The School of Computing Science Engineering and Artificial Intelligence (SCAI) has planned to organize a Two-day National level Workshop on “Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications” from 10 – 11 October 2024 for Faculty, Research scholars and students both Internal and external participants through virtual mode.

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Resource persons:

Dr. Surendiran B, NIT, Puducherry.
Dr. Malaya Kumar Nath, NIT, Puducherry.
Dr. Senthil Kumar T, Amrita University, Coimbatore.
Dr. Kannimuthu S, Karpagam College of Engineering, Coimbatore.

Topics to be covered

• Data collection approaches from several sources for Machine Learning and Deep Learning models
• Machine Learning and Deep Learning models for Disease classification and prediction using images
• Recent Trends and Advancements in Healthcare Image Analytics
• Current and Future Impacts of Pandemics and Risk Mitigation in Healthcare
• Fundamentals of ML and DL in the context of the Healthcare domain
• Data collection approaches from several sources and how to use them in ML/DL models
• ML and DL models for Disease classification and prediction using images
• Recent Trends and Advancements in Healthcare Image Analytics
• Current and Future Impacts of Pandemics and Risk Mitigation in Healthcare
• Identification of heart defects from ECG signal using Explainable AI

Co-ordinator: 

Dr. Komarasamy G, Senior Associate Professor, SCAI, VIT Bhopal University, Bhopal.
Dr. Jothiaruna N, Assistant Professor, SCAI, VIT Bhopal University, Bhopal.

Registration details:

Registration Fee External (Faculty / Student): Rs 300
Registration Fee VIT (Faculty / Student) : Rs 100             

Account details for payment

After successful payment, submit the registration form:

https://forms.gle/dgBE7wKcTs5rTygh9

Last date for applying: 3rd October 2024

Registration confirmation: 4th October 2024

Benefits of attending the workshop

• Collaborate on projects and research initiatives.
• Access to workshop materials, resources, and tools.
• Opportunities for research publication and presentation.
• Insight into regulatory and ethical considerations.
• Potential for startup or entrepreneurship opportunities.
• Develop expertise in a rapidly growing field.
• Develop predictive models for patient outcomes and disease risk.
• Implement AI-driven solutions for healthcare challenges.