Chintan Bhatt
Papers
1
Total Citations
57
H-Index
1
About
Dr. Chintan Bhatt is a leading researcher at the intersection of artificial intelligence, explainable AI (XAI), and human-computer interaction. His most cited work, "SignExplainer: An Explainable AI-Enabled Framework for Sign Language Recognition With Ensemble Learning" (2023), has garnered 57 citations, showcasing his commitment to making AI systems both powerful and transparent. Dr. Bhatt’s major contributions lie in developing ensemble learning frameworks that not only achieve state-of-the-art performance in sign language recognition but also provide interpretable outputs—a critical step for real-world deployment in assistive technologