection. Presented at 16th EAI International Conference, BODYNETS 2021, Online

Human Posture Classification (HPC) is used in many fields such as human computer interfacing, security surveillance, rehabilitation, remote monitoring, and so on. This paper compares the performance of different classifiers in the detection of 3 post... Read More about Comparing the Performance of Different Classifiers for Posture Detection.

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.

COVID-opt-aiNet: a clinical decision support system for COVID-19 detection (2022)
Journal Article
Kanwal, S., Khan, F., Alamri, S., Dashtipur, K., & Gogate, M. (2022). COVID-opt-aiNet: a clinical decision support system for COVID-19 detection. International Journal of Imaging Systems and Technology, 32(2), 444-461. https://doi.org/10.1002/ima.22695

Coronavirus disease (COVID-19) has had a major and sometimes lethal effect on global public health. COVID-19 detection is a difficult task that necessitates the use of intelligent diagnosis algorithms. Numerous studies have suggested the use of artif... Read More about COVID-opt-aiNet: a clinical decision support system for COVID-19 detection.

A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect (2021)
Journal Article
Guellil, I., Adeel, A., Azouaou, F., Benali, F., Hachani, A., Dashtipour, K., Gogate, M., Ieracitano, C., Kashani, R., & Hussain, A. (2021). A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect. SN Computer Science, 2, Article 118. https://doi.org/10.1007/s42979-021-00510-1

In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach c... Read More about A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect.

ASPIRE - Real noisy audio-visual speech enhancement corpus (2020)
Data
Gogate, M., Dashtipour, K., Adeel, A., & Hussain, A. (2020). ASPIRE - Real noisy audio-visual speech enhancement corpus. [Data]. https://doi.org/10.5281/zenodo.4585619

ASPIRE is a a first of its kind, audiovisual speech corpus recorded in real noisy environment (such as cafe, restaurants) which can be used to support reliable evaluation of multi-modal Speech Filtering technologies. This dataset follows the same sen... Read More about ASPIRE - Real noisy audio-visual speech enhancement corpus.

Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System. (2020)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2020, October). Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System. Presented at Interspeech 2020, Shanghai, China

In this paper, we present VIsual Speech In real nOisy eNvironments (VISION), a first of its kind audio-visual (AV) corpus comprising 2500 utterances from 209 speakers, recorded in real noisy environments including social gatherings, streets, cafeteri... Read More about Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System..

Deep Neural Network Driven Binaural Audio Visual Speech Separation (2020)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., Bell, P., & Hussain, A. (2020, July). Deep Neural Network Driven Binaural Audio Visual Speech Separation. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow

The central auditory pathway exploits the auditory signals and visual information sent by both ears and eyes to segregate speech from multiple competing noise sources and help disambiguate phonological ambiguity. In this study, inspired from this uni... Read More about Deep Neural Network Driven Binaural Audio Visual Speech Separation.

Robust Visual Saliency Optimization Based on Bidirectional Markov Chains (2020)
Journal Article
Jiang, F., Kong, B., Li, J., Dashtipour, K., & Gogate, M. (2021). Robust Visual Saliency Optimization Based on Bidirectional Markov Chains. Cognitive Computation, 13, 69–80. https://doi.org/10.1007/s12559-020-09724-6

Saliency detection aims to automatically highlight the most important area in an image. Traditional saliency detection methods based on absorbing Markov chain only take into account boundary nodes and often lead to incorrect saliency detection when t... Read More about Robust Visual Saliency Optimization Based on Bidirectional Markov Chains.

CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement (2020)
Journal Article
Gogate, M., Dashtipour, K., Adeel, A., & Hussain, A. (2020). CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement. Information Fusion, 63, 273-285. https://doi.org/10.1016/j.inffus.2020.04.001

Noisy situations cause huge problems for the hearing-impaired, as hearing aids often make speech more audible but do not always restore intelligibility. In noisy settings, humans routinely exploit the audio-visual (AV) nature of speech to selectively... Read More about CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement.

Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances (2020)
Presentation / Conference Contribution
Ahmed, R., Dashtipour, K., Gogate, M., Raza, A., Zhang, R., Huang, K., Hawalah, A., Adeel, A., & Hussain, A. (2019, July). Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances. Presented at 10th International Conference, BICS 2019, Guangzhou, China

In pattern recognition, automatic handwriting recognition (AHWR) is an area of research that has developed rapidly in the last few years. It can play a significant role in broad-spectrum of applications rending from, bank cheque processing, applicati... Read More about Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances.

Random Features and Random Neurons for Brain-Inspired Big Data Analytics (2020)
Presentation / Conference Contribution
Gogate, M., Hussain, A., & Huang, K. (2019, November). Random Features and Random Neurons for Brain-Inspired Big Data Analytics. Presented at 2019 International Conference on Data Mining Workshops (ICDMW), Beijing, China

With the explosion of Big Data, fast and frugal reasoning algorithms are increasingly needed to keep up with the size and the pace of user-generated contents on the Web. In many real-time applications, it is preferable to be able to process more data... Read More about Random Features and Random Neurons for Brain-Inspired Big Data Analytics.

Deep Cognitive Neural Network (DCNN) (2019)
Patent
Howard, N., Adeel, A., Gogate, M., & Hussain, A. (2019). Deep Cognitive Neural Network (DCNN). US2019/0156189

Embodiments of the present systems and methods may provide a more efficient and low-powered cognitive computational platform utilizing a deep cognitive neural network (DCNN), incorporating an architecture that integrates convolutional feedforward and... Read More about Deep Cognitive Neural Network (DCNN).

A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA) (2019)
Journal Article
Ozturk, M., Gogate, M., Onireti, O., Adeel, A., Hussain, A., & Imran, M. A. (2019). A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA). Neurocomputing, 358, 479-489. https://doi.org/10.1016/j.neucom.2019.01.031

One of the fundamental goals of mobile networks is to enable uninterrupted access to wireless services without compromising the expected quality of service (QoS). This paper reports a number of significant contributions. First, a novel analytical mod... Read More about A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA).

Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection (2018)
Presentation / Conference Contribution
Ieracitano, C., Adeel, A., Gogate, M., Dashtipour, K., Morabito, F., Larijani, H., …Hussain, A. (2018). Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection. . https://doi.org/10.1007/978-3-030-00563-4_74

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology (ICT) systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially... Read More about Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection.

Exploiting Deep Learning for Persian Sentiment Analysis (2018)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Ieracitano, C., Larijani, H., & Hussain, A. (2018, July). Exploiting Deep Learning for Persian Sentiment Analysis. Presented at 9th International Conference, BICS 2018, Xi'an, China

The rise of social media is enabling people to freely express their opinions about products and services. The aim of sentiment analysis is to automatically determine subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspe... Read More about Exploiting Deep Learning for Persian Sentiment Analysis.

A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management (2018)
Book Chapter
Adeel, A., Gogate, M., Farooq, S., Ieracitano, C., Dashtipour, K., Larijani, H., & Hussain, A. (2019). A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management. In T. S. Durrani, W. Wang, & S. M. Forbes (Eds.), Geological Disaster Monitoring Based on Sensor Networks (57-66). Springer. https://doi.org/10.1007/978-981-13-0992-2_5

Extreme events and disasters resulting from climate change or other ecological factors are difficult to predict and manage. Current limitations of state-of-the-art approaches to disaster prediction and management could be addressed by adopting new un... Read More about A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management.