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A robust deep learning approach for tomato plant leaf disease localization and classification (2022)
Journal Article
Nawaz, M., Nazir, T., Javed, A., Masood, M., Rashid, J., Kim, J., & Hussain, A. (2022). A robust deep learning approach for tomato plant leaf disease localization and classification. Scientific Reports, 12(1), Article 18568. https://doi.org/10.1038/s41598

Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato pla... Read More about A robust deep learning approach for tomato plant leaf disease localization and classification.

A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks (2022)
Journal Article
Yan, S., Zhang, Y., Gao, F., Sun, J., Hussain, A., & Zhou, H. (2022). A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1

Semisupervised learning in synthetic aperture radars (SARs) is one of the research hotspots in the field of radar image automatic target recognition. It can efficiently deal with challenging environments where there are insufficient labeled samples a... Read More about A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks.

A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling (2022)
Journal Article
Comminiello, D., Nezamdoust, A., Scardapane, S., Scarpiniti, M., Hussain, A., & Uncini, A. (2023). A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling. IEEE Transactions on Systems, Man and Cybernetics: Systems, 53(3), 1384-1396. https

Nonlinear models are known to provide excellent performance in real-world applications that often operate in nonideal conditions. However, such applications often require online processing to be performed with limited computational resources. To addr... Read More about A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling.

DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm (2022)
Journal Article
Aboud, A., Rokbani, N., Fdhila, R., Qahtani, A. M., Almutiry, O., Dhahri, H., …Alimi, A. M. (2022). DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm. Applied Soft Computing, 129, Article 109622. https://doi.org/

Particle Swarm Optimization (PSO) system based on the distributed architecture over multiple sub-swarms is very efficient for static multi-objective optimization but has not been considered for solving dynamic multi-objective problems (DMOPs). Tracki... Read More about DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm.

A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion (2022)
Journal Article
Gao, F., Xu, J., Lang, R., Wang, J., Hussain, A., & Zhou, H. (2022). A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion. Remote Sensing, 14(18), Article 4583. https://doi.org/10.3390/rs14184583

Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usually encounter the problem of poor feature representation ability due to insuf... Read More about A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion.

Multimodal audio-visual information fusion using canonical-correlated Graph Neural Network for energy-efficient speech enhancement (2022)
Journal Article
Passos, L. A., Papa, J. P., Del Ser, J., Hussain, A., & Adeel, A. (2023). Multimodal audio-visual information fusion using canonical-correlated Graph Neural Network for energy-efficient speech enhancement. Information Fusion, 90, 1-11. https://doi.org/10.

This paper proposes a novel multimodal self-supervised architecture for energy-efficient audio-visual (AV) speech enhancement that integrates Graph Neural Networks with canonical correlation analysis (CCA-GNN). The proposed approach lays its foundati... Read More about Multimodal audio-visual information fusion using canonical-correlated Graph Neural Network for energy-efficient speech enhancement.

Pushing the limits of remote RF sensing by reading lips under the face mask (2022)
Journal Article
Hameed, H., Usman, M., Tahir, A., Hussain, A., Abbas, H., Cui, T. J., …Abbasi, Q. H. (2022). Pushing the limits of remote RF sensing by reading lips under the face mask. Nature Communications, 13(1), Article 5168. https://doi.org/10.1038/s41467-022-3223

The problem of Lip-reading has become an important research challenge in recent years. The goal is to recognise speech from lip movements. Most of the Lip-reading technologies developed so far are camera-based, which require video recording of the ta... Read More about Pushing the limits of remote RF sensing by reading lips under the face mask.

A Mixed Approach for Aggressive Political Discourse Analysis on Twitter (2022)
Journal Article
Torregrosa, J., D’Antonio-Maceiras, S., Villar-Rodríguez, G., Hussain, A., Cambria, E., & Camacho, D. (2023). A Mixed Approach for Aggressive Political Discourse Analysis on Twitter. Cognitive Computation, 15, 440-465. https://doi.org/10.1007/s12559-02

Political tensions have grown throughout Europe since the beginning of the new century. The consecutive crises led to the rise of different social movements in several countries, in which the political status quo changed. These changes included an in... Read More about A Mixed Approach for Aggressive Political Discourse Analysis on Twitter.

