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All Outputs (316)

ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption (2024)
Journal Article
Jiang, D., Tsafack, N., Boulila, W., Ahmad, J., & Barba-Franco, J. (in press). ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption. Expert Systems with Applications, 236, Article 121378. https://doi.org/10.1016/j.eswa.2023.121378

Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ priv... Read More about ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption.

The retailer perspective on the potential for using urban consolidation centres (UCCs) (2024)
Journal Article
Akgün, E. Z., Monios, J., Cowie, J., & Fonzone, A. (2024). The retailer perspective on the potential for using urban consolidation centres (UCCs). Research in Transportation Economics, 103, Article 101413. https://doi.org/10.1016/j.retrec.2024.101413

This paper examines the role of supportive policies and value-added services which may incentivise retailers to use an urban consolidation centre (UCC). The methodology is a case study of the city of Edinburgh, Scotland, based on semi-structured inte... Read More about The retailer perspective on the potential for using urban consolidation centres (UCCs).

Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System (2024)
Presentation / Conference Contribution
Gupta, A., Bishnu, A., Ratnarajah, T., Adeel, A., Hussain, A., & Sellathurai, M. (2023, December). Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System. Presented at GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia

Designing an efficient receiver for multiple users transmitting orthogonal frequency-division multiplexing signals to the base station remain a challenging interference-limited problem in 5G-new radio (5G-NR) system. This can lead to stagnation of de... Read More about Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System.

A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing (2024)
Presentation / Conference Contribution
Benzaïd, C., Taleb, T., Sami, A., & Hireche, O. (2023, December). A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing. Presented at GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia

Network slicing is recognized as a key enabler for 5G and beyond (B5G) services. However, its dynamic nature and the growing sophistication of DDoS attacks put it at risk of Economical Denial of Sustainability (EDoS) attack, causing economic losses t... Read More about A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing.

Hate speech detection: A comprehensive review of recent works (2024)
Journal Article
Gandhi, A., Ahir, P., Adhvaryu, K., Shah, P., Lohiya, R., Cambria, E., Poria, S., & Hussain, A. (2024). Hate speech detection: A comprehensive review of recent works. Expert Systems, 41(8), Article e13562. https://doi.org/10.1111/exsy.13562

There has been surge in the usage of Internet as well as social media platforms which has led to rise in online hate speech targeted on individual or group. In the recent years, hate speech has resulted in one of the challenging problems that can unf... Read More about Hate speech detection: A comprehensive review of recent works.

Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions (2024)
Journal Article
Liu, J., Zhang, Y., Zhou, Y., & Chen, J. (2024). Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions. Mathematics, 12(5), Article 667. https://doi.org/10.3390/math12050667

This study presents a novel event-triggered relearning framework for neural network modeling, designed to improve prediction precision in dynamic stochastic complex industrial systems under non-stationary and variable conditions. Firstly, a sliding w... Read More about Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions.

Solar Wall Technology and Its Impact on Building Performance (2024)
Journal Article
Ghamari, M., & Sundaram, S. (2024). Solar Wall Technology and Its Impact on Building Performance. Energies, 17(5), Article 1075. https://doi.org/10.3390/en17051075

Solar walls provide transformative solutions by harnessing solar energy to generate electricity, improve thermal comfort, and reduce energy consumption and emissions, contributing to zero-energy buildings and mitigating climate change. In hot and hum... Read More about Solar Wall Technology and Its Impact on Building Performance.

Toward a Real-Time TCP SYN Flood DDoS Mitigation Using Adaptive Neuro-Fuzzy Classifier and SDN Assistance in Fog Computing (2024)
Journal Article
Bensaid, R., Labraoui, N., Abba Ari, A. A., Maglaras, L., Saidi, H., Abdu Lwahhab, A. M., & Benfriha, S. (2024). Toward a Real-Time TCP SYN Flood DDoS Mitigation Using Adaptive Neuro-Fuzzy Classifier and SDN Assistance in Fog Computing. Security and Communication Networks, 2024, Article 6651584. https://doi.org/10.1155/2024/6651584

The growth of the Internet of Things (IoT) has recently impacted our daily lives in many ways. As a result, a massive volume of data are generated and need to be processed in a short period of time. Therefore, a combination of computing models such a... Read More about Toward a Real-Time TCP SYN Flood DDoS Mitigation Using Adaptive Neuro-Fuzzy Classifier and SDN Assistance in Fog Computing.

