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Abu Taha Zamani

Faculty · Northern Border University, Saudi Arabia

https://researchid.co/abutaha
@faculty.nbu.edu.sa
21Scopus Publications
480Google Scholar Citations
12Google Scholar h-index
15Google Scholar i10-index

Biography

Dr. Abu Taha Zamani is a dedicated academician and researcher, currently serving as a Lecturer in the Department of Computer Science at the Faculty of Science, Northern Border University, Arar, Kingdom of Saudi Arabia. With a deep passion for technology and research, he has significantly contributed to the advancement of knowledge in various fields within computer science. He has an extensive research portfolio, with numerous articles published in prestigious international journals. His research interests span several cutting-edge areas of computer science, including Cloud Computing, Ad hoc Networks, Cyber Security, Artificial Intelligence (AI), the Internet of Things (IoT), Machine Learning, and Data Mining. His actively contributes to the academic community as a reviewer for prominent international journals and is a respected member of prestigious organizations like IEEE and ACM. He is an editorial board member of several respected computer science journals, where his work as a resea

Recent Scopus Publications

  1. UNet with self-adaptive Mamba-like attention and causal-resonance learning for medical image segmentation
    Scientific Reports, 2026
  2. Autonomous Communication Networks Powered by Quantum Variational Circuits for Real-Time Traffic Prediction and Resource Optimization
    Journal of Communications, 2026
  3. Cloud-based optimized deep learning framework for automated glaucoma detection using stationary wavelet transform and improved grey-wolf-optimization with ELM approach
    Results in Engineering, 2025
  4. Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding Techniques
    IEEE Access, 2025
  5. A Multi-Layered AI-Driven Cybersecurity Architecture: Integrating Entropy Analytics, Fuzzy Reasoning, Game Theory, and Multi-Agent Reinforcement Learning for Adaptive Threat Defense
    IEEE Access, 2025

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