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Abdussalam Elhanashi

Department of Information Engineering · University of Pisa

https://researchid.co/ahanashi
@dii.unipi.it
56Scopus Publications
2854Google Scholar Citations
26Google Scholar h-index
31Google Scholar i10-index

Biography

Abdussalam Elhanashi is an an accomplished AI researcher specializing in deep learning for computer vision, imaging, and video applications. He holds a PhD in Information Engineering from Università di Pisa (IsDB scholarship), an MSc from the University of Glasgow, and an MBA from the University of Nicosia. With 16+ years of experience, he has developed AI-driven diagnostic tools (University of Strathclyde) and advanced video analysis techniques (Hiroshima University). His work bridges AI research and real-world applications in healthcare imaging, surveillance, and edge AI. An active SIIM member, he focuses on optimized, deployable AI systems, with expertise in model optimization, medical imaging, video analysis, and lightweight AI for edge devices.

Education

PhD in Information Engineering at University of Pisa (Italy) :-From June 2019 till June 2022 (Graduated on 03/02/2023) MBA, University of Nicosia: Nicosia, Cyprus (2015-11-11 to 2018-03-01) MSc Electronic and Electrical Engineering with Management at University of Glasgow ( The UK ) Year :-From January 2017 till January 2018 (Graduated on 08/01/2018) BSc in Electronics and Electrical Engineering at College of Engineering Technology Janzour (Libya) Year:-From February 2010 till February 2015 (Graduated on 10/02/2015)

Recent Scopus Publications

  1. Generative AI and the Foundation Model Era: A Comprehensive Review
    Big Data and Cognitive Computing, 2026
  2. Advanced Fault Detection and Diagnosis Exploiting Machine Learning and Artificial Intelligence for Engineering Applications
    Electronics Switzerland, 2026
  3. Large-Scale Foundation Models for Radiological Image Analysis: Clinical Applications, Technical Challenges, and Future Directions
    Journal of Imaging Informatics in Medicine, 2026
  4. Tiny deep learning models for real-time and efficient embedded driver state detection
    Journal of Real Time Image Processing, 2026
  5. Embedded Anomaly Detection System for In-Vehicle Networking Cybersecurity
    Lecture Notes in Electrical Engineering, 2025

Research Outputs

Early Stroke Detection: A Mobile Application for Real-Time Stroke Diagnosis Using Video and Lightweight Deep Learning

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