Mehdi Hosseinzadeh

About

Mehdi Hosseinzadeh is the Director of the DTU AI & Data Science Hub (DAIDASH) at Duy Tan University, Vietnam. With a prolific research career, Mehdi has authored over 500 peer-reviewed publications and has supervised more than 120 Master's, Ph.D., and Postdoc students across a range of disciplines. He has an h-index of 85, reflecting the significant impact of his research contributions. His work primarily focuses on computer science and artificial intelligence, with specific expertise in deep learning, health informatics, data analysis, and the Internet of Things (IoT). Mehdi was recognized as a top 2% scientist globally in 2022, 2023, 2024, and 2025.

Employment

• Duy Tan University Director of the DTU AI & Data Science Hub (DAIDASH)
  2021 - Present

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (593)

• A privacy-aware and sustainable joint optimization for resource-constrained internet of things using deep reinforcement learning
  Internet of Things the Netherlands 2026 DOI: 10.1016/j.iot.2025.101837
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• AMORT-FIS: adaptive metaheuristic-optimized real-time fuzzy inference system for RFID network planning using heterogeneous directional antennas
  AEU International Journal of Electronics and Communications 2026 DOI: 10.1016/j.aeue.2025.156061
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• An environment-aware Q-learning-based trust evaluation scheme in Underwater Acoustic Sensor Networks (UASNs)
  Computer Standards and Interfaces 2026 DOI: 10.1016/j.csi.2025.104087
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• Dynamic Simulation and Optimization of an Innovative Cogeneration System Using TRNSYS, EES, and Response Surface Methodology as a Machine learning method
  Journal of the Taiwan Institute of Chemical Engineers 2026 DOI: 10.1016/j.jtice.2025.106461
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• Enhanced fetal electrocardiogram extraction via optimized template subtraction: A simplified approach for improved prenatal monitoring
  Computers and Electrical Engineering 2026 DOI: 10.1016/j.compeleceng.2025.110880
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• Enhancing security in IoT networks: A multifaceted approach to vulnerability analysis and protection
  Array 2026 DOI: 10.1016/j.array.2025.100626
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• Non-parametric double-layer locally weighted k-means clustering for multi-view data
  Expert Systems with Applications 2026 DOI: 10.1016/j.eswa.2025.131005
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• A Comprehensive Overview of PSO-LSTM Approaches: Applications, Analytical Insights, and Future Opportunities
  Archives of Computational Methods in Engineering 2025 DOI: 10.1007/s11831-025-10445-y
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• A Comprehensive Survey Inspired by Elephant Optimization Algorithms: Comprehensive Analysis, Scrutinizing Analysis, and Future Research Directions
  Archives of Computational Methods in Engineering 2025 DOI: 10.1007/s11831-025-10303-x
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• A Comprehensive Survey of Hybrid Whale Optimization Algorithm with Long-Short Term Memory: Applications, Improvements, and Future Perspective
  Archives of Computational Methods in Engineering 2025 DOI: 10.1007/s11831-025-10413-6
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