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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.

Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis (2023)
Journal Article
Rashid, A., Anwary, A. R., Al-Obeidat, F., Brierley, J., Uddin, M., Alkhzaimi, H., Sarpal, A., Toufiq, M., Malik, Z. A., Kadwa, R., Khilnani, P., Guftar Shaikh, M., Benakatti, G., Sharief, J., Ahmed Zaki, S., Zeyada, A., Al-Dubai, A., Hafez, W., & Hussain, A. (2023). Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis. Informatics in Medicine Unlocked, 41, Article 101293. https://doi.org/10.1016/j.imu.2023.101293

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The... Read More about Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis.

Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation (2023)
Journal Article
Wani, M. A., ELAffendi, M., Shakil, K. A., Abuhaimed, I. M., Nayyar, A., Hussain, A., & El-Latif, A. A. A. (2024). Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation. IEEE Transactions on Computational Social Systems, 11(4), 5101 - 5118. https://doi.org/10.1109/tcss.2023.3276764

The emergence of COVID-19 has led to a surge in fake news on social media, with toxic fake news having adverse effects on individuals, society, and governments. Detecting toxic fake news is crucial, but little prior research has been done in this are... Read More about Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation.

PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification (2023)
Journal Article
Yao, K., Huang, K., Sun, J., & Hussain, A. (2023). PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification. IEEE Transactions on Emerging Topics in Computational Intelligence, https://doi.org/10.1109/tetci.2023.3281864

Automatic nuclei segmentation and classification play a vital role in digital pathology. However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or misleading in actual downstream tasks... Read More about PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification.

WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text (2023)
Journal Article
Rashid, J., Kim, J., Hussain, A., & Naseem, U. (2023). WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text. Pattern Recognition Letters, 172, 158-164. https://doi.org/10.1016/j.patrec.2023.06.007

Short texts are a common source of knowledge, and the extraction of such valuable information is beneficial for several purposes. Traditional topic models are incapable of analyzing the internal structural information of topics. They are mostly based... Read More about WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text.

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.neucom.2023.126387

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.

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/s23104828

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/lgrs.2023.3269480

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, 7(4), 818-828. https://doi.org/10.1109/tetci.2022.3228537

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.

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/10.1016/j.engappai.2023.105968

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 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.

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., Elyan, E., & Hussain, A. (2023). Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead. Information Fusion, 93, 227-242. https://doi.org/10.1016/j.inffus.2022.12.026

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.engappai.2022.105708

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.

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/j.engappai.2022.105707

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, 220-241. https://doi.org/10.1016/j.ins.2022.12.021

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/10.1109/tetci.2022.3224954

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.