Model Institute of Engineering and Technology, India
Summary
Dr. Mohammad Shabaz has completed his B. Tech in Information Technology and Telecommunication Engineering from Baba Ghulam Shah Badshah University, J&K, M.E and Ph.D. in Computer Science Engineering from Chandigarh University, Mohali. He is working as Assistant Professor (Senior Scale) at Model Institute of Engineering and Technology, Jammu, India. He is Editor at Neuroscience Informatics (Elsevier). Furthermore, he Guest-Edited many special issues in various journals like Healthcare Analytics, Decision Analytics Journal, SLAS Technology, Journal of Neuroscience Methodx, IEEE Transactions on Consumer Electronics, IEEE JBHI, Nanotechnology reviews, Nonlinear Engineering, Open Life Sciences, Discover Sustainability etc. His area of interest is application of computer science in interdisciplinary domains. He has published over 200+ research papers in various journals indexed in Scopus/Web of Science and having a Scopus H-index of 32 and Google Scholar H-index of 38, 10 Indian Patents and 3 Australian Patents. His major work is in the healthcare domain. His major contributions include the creation of novel algorithms like SA Sorting, Shabaz-Urvashi Link Prediction. He was included in the Stanford University and Elsevier World's top two percent scientist rankings 2023, 2024.
In the evolving framework of the Intelligence of Social Things (IoST), which amalgamates social networks and IoT ecosystems, knowledge graphs are essential for facilitating networked systems to efficiently process and leverage intricate relational data. Knowledge graphs offer essential technical assistance for various artificial intelligence applications, such as e-commerce, intelligent navigation, healthcare, and social media. Nonetheless, current knowledge graphs frequently lack completeness, harboring a considerable quantity of implicit knowledge that remains to be revealed. Consequently, tackling the difficulty of finalising knowledge graphs has emerged as a pressing research priority. M... More >
Graphical Abstract
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