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Rishi Raj

Rishi Raj

Assistant Professor
Information Systems
rishi[dot]raj[at]iimv[dot]ac[dot]in



Dr. Rishi is a faculty in Information Systems area. He has received his B.Tech. and M. Tech. degrees in Computer Science and Engineering from IIT (BHU). He holds a PhD degree from IIT Patna with the thesis titled "Brain Stroke Classification and Management using Deep Learning Techniques". Prior joining to IIM Visakhapatnam, he worked as an Assistant Professor at Kalinga Institute of Industrial Technology, Bhubaneswar.

His current research interest lies in Generative AI with a special focus on Large Language Models and AI integration in IT Systems.

  • Management Information Systems

Patents - Granted
  • Artificial Intelligence Powered Surveillance Equipment for Transport Safety  Authority: Intellectual Property Office, Government of the United Kingdom. Reference Number: 6374431
  • Autonomous Solar-Powered Mobile Surveillance and Navigation System
  • Authority: Intellectual Property Office, Government of the United Kingdom. Reference Number: 6385538
  • Person Counter System using Artificial Intelligence Authority: Intellectual Property Office, Government of the United Kingdom. Reference Number: 6392134
Patents - Published
  • System and Method for Artificial Intelligence Based Automated Diagnosis for Brain Stroke on Radiographic Films Authority: The Office of Controller General of Patents, Designs & Trade Marks, Govt. of India. Ref. No. 202431003954
Journal Publications
  • Raj, R., Mathew, J., Kannath, S. K., & Rajan, J. (2022). Crossover based technique for data augmentation. Computer Methods and Programs in Biomedicine, 218, 106716.
  • Raj, R., Mathew, J., Kannath, S. K., & Rajan, J. (2023). StrokeViT with AutoML for brain stroke classification. Engineering Applications of Artificial Intelligence, 119, 105772.
  • Raj, R., Kannath, S. K., Mathew, J., & Sylaja, P. N. (2023). AutoML accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke. Frontiers in Neurology, 14, 1259958.
  • Raj, R., Pruthviraja, D., Gupta, A., Mathew, J., Kannath, S. K., Prakash, A., & Rajan, J. (2024). Multilevel Multimodal framework for automatic collateral scoring in brain stroke. IEEE Access.
International Conferences
  • Raj, Rishi, et al. "Resident Vision Transformer: Lightweight Deep Learning Model for Disease Diagnosis on Edge Devices." 2024 10th International Conference on Smart Computing and Communication (ICSCC). IEEE, 2024.
  • Pattnaik, Nitish, Raj, Rishi, et. al. “High Performance Image Restoration with SpectraNet: A Lightweight CNN Architecture" 2024 International Conference on Machine Learning and Data Engineering (iCMLDE). IEEE, 2024.
  • Gaurav, Raj, Rishi, et. al. “AlzClassNet: A Hybrid CNN-Transformer model for Alzheimer’s Disease Detection" 2024 International Conference on Machine Learning and Data Engineering (iCMLDE). IEEE, 2024.
Incubations
  • Development of PACS Software (Technology Readiness Level 6): Focused on enhancing robustness, ensuring cost-effectiveness, and prioritizing security to meet industry standards and user needs.
  • Development of Person Tracker System (Technology Readiness Level 5): Utilizing YOLO models to ensure only authorized patients are present in radiotherapy rooms, adhering to AERB guidelines for enhanced safety and compliance.
  • Developing an Innovative Medical Device for Retained Surgical Objects Prevention (Technology Readiness Level 3): Leveraging IoT technology to prevent unintentional retention of surgical sponges or instruments, enhancing patient safety and surgical accountability.



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