Next Generation Cognition-Aware Hearing Aid Devices With Microwave Sensors: Opportunities and Challenges (2022)
Journal Article
Anwar, U., Arslan, T., Hussain, A., & Lomax, P. (2022). Next Generation Cognition-Aware Hearing Aid Devices With Microwave Sensors: Opportunities and Challenges. IEEE Access, 10, 82214-82235. https://doi.org/10.1109/access.2022.3195875

The strong association between hearing loss and cognitive decline has developed into a major health challenge that calls for early detection, diagnosis and prevention. Hearing loss usually results in severe health implications that include loss of mo... Read More about Next Generation Cognition-Aware Hearing Aid Devices With Microwave Sensors: Opportunities and Challenges.

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.

A novel multiple kernel fuzzy topic modeling technique for biomedical data (2022)
Journal Article
Rashid, J., Kim, J., Hussain, A., Naseem, U., & Juneja, S. (2022). A novel multiple kernel fuzzy topic modeling technique for biomedical data. BMC Bioinformatics, 23(1), Article 275. https://doi.org/10.1186/s12859-022-04780-1

Background: Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used t... Read More about A novel multiple kernel fuzzy topic modeling technique for biomedical data.

Novel single and multi-layer echo-state recurrent autoencoders for representation learning (2022)
Journal Article
Chouikhi, N., Ammar, B., Hussain, A., & Alimi, A. M. (2022). Novel single and multi-layer echo-state recurrent autoencoders for representation learning. Engineering Applications of Artificial Intelligence, 114, Article 105051. https://doi.org/10.1016/j.en

Representation learning impacts the performance of Machine Learning (ML) models. Feature extraction-based methods such as Auto-Encoders (AEs) are used to find new, more accurate data representations from original ones. They perform efficiently on a s... Read More about Novel single and multi-layer echo-state recurrent autoencoders for representation learning.

Educational data mining to predict students' academic performance: A survey study (2022)
Journal Article
Batool, S., Rashid, J., Nisar, M. W., Kim, J., Kwon, H., & Hussain, A. (2023). Educational data mining to predict students' academic performance: A survey study. Education and Information Technologies, 28(1), 905-971. https://doi.org/10.1007/s10639-022-11

Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various da... Read More about Educational data mining to predict students' academic performance: A survey study.

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation (2022)
Journal Article
Rashid, J., Kanwal, S., Wasif Nisar, M., Kim, J., & Hussain, A. (2023). An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation. Computer Systems Science and Engineering, 44(2), 1309-1324. https://doi.org/10.32604/css

In project management, effective cost estimation is one of the most crucial activities to efficiently manage resources by predicting the required cost to fulfill a given task. However, finding the best estimation results in software development is ch... Read More about An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation.

Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition (2022)
Journal Article
Xu, H., Jin, X., Wang, Q., Hussain, A., & Huang, K. (2022). Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition. ACM transactions on multimedia computing communications and applications, 18(2S), Article 119. https://doi.or

Currently, many action recognition methods mostly consider the information from spatial streams. We propose a new perspective inspired by the human visual system to combine both spatial and temporal streams to measure their attention consistency. Spe... Read More about Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition.

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

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.

Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions (2022)
Journal Article
Zhou, Y., Huang, K., Cheng, C., Wang, X., Hussain, A., & Liu, X. (2023). Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(2), 565

The training process for deep learning and pattern recognition normally involves the use of convex and strongly convex optimization algorithms such as AdaBelief and SAdam to handle lots of “uninformative” samples that should be ignored, thus incurrin... Read More about Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions.

RoadSeg-CD: A Network With Connectivity Array and Direction Map for Road Extraction From SAR Images (2022)
Journal Article
Gao, F., Tu, J., Wang, J., Hussain, A., & Zhou, H. (2022). RoadSeg-CD: A Network With Connectivity Array and Direction Map for Road Extraction From SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 3992-4003

Road extraction from synthetic aperture radar (SAR) images has attracted much attention in the field of remote sensing image processing. General road extraction algorithms, affected by shadows of buildings and trees, are prone to producing fragmented... Read More about RoadSeg-CD: A Network With Connectivity Array and Direction Map for Road Extraction From SAR Images.

Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications (2022)
Journal Article
Hammad, M., Abd El-Latif, A. A., Hussain, A., Abd El-Samie, F. E., Gupta, B. B., Ugail, H., & Sedik, A. (2022). Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications. Computers and Electrical Engineering, 100, Article 108011. https:

In this paper, novel convolutional neural network (CNN) and convolutional long short-term (ConvLSTM) deep learning models (DLMs) are presented for automatic detection of arrhythmia for IoT applications. The input ECG signals are represented in 2D for... Read More about Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications.

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 Intelligen

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.