All Outputs (478)

A real‐time lane detection network using two‐directional separation attention (2023)
Journal Article
Zhang, L., Jiang, F., Yang, J., Kong, B., & Hussain, A. (2023). A real‐time lane detection network using two‐directional separation attention. Computer-Aided Civil and Infrastructure Engineering, https://doi.org/10.1111/mice.13051

Real-time network by adopting attention mechanism is helpful for enhancing lane detection capability of autonomous vehicles. This paper proposes a real-time lane detection network (TSA-LNet) that incorporates a lightweight network (LNet) and a two-di... Read More about A real‐time lane detection network using two‐directional separation attention.

Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG (2023)
Journal Article
Shah, J., Chougule, A., Chamola, V., & Hussain, A. (2023). Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG. Neurocomputing, 549, Article 126387. https://doi.org/10.1016/j.neuco

The growing demand for semi-autonomous human–machine systems has led to an increased requirement for human fatigue detection. Direct and invasive approaches for microsleep detection include cognitive computing methods using Brain-Computer Interfaces... Read More about Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG.

A novel multimodal online news popularity prediction model based on ensemble learning (2023)
Journal Article
Arora, A., Hassija, V., Bansal, S., Yadav, S., Chamola, V., & Hussain, A. (2023). A novel multimodal online news popularity prediction model based on ensemble learning. Expert Systems, 40(8), Article e13336. https://doi.org/10.1111/exsy.13336

The prediction of news popularity is having substantial importance for the digital advertisement community in terms of selecting and engaging users. Traditional approaches are based on empirical data collected through surveys and applied statistical... Read More about A novel multimodal online news popularity prediction model based on ensemble learning.

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.

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., …Ratnarajah, T. (2023). Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids. In IEEE ISCAS 2023 Symposium Proceedings. https://doi.org/10.1

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.

A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction (2023)
Journal Article
Huang, H., Zhao, B., Gao, F., Chen, P., Wang, J., & Hussain, A. (2023). A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction. Sensors, 23(10), Article 4828. https://doi.org/10.3390/s231048

Reconstruction-based and prediction-based approaches are widely used for video anomaly detection (VAD) in smart city surveillance applications. However, neither of these approaches can effectively utilize the rich contextual information that exists i... Read More about A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction.

Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning (2023)
Journal Article
Basabain, S., Cambria, E., Alomar, K., & Hussain, A. (2023). Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning. Expert Systems, 40(8), Article e13329. https://doi.org/10.1111/exsy.13329

A growing amount of research use pre-trained language models to address few/zero-shot text classification problems. Most of these studies neglect the semantic information hidden implicitly beneath the natural language names of class labels and develo... Read More about Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning.

An Incremental SAR Target Recognition Framework via Memory-Augmented Weight Alignment and Enhancement Discrimination (2023)
Journal Article
Huang, H., Gao, F., Wang, J., Hussain, A., & Zhou, H. (2023). An Incremental SAR Target Recognition Framework via Memory-Augmented Weight Alignment and Enhancement Discrimination. IEEE Geoscience and Remote Sensing Letters, 20, https://doi.org/10.1109/lgr

Synthetic aperture radar automatic target recognition (SAR ATR) is one of the most important research directions in SAR image interpretation. While much existing research into SAR ATR has focused on deep learning technology, an equally important yet... Read More about An Incremental SAR Target Recognition Framework via Memory-Augmented Weight Alignment and Enhancement Discrimination.

Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets (2023)
Journal Article
Adeel, A., Adetomi, A., Ahmed, K., Hussain, A., Arslan, T., & Phillips, W. A. (2023). Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets. IEEE Transactions on Emerging Topics in Computational Intelligence,

Context-sensitive two-point layer 5 pyramidal cells (L5PCs) were discovered as long ago as 1999. However, the potential of this discovery to provide useful neural computation has yet to be demonstrated. Here we show for the first time how a transform... Read More about Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets.

