Kuldeep Sharma

Papers

Total Citations

H-Index

About

Kuldeep Sharma is a researcher whose work bridges computer vision, machine learning, and socially impactful applications. His primary research areas include gesture recognition for assistive technology and predictive modeling for financial risk assessment. In his most cited work, "Vision-based Hand Gesture Recognition for Indian Sign Language Using Convolution Neural Network" (2023, 3 citations), Sharma developed a deep learning approach to interpret Indian Sign Language, offering a non-invasive, vision-based solution that can improve communication accessibility for the hearing-impaired community. This contribution demonstrates his commitment to applying AI for inclusive technologies. Additionally, his paper "Predicting Possible Loan Default Using Machine Learning" (2022, 2 citations) explores financial analytics, using classification models to forecast borrower risk—a practical tool for banking and lending institutions. Though early in his career, Sharma’s work has already garnered attention for its dual focus on social good and real-world problem-solving. His research stands out for its clarity in methodology and direct applicability, making him a promising voice in applied machine learning and computer vision.