Biography

Dr. AMIN UL HAQ is an experienced researcher with more than 12 years of cutting-edge research and teaching experience in prestigious institutes, including the National University of Modern Languages (NUML), Abdul Wali Khan University Mardan Bunar campus, and Agricultural University Peshawar Pakistan. He received his Ph.D. in Computer Science (Software Engineering) from the University of Electronic Science and Technology of China. He also completed his postdoc at the School of Computer Science and Engineering, University of Electronic Science and Technology of China. Also an associate professor in Moh- Uddin Islamic University Azad Kashmir Pakistan. He currently working/collaborating with TeCIP Institute Scoula Superiore Sant’Anna (SSSA), Via Moruzzi 1, 56124, Pisa, Italy. He has first and co-authored more than 88 research papers, including 51 in leading international journals SCI of rank JCR 1, 2, 3, 4, and 7 papers are in JCR rank 1 and Q1 SCI journals(namely Knowledge-based Systems, Expert Systems with Applications, IEEE Journal of Biomedical and Health Informatics, Computer and Electrical Engineering, Scientific Reports, Journal of Ambient Intelligence and Humanized Computing, IEEE Access, and Sensors) and 36 in peer-reviewed international conference proceedings and one Patent. During his career, he has taught various courses at the Undergraduate (UG) and Postgraduate (PG) levels. He regularly organizes timely special sessions and workshops for several Flagship IEEE conferences and is an active member of the IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). Also, sponsor the IDITR IEEE conference. He is an invited reviewer for numerous world-leading high-impact journals (reviewed 70+ journal papers to date). He has more than 3703 Google citations, an h-index of 29, an I-10 index of 50, and an impact factor of more than 184.40. His area of research is medical data analysis to diagnose diseases using Machine learning, Deep learning models, Transfer Learning, and Federated Learning Techniques.