Editorial: Physiological computing of social cognition, volume II (2023)
Journal Article
Fernández-Caballero, A., Hussain, A., Latorre, J. M., Martínez-Rodrigo, A., Rodriguez-Jimenez, R., & Fernández-Sotos, P. (2023). Editorial: Physiological computing of social cognition, volume II. Frontiers in Human Neuroscience, 17, Article 1152291. ht

[Abstract unavailable.]

Randomized block-coordinate adaptive algorithms for nonconvex optimization problems (2023)
Journal Article
Zhou, Y., Huang, K., Li, J., Cheng, C., Wang, X., Hussian, A., & Liu, X. (2023). Randomized block-coordinate adaptive algorithms for nonconvex optimization problems. Engineering Applications of Artificial Intelligence, 121, Article 105968. https://doi.org

Nonconvex optimization problems have always been one focus in deep learning, in which many fast adaptive algorithms based on momentum are applied. However, the full gradient computation of high-dimensional feature vector in the above tasks become pro... Read More about Randomized block-coordinate adaptive algorithms for nonconvex optimization problems.

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

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), Doh

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.

Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead (2023)
Journal Article
Zhang, K., Zhang, F., Wan, W., Yu, H., Sun, J., Del Ser, J., …Hussain, A. (2023). Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead.

Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks c... Read More about Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead.

Diverse features discovery transformer for pedestrian attribute recognition (2022)
Journal Article
Zheng, A., Wang, H., Wang, J., Huang, H., He, R., & Hussain, A. (2023). Diverse features discovery transformer for pedestrian attribute recognition. Engineering Applications of Artificial Intelligence, 119, Article 105708. https://doi.org/10.1016/j.engapp

Recently, Swin Transformer has been widely explored as a general backbone for computer vision, which helps to improve the performance of vision tasks due to the ability to establish associations for long-range dependencies of different spatial locati... Read More about Diverse features discovery transformer for pedestrian attribute recognition.

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 Intern

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.

Multimodal salient object detection via adversarial learning with collaborative generator (2022)
Journal Article
Tu, Z., Yang, W., Wang, K., Hussain, A., Luo, B., & Li, C. (2023). Multimodal salient object detection via adversarial learning with collaborative generator. Engineering Applications of Artificial Intelligence, 119, Article 105707. https://doi.org/10.1016

Multimodal salient object detection(MSOD), which utilizes multimodal information (e.g., RGB image and thermal infrared or depth image) to detect common salient objects, has received much attention recently. Different modalities reflect different appe... Read More about Multimodal salient object detection via adversarial learning with collaborative generator.

A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator (2022)
Journal Article
Kouka, N., BenSaid, F., Fdhila, R., Fourati, R., Hussain, A., & Alimi, A. M. (2023). A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator. Information Sciences, 623, 22

Most evolutionary algorithms, including particle swarm optimization (PSO), use Pareto dominance as a major selection criterion and face significant challenges when dealing with many-objective problems. To tackle this issue, this paper proposes a nove... Read More about A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator.

Towards Simple and Accurate Human Pose Estimation With Stair Network (2022)
Journal Article
Jiang, C., Huang, K., Zhang, S., Wang, X., Xiao, J., Niu, Z., & Hussain, A. (2023). Towards Simple and Accurate Human Pose Estimation With Stair Network. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(3), 805-817. https://doi.org/10

In this paper, we focus on tackling the precise keypoint coordinates regression task. Most existing approaches adopt complicated networks with a large number of parameters, leading to a heavy model with poor cost-effectiveness in practice. To overcom... Read More about Towards Simple and Accurate Human Pose Estimation With Stair Network.

Canonical cortical graph neural networks and its application for speech enhancement in audio-visual hearing aids (2022)
Journal Article
Passos, L. A., Papa, J. P., Hussain, A., & Adeel, A. (2023). Canonical cortical graph neural networks and its application for speech enhancement in audio-visual hearing aids. Neurocomputing, 527, 196-203. https://doi.org/10.1016/j.neucom.2022.11.081

Despite the recent success of machine learning algorithms, most models face drawbacks when considering more complex tasks requiring interaction between different sources, such as multimodal input data and logical time sequences. On the other hand, th... Read More about Canonical cortical graph neural networks and its application for speech enhancement in audio-visual hearing aids.