BBox-Free SAR Ship Instance Segmentation Method Based on Gaussian Heatmap (2024)
Journal Article
Gao, F., Zhong, F., Sun, J., Hussain, A., & Zhou, H. (2024). BBox-Free SAR Ship Instance Segmentation Method Based on Gaussian Heatmap. IEEE Transactions on Geoscience and Remote Sensing, 62, Article 5206218. https://doi.org/10.1109/tgrs.2024.3369614

Recently, deep learning methods have been widely adopted for ship detection in synthetic aperture radar (SAR) images. However, many of the existing methods miss adjacent ship instances when detecting densely arranged ship targets in inshore scenes. B... Read More about BBox-Free SAR Ship Instance Segmentation Method Based on Gaussian Heatmap.

A QOBL-SAO and its variant: An open source software for optimizing PV/wind/battery system and CEC2020 real world problems (2024)
Journal Article
Mas’ud, A. A., Salawudeen, A. T., Umar, A. A., Shaaban, Y. A., Muhammad-Sukki, F., Musa, U., & Alshammari, S. J. (2024). A QOBL-SAO and its variant: An open source software for optimizing PV/wind/battery system and CEC2020 real world problems. Software Impacts, 19, Article 100630. https://doi.org/10.1016/j.simpa.2024.100630

The Quasi oppositional smell agent optimization (QOBL-SAO) and its levy flight variant (LFQOBL-SAO) are two cutting-edge software tools for optimizing PV/wind/battery power systems. They can also be used to solve real-world CEC2020 optimization probl... Read More about A QOBL-SAO and its variant: An open source software for optimizing PV/wind/battery system and CEC2020 real world problems.

Exploring the Use of Rock Flour for Sustainable Peat Stabilisation (2024)
Presentation / Conference Contribution
Bernal-Sanchez, J., Coll, J., Leak, J., & Barreto, D. (2024, February). Exploring the Use of Rock Flour for Sustainable Peat Stabilisation. Presented at Geo-Congress 2024, Vancouver, British Columbia, Canada

This paper aims to investigate the mechanical behaviour of peat stabilised with glacial rock flour for foundation construction. Peat, a natural organic soil, presents challenges for construction due to its high compressibility and low bearing capacit... Read More about Exploring the Use of Rock Flour for Sustainable Peat Stabilisation.

A Grading Entropy Review of PSD-Based Frost Susceptibility Criteria (2024)
Presentation / Conference Contribution
Leak, J., Barreto, D., Wright, C., Bernal Sanchez, J., & Imre, E. (2024, February). A Grading Entropy Review of PSD-Based Frost Susceptibility Criteria. Presented at Geo-Congress 2024, Vancouver, British Columbia

Particle size distribution (PSD) is recognised among geotechnical engineers as an informative soil descriptor, and often used to predict geomechanical behaviours. However, the effectiveness of PSD to characterise frost action is debatable. Existing c... Read More about A Grading Entropy Review of PSD-Based Frost Susceptibility Criteria.

Performance Analysis of Multiport Antennas in Vehicle-to-Vehicle Communication Channels (2024)
Journal Article
Pour Sohrab, A., Huang, Y., & Karadimas, P. (2024). Performance Analysis of Multiport Antennas in Vehicle-to-Vehicle Communication Channels. Wireless Personal Communications, 134, 1231–1257. https://doi.org/10.1007/s11277-024-10875-0

A holistic performance analysis and classification of multiport antennas (MPAs) is conducted in this paper. We focus on 5.9 GHz vehicle-to-vehicle communications suited to the emerging technology of intelligent transportation systems. Three-dimension... Read More about Performance Analysis of Multiport Antennas in Vehicle-to-Vehicle Communication Channels.

