Harnessing Artificial Intelligence-Enhanced Graph Models for Biological Discovery: Unveiling Biological Frontiers - Softcover

Über die Autorinnen und Autoren

With 24+ years of combined experience in teaching, research, and industry, Prof. Sudan Jha is a Senior Member of IEEE and a Professor in the Department of Computer Science & Engineering at Kathmandu University, Nepal. He leads the IoT R&D Lab at Kathmandu University and has previously been affiliated with KIIT University, Chandigarh University, Christ University, and more. He has held key positions such as Technical Director at Nepal Television, Principal in engineering colleges, and Individual Consultant at Nepal Telecom Authority.

Prof. Jha is an AI expert with active contributions to Nepal's AI Policy 2081 for the Government of Nepal. He has also mentored the AI Concept Paper submitted by the Federation of Computer Association of Nepal (CAN Federation). Additionally, he serves as the Vice President of npCERT (Information Security Response Team Nepal), playing a crucial role in cybersecurity and digital infrastructure initiatives.

Dedicated to advancing higher education and smart platform technologies, he has published 95+ SCI/SCIE-indexed research papers and book chapters in international peer-reviewed journals and conferences. He is a Co-Editor-in-Chief of an international journal and a Guest Editor for SCIE/ESCI/SCOPUS-indexed journals. His research contributions include three patents and authorship/editorship of seven books on IoT, 5G, and AI, published by Elsevier, CRC, and AAP. His work has also secured funding for two international projects.

Prof. Jha has delivered keynote speeches at over 45 international conferences and has conducted faculty development programs, short-term training programs, and workshops at both national and international levels. He holds certifications in Microservices Architecture, Data Science, and Foundations of Artificial Intelligence.

His primary research interests include AI & IoT integration, Quality of Services in IoT-enabled devices, Neutrosophic Theory, and Neutrosophic Soft Set Systems.

Sultan Ahmad (Member, IEEE) is currently a Faculty Member of the Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia. He earneda Master of Computer Science and Applications from Aligarh Muslim University, India, in 2006, and a Ph.D. degree in CSE from Glocal University. He has almost 2 decades of teaching and research experience. He has more than 110 accepted and published research papers and book chapters in reputed SCI, SCIE, ESCI, and SCOPUS-indexed journals and conferences. He has an Australian patent and a Chinese patent in his name. He has authored several books and presented his research papers at many national and international conferences. His research interests include Intelligent computing, data Science, machine learning, and the Internet of Things. He is a member of IEEE, IACSIT and the Computer Society of India

Dr. Subhendu kumar Pani received his Ph.D. from Utkal University ,Odisha, India. He is working as professor at Krupajal Engineeing College under BPUT, Odisha, India. He has almost 2 decades of teaching and research experience. His research interests include Data mining, Big Data Analysis, web data analytics, Fuzzy Decision Making and Computational Intelligence. He is the recipient of 5 researcher awards. He has published dozens of international journal papers. His professional activities include roles as Book Series Editor, Associate Editor, Editorial board member and/or reviewer of various international journals. He is Associated with a number of conference societies. He has more than 150 international publications, 5 authored books, 15 edited and upcoming books; 20 book chapters into his account. He is a fellow in SSARSC and life member in IE, ISTE, ISCA,OBA.OMS, SMIACSIT, SMUACEE, CSI.

Von der hinteren Coverseite

Harnessing Artificial Intelligence-Enhanced Graph Models for Biological Discovery: Unveiling Biological Frontiers provides an innovative solution by presenting advanced techniques that integrate artificial intelligence with graph-based methodologies to unravel intricate biological networks. The book includes practical examples and case studies, equipping researchers and practitioners with the necessary tools to analyze molecular interactions, identify crucial biomarkers, and accelerate the discovery of new therapeutics. It explores the intricate synergy between advanced AI techniques and graph models designed specifically to unravel the complexities of biological systems. By leveraging cutting-edge AI algorithms and graph-based methodologies, researchers and practitioners can unlock new avenues for biological discovery, enabling them to analyse complex biological networks, predict molecular interactions, and identify novel drug targets with unparalleled accuracy and efficiency.