JIIT

Academic Experts

Dr. Ankit Vidyarthi

ASSOCIATE PROFESSOR

ankit.vidyarthi@jiit.ac.in

Biography

Ankit Vidyarthi presently holds a post of Associate Professor in the Department of Computer Science Engineering & Information Technology, Jaypee Institute of Information Technology Noida. He joined the Institute in June 2018 and from then onwards he is associated with academics and research activities at the university level. He had completed his Postdoc (Machine Learning) from Bennett University in 2018. He obtained his Ph.D. from the Department of CSE, Malaviya National Institute of Technology Jaipur in 2017. He had several research papers in reputed SCI/(E) indexed journals with good impact factors. He had also published 20 research articles in various peer-reviewed conferences of IEEE/Springer/ACM/Elsevier which were indexed in Scopus. He is a member of ACM with professional members and SIGACT membership. He is also associated with various journals as a reviewer which are of high standards and indexed in SCI/(E). He is also a member of the Technical Program Committee in various conferences and workshops. He is associated with one journal (JIEEE) as an associate Editor-in-Chief, Senior Editor with AI Foundation Trust, India, Editor of Book Series in IET, Associate Editor (GE) with IEEE Transactions on Industrial Informatics, Interdisciplinary Sciences: Computational Life Science Springer Journal, Frontiers in Genetics, IEEE JSTAR, ACM Transactions on Asian and Low-Resource Language Information Processing, and IEEE Transactions on Consumer Electronics.

Research Highlights

Ankit Vidyarthi research primarily focuses on advancing Healthcare through the integration of Machine Learning, Artificial Intelligence, Natural Language Processing, and Medical Image Analysis. He has contributed to the development of intelligent diagnostic frameworks that leverage deep learning models for improved disease detection, prognosis, and treatment planning. His work emphasizes automation and efficiency in medical imaging tasks such as segmentation, classification, and anomaly detection, thereby reducing clinical workload and enhancing accuracy. In addition, he has explored the role of Natural Language Processing in extracting meaningful insights from unstructured clinical text, bridging the gap between medical data and practical decision-making. By combining Artificial Intelligence with robust computational models, his research strengthens the potential of technology-driven solutions to transform patient care, optimize healthcare workflows, and support precision medicine.

Areas Of Interest

Medical Image Analysis
Healthcare Systems
Natural Language Processing
Machine Learning Applications
Artificial Intelligence based Solutions

Publications

N. Bharot, P. Verma, A. Vidyarthi, D. Gupta and J. G. Breslin, "Revolutionizing Wearable Sensor Data Analysis With an Automated Decision-Making Model for Enhanced Human Activity Detection," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2025.3604710.
N. Bansal and A. Vidyarthi, "Hierarchical Meta-Heuristic Encoder-Decoder Architecture With Next-Generation Imaging for Consumer-Centric Segmentation of Diabetic Foot Ulcers," in IEEE Transactions on Consumer Electronics, vol. 71, no. 1, pp. 189-197, Feb. 2025, doi: 10.1109/TCE.2025.3532639.
U. K. Jaiswal et al., "Optimizing Near-Field Communication for Industrial IoT: A Biologically-Inspired Approach to Security and Efficiency," in IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 5232-5240, May 2025, doi: 10.1109/TCE.2025.3570575.
Jain, Deepak Kumar, S. Neelakandan, Ankit Vidyarthi, Anand Mishra, and Ahmed Alkhayyat. "A knowledge-Aware NLP-Driven conversational model to detect deceptive contents on social media posts." Computer Speech & Language 90 (2025): 101743.
Thuppari, S. Jannu, D. R. Edla, A. Vidyarthi, K. K. Agarwal and A. Alkhayyat, "Energy-Aware Compression and Consumption Algorithms for Efficient TinyML Model Using Aquila Optimization in Industrial IoT," in IEEE Transactions on Consumer Electronics, vol. 71, no. 1, pp. 334-342, Feb. 2025, doi: 10.1109/TCE.2024.3475393.