Temporal analysis and predictive modeling of ambient air quality in Hulu Langat District, Selangor, Malaysia: A chemometric approach (2024)
Journal Article
Abdullah, A., Mohd Saudi, A. S., Shafii, N. Z., Kamarudin, M. K. A., & Muhammad-Sukki, F. (2024). Temporal analysis and predictive modeling of ambient air quality in Hulu Langat District, Selangor, Malaysia: A chemometric approach. Planning Malaysia Journal, 22(1), https://doi.org/10.21837/pm.v22i30.1448

One of the most important environmental problems facing the globe today is air pollution. The centre area for the local populace is the Hulu Langat district, which borders Kuala Lumpur, the capital. The purpose of this study is to look at how the amb... Read More about Temporal analysis and predictive modeling of ambient air quality in Hulu Langat District, Selangor, Malaysia: A chemometric approach.

Decision Making and Security Risk Management for IoT Environments (2024)
Book
Boulila, W., Ahmad, J., Koubaa, A., Driss, M., & Farah, I. R. (Eds.). (2024). Decision Making and Security Risk Management for IoT Environments. Springer. https://doi.org/10.1007/978-3-031-47590-0

This book contains contemporary research that outlines and addresses security, privacy challenges and decision-making in IoT environments. The authors provide a variety of subjects related to the following Keywords: IoT, security, AI, deep learning,... Read More about Decision Making and Security Risk Management for IoT Environments.

Federated Learning for Market Surveillance (2024)
Book Chapter
Song, P., Kanwal, S., Dashtipour, K., & Gogate, M. (2024). Federated Learning for Market Surveillance. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (199-218). Springer. https://doi.org/10.1007/978-3-031-47590-0_10

The data utilized in market surveillance is highly sensitive; what may be available for machine learning is limited. In this paper, we examine how federated learning for time series data can be used to identify potential market abuse while maintainin... Read More about Federated Learning for Market Surveillance.

Statistical Downscaling Modeling for Temperature Prediction (2024)
Book Chapter
Ashraf, Z., Kanwal, B., Hussain, I., Dashtipour, K., Gogate, M., & Kanwal, S. (2024). Statistical Downscaling Modeling for Temperature Prediction. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (147-169). Springer. https://doi.org/10.1007/978-3-031-47590-0_8

The application compares the Statistical Downscaling Model (SDSM) and partial least square (PLS) to bridge the gap between (minimum and maximum) daily temperatures of 11 sites in Punjab between 1961 and 2013 with atmospheric variables. The data set w... Read More about Statistical Downscaling Modeling for Temperature Prediction.

Multivariate Procedure for Modeling and Prediction of Temperature in Punjab, Pakistan (2024)
Book Chapter
Kanwal, B., Ashraf, Z., Mehmood, T., Kanwal, S., Dashtipour, K., & Gogate, M. (2024). Multivariate Procedure for Modeling and Prediction of Temperature in Punjab, Pakistan. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (99-124). Springer. https://doi.org/10.1007/978-3-031-47590-0_6

Climate study often relies upon global climate models (GCM) to project future scenarios of change in climate behavior. This study aims to refine GCM results to fill the gap between local scale surface weather with regional atmospheric predictors. The... Read More about Multivariate Procedure for Modeling and Prediction of Temperature in Punjab, Pakistan.

A novel generative adversarial network‐based super‐resolution approach for face recognition (2024)
Journal Article
Chougule, A., Kolte, S., Chamola, V., & Hussain, A. (2024). A novel generative adversarial network‐based super‐resolution approach for face recognition. Expert Systems, 41(8), Article e13564. https://doi.org/10.1111/exsy.13564

Face recognition is an essential feature required for a range of computer vision applications such as security, attendance systems, emotion detection, airport check-in, and many others. The super-resolution of subject images is an important and chall... Read More about A novel generative adversarial network‐based super‐resolution approach for face recognition.