Hany Mahgoub
Orcid: 0009-0004-1982-5851
According to our database1,
Hany Mahgoub authored at least 15 papers
between 2022 and 2026.
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Bibliography
2026
Deep Learning Approach for Social IoT and Mental Health Emotion Detection From Videos.
IEEE Trans. Comput. Soc. Syst., February, 2026
Holographic Counterpart Modelling of Wearable IoT Data for Forensic Timeline Reconstruction and Custodial Monitoring.
IEEE Trans. Consumer Electron., February, 2026
Secure drone-based scene classification and intrusion detection using multi-model deep learning.
Eng. Appl. Artif. Intell., 2026
2025
Cloud-to-Thing continuum-based sports monitoring system using machine learning and deep learning model.
PeerJ Comput. Sci., 2025
2024
Optimizing point-of-sale services in MEC enabled near field wireless communications using multi-agent reinforcement learning.
Comput. Commun., 2024
Mathematical Modeling and Simulation of Traffic Flow Control in Urban Environments Using Fuzzy Deep Neural Network With Optimization Algorithm.
IEEE Access, 2024
Design of Hybrid Snake Optimizer Based Route Selection Approach for Unmanned Aerial Vehicles Communication.
IEEE Access, 2024
Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment.
IEEE Access, 2024
2023
Multi-Objective Seagull Optimization Algorithm with Deep Learning-Enabled Vulnerability Detection for Secure Cloud Environments.
Sensors, December, 2023
Hybrid precoding design for secure smart-grid enabled MIMO wireless communications in Industry 5.0.
J. King Saud Univ. Comput. Inf. Sci., July, 2023
Comput. Syst. Sci. Eng., 2023
Deep learning enabled class imbalance with sand piper optimization based intrusion detection for secure cyber physical systems.
Clust. Comput., 2023
Hyperspectral Object Detection Using Bioinspired Jellyfish Search Optimizer With Deep Learning.
IEEE Access, 2023
2022
Anomalies Prediction in Radon Time Series for Earthquake Likelihood Using Machine Learning-Based Ensemble Model.
IEEE Access, 2022
Smart Cities-Based Improving Atmospheric Particulate Matters Prediction Using Chi-Square Feature Selection Methods by Employing Machine Learning Techniques.
Appl. Artif. Intell., 2022