Outputs (400)

A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments (2020)
Journal Article
Al-Ghadir, A. I., Azmi, A. M., & Hussain, A. (2021). A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments. Information Fusion, 67, 29-40. https://doi.org/10.1016/j.inffus.2020.10.003

Stance detection is a relatively new concept in data mining that aims to assign a stance label (favor, against, or none) to a social media post towards a specific pre-determined target. These targets may not be referred to in the post, and may not be... Read More about A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments.

A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19 (2020)
Journal Article
Ren, J., Yan, Y., Zhao, H., Ma, P., Zabalza, J., Hussain, Z., Luo, S., Dai, Q., Zhao, S., Sheikh, A., Hussain, A., & Li, H. (2020). A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19. IEEE Journal of Biomedical and Health Informatics, 24(12), 3551-3563. https://doi.org/10.1109/JBHI.2020.3027987

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective mana... Read More about A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19.

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.

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications (2020)
Journal Article
Ju, Z., Gun, L., Hussain, A., Mahmud, M., & Ieracitano, C. (2020). A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications. Applied Sciences, 10(19), Article 6761. https://doi.org/10.3390/app10196761

In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specif... Read More about A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications.

A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers (2020)
Journal Article
Ieracitano, C., Paviglianiti, A., Campolo, M., Hussain, A., Pasero, E., & Carlo Morabito, F. (2021). A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers. IEEE/CAA Journal of Automatica Sinica, 8(1), 64-76. https://doi.org/10.1109/JAS.2020.1003387

The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope ( SEM ) images of the electrospun nanofiber, to ensure that no structural defects are produced. The... Read More about A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers.

Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding (2020)
Journal Article
Chen, R., Yu, Y., Chen, J., Zhong, Y., Zhao, H., Hussain, A., & Tan, H. (2020). Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding. Sensors, 20(17), Article 4926. https://doi.org/10.3390/s20174926

With the development of commodity economy, the emergence of fake and shoddy products has seriously harmed the interests of consumers and enterprises. To tackle this challenge, customized 2D barcode is proposed to satisfy the requirements of the enter... Read More about Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding.

A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System (2020)
Journal Article
Farah, L., Hussain, A., Kerrouche, A., Ieracitano, C., Ahmad, J., & Mahmud, M. (2020). A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System. IEEE Access, 8, 163225-163237. https://doi.org/10.1109/access.2020.3016981

Most conventional Fuzzy Logic Controller ( FLC ) rules are based on the knowledge and experience of expert operators: given a specific input, FLCs produce the same output. However, FLCs do not perform very well when dealing with complex problems that... Read More about A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System.

Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images (2020)
Journal Article
Gao, F., He, Y., Wang, J., Hussain, A., & Zhou, H. (2020). Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images. Remote Sensing, 12(16), Article 2619. https://doi.org/10.3390/rs12162619

In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it is urgent to develop methods with higher accuracy and faster speed for ship detection in high-resolution SAR images. Among all kinds of methods, deep-learn... Read More about Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images.

A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation (2020)
Journal Article
Yue, Z., Gao, F., Xiong, Q., Wang, J., Hussain, A., & Zhou, H. (2020). A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4585-4598. https://doi.org/10.1109/jstars.2020.3016064

As an important step of synthetic aperture radar image interpretation, synthetic aperture radar image segmentation aims at segmenting an image into different regions in terms of homogeneity. Because of the deficiency of the labeled samples and the ex... Read More about A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation.

Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning (2020)
Journal Article
Xiong, F., Liu, Z., Huang, K., Yang, X., Qiao, H., & Hussain, A. (2020). Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning. Neural Networks, 129, 163-173. https://doi.org/10.1016/j.neunet.2020.06.003

Continual learning, a widespread ability in people and animals, aims to learn and acquire new knowledge and skills continuously. Catastrophic forgetting usually occurs in continual learning when an agent attempts to learn different tasks sequentially... Read More about Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning.