About the Center

The Council of Vishwakarma University established the Vishwakarma University Research Centre of Excellence for Health Informatics (VU-RCEHI) on 16 December 2021. The main aim of VU-RCEHI is to use technologies like artificial intelligence, big data analytics, and the internet of things (IoT) in the effectiveness of present-day healthcare systems.

VU-RCEHI aims to improve the practical and scientific research in health informatics at a global level and further strengthen the scholarly society. This center aims to develop reliable and accurate healthcare models for solving various complexities in the healthcare systems and contributing towards building the national economy.


Our vision is to foster and facilitate the next generation of exposure in healthcare systems to the use and development of advanced computer algorithms, informatics, and methods. A world where interdisciplinary researchers from medical electronics, biostatistics, clinical informatics, human-computer interaction, artificial intelligence, big data analytics, and the internet of things can come together to discover, manage, and work on new data knowledge relating to health and diseases.


● To carry out the training activities of strategic and national importance in health informatics with the help of artificial intelligence, big data analytics, and the internet of things.
● To provide a scientific research environment for researchers, Ph.D. scholars, post-graduate and graduate students leading to the development of advanced algorithms and techniques for tackling challenges in present health informatics.
● To collaborate researchers and experts from national and international universities working in healthcare informatics.
● To bring inter-disciplinary researchers of medical electronics to contribute to healthcare informatics.
● To strengthen the relationship between the research and development sections, corporate sector, and industry investors in healthcare informatics.
● To strengthen the cooperation with other research excellence centre to improve the present state of e-health.
● To attract investors for funding projects emanating from the stakeholders in the research centre of excellence in healthcare informatics.
● To handle joint proposals and projects with other universities and centre of excellence that cater to healthcare informatics.

Prof Mamoon Rashid

Dr. Pratibha Mahajan
Prof Nitin Satpute

Prof. (Dr.) Nitin Satpute
Team Member
Dr. Nagesh Adluru

Dr. Nagesh Adluru
Associate Scientist at Waisman Laboratory for Brain Imaging and Behavior, Madison, Wisconsin, United States.
Dr. Diana Lungeanu

Dr. Diana Lungeanu
Professor of Medical Informatics, University of Medicine and Pharmacy, Timișoara, Romania.

Dr. Siamak Yousefi

Dr. Siamak Yousefi
Assistant Professor, University of Tennessee Health Science Center, Memphis, Tennessee, United States.

Dr. Chaojie Wang

Dr. Chaojie Wang
Principal Systems Engineer, MITRE Corporation,Washington DC, USA.

Dr. Dileep Kumar

Dr. Dileep Kumar
Director of Scientific Collaborations, United Imaging Healthcare, Asia, Middle East and Emerging Markets.
Dr. Vijayakumar Varadarajan

Dr. Vijayakumar Varadarajan
Professor, University of New South Wales, Australia.

Research Activities

Ongoing Internships

Project Title: Efficient Deep Learning Model for ROI Extraction in functional Magnetic Resonance Imaging
Dates: 23 Jan 2023- 31 July 2023
Students Enrolled:

  1. Kaushal Rohit Oza (B.Tech,Computer Engineering, 2019-2023)
  2. Shree Ramesh Udavant (B.Tech, Computer Engineering, 2019-2023)
  3. Rushikesh Girish Jyoti (B.Tech, Computer Engineering, 2019-2023)

Students worked on following modules:

  • Understanding anatomy of 4D medical images.
  • Dealing with the high dimensional medical data.
  • ROI extraction in brain images.
  • Training Deep learning model for evaluating performance of extracted ROIs.

Completed Projects

Project Title: A Novel AI Therapy for Depression Detection using Face Emotion Techniques
Dates: 01-January 2022 – 31 December 2022
Students Enrolled:

  1. Prathmesh Kawtikwar (B.Tech,Computer Engineering, 2019-2023)
  2. Pranesh Kathavate (B.Tech,Computer Engineering, 2019-2023)
  3. Pratyush Singh(B.Tech,Computer Engineering, 2019-2023)
  4. Pratham Vernekar (B.Tech,Computer Engineering, 2019-2023)
  5. Rahul Mane (B.Tech,Computer Engineering, 2019-2023)

Students worked on following modules:

  • Explored Depression Facial Imaging Datasets.
  • Identify depression symptoms in patients from Facial Images.
  • Developed model for Depression Detection for Real Time Facial Images.

Project Title: Emotional Recognition from Facial Images using Deep Learning Approach
Dates: 01-March 2022 – 31 July 2022
Students Enrolled:

  1. Shaikh Meraj Abdulkadar (Masters in Computer Science, 2021-2023)
  2. Mundada Pooja Rajesh (Masters in Computer Science, 2021-2023)
  3. Dokrimare Mansi Bhaskar (Masters in Computer Science , 2021-2023)
  4. Sable Shubhranshu Pramod (Masters in Computer Science, 2021-2023)
  5. Dhumal Shubhankar Prashant (Masters in Computer Science , 2021-2023)
  6. Tamboli Samad Sabir (Masters in Computer Science , 2021-2023)

Students worked on universal facial expressions of seven emotions – anger, contempt, disgust, fear, joy, sadness, and surprise.

The idea was to develop an intelligent system for facial image based expression classification using deep learning.

Completed Internships

Project Title: Image Based Fall Detection Model For Elderly People
Dates: 01 Feb 2022- 10 May 2022
Students Enrolled:

  • Harshit Tiwari (B.Tech,Computer Engineering, 2018-2022)
  • Aditya Upaganlawar (B.Tech, Computer Engineering, 2018-2022)
  • Aishwarya Ramakrishnan (B.Tech, Computer Engineering, 2018-2022)
  • Kundan Choudhary (B.Tech, Computer Engineering, 2018-2022)

Students worked on following modules:

  • How to use Regular 2D Cameras for Video Capturing.
  • Algorithm for Object Detection in Real Time.
  • Algorithm for Object Tracking.
  • Algorithm for Person Positioning.
  • Algorithm for Posture Recognition.
  • Algorithm for Fall Detection in Elderly People.

Project Title: Brain Tumor Classification with Deep Learning Approach
Dates: 16 June 2022- 31 July 2022
Students Enrolled:


  1. Adityaraj Sanjay Belhe (B.Tech, Artificial Intelligence and Data Science, 2020-2024)
  2. Janvi Pagariya (B.Tech, Artificial Intelligence and Data Science, 2020-2024)
  3. Uravane Prathamesh Suhas (B.Tech, Artificial Intelligence and Data Science, 2020-2024)
  4. Vedant Vinay Ganthade (B.Tech, Artificial Intelligence and Data Science, 2020-2024)


  1. Pratham Sampat (B.Tech, Computer Engineering, 2020-2024)
  2. Sakshi Jagtap (B.Tech, Computer Engineering, 2020-2024)


  1. Sakshi Patil (B.Tech, Computer Engineering, 2020-2024)
  2. Dev Dinesh Shah (B.Sc, Computer Science, 2020-2023)
  3. Chirayu Avadhraj Yadav (B.Tech, Computer Engineering, 2020-2024)

Students worked on following modules:

  • Understanding anatomy of MRI images
  • Dealing with the 3D medical data.
  • Classification of Brain Tumors
  • Training Deep learning model for evaluating performance of Brain Tumors.


    • Segmentation of Nucleus and Cytoplasm from H&E-Stained Follicular Lymphoma, Electronics, MDPI, SCIE, IF=2.690, January, 2023. DOI: 10.3390/electronics12030651

    • IoMT with Deep-CNN: An Intelligent Decision-Support System for Pandemic Disease Classification and Prediction, Electronics, MDPI, SCIE, IF=2.690, January, 2023. DOI: 10.3390/electronics12020424

    • Comprehensive Database Creation for Potential Fish Zones using IoT and ML with Assimilation of Geospatial Techniques, Sustainability, MDPI, SCIE, IF=3.889, January 2023. DOI: 10.3390/su15021062

    • An Efficient Deep Learning based Approach for the Detection of Brain Tumours, 5th IEEE International Conference on Contemporary Computing and Informatics (IC3I-2022), 14-16 December 2022, Amity University, Knowledge Park III, Greater Noida Gautam Budhh Nagar, Uttar Pradesh, India.

    • An Efficient Machine Learning Approach for Diagnosing Parkinson's Disease by Utilizing Voice Features, Electronics, MDPI, SCIE, IF=2.690, November, 2022. DOI: 10.3390/electronics11223782

    • Chest X Ray and Cough Sample based Deep Learning Framework for Accurate Diagnosis of COVID-19, Computers and Electrical Engineering, Elsevier, SCIE, IF=4.152, September 2022. DOI: 10.1016/j.compeleceng.2022.108391

    • Efficient Machine Learning based Feature Optimization Model for the Detection of Dyslexia, Computational Intelligence and Neuroscience, Hindawi, SCIE, IF=3.633, June 2022. (Accepted).

    • Artificial Intelligence for Innovative Healthcare Informatics, Springer. https://link.springer.com/book/10.1007/978-3-030-96569-3

    • An optimization-based diabetes prediction model using CNN and bi-directional LSTM in real-time environment, Applied Sciences, MDPI, SCIE, IF=2.679, April, 2022. DOI: 10.3390/app12083989

    • Digital Taste in Multimedia Augmented Reality: Perspective and Challenges, Electronics, MDPI, SCIE, IF=2.679, April, 2022. DOI: 10.3390/electronics11091315

    • AI-Enabled Radiologist in the Loop: Novel AI-based Framework to Augment Radiologist Performance for COVID-19 Chest CT Medical Image Annotation and Classification from Pneumonia, Neural Computing and Applications, SCI, IF=5.606, February 2022. DOI: 10.1007/s00521-022-07055-1

    • Cloud server and Internet of Things Assisted System for Stress Monitoring, Electronics, MDPI, SCIE, IF=2.679, December, 2021. DOI: 10.3390/electronics10243133

Contact Us

Dr. Mamoon Rashid

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