Research Intern
Interdisciplinary research in collaboration with the CSRTC, focusing on integrated approaches for impurity detection and classification in solid foods using machine learning and NIR spectrometry.Designed a robust framework leveraging transfer learning to enhance the detection and classification of skin diseases, achieving a validation accuracy of 96.46%on datasets including the Monkeypox Skin Images Dataset (MSID).Supported and mentored peers by guiding them efficient techniques to carry out researches, and collaborating on research methodologies.Authored and presented findings at prestigious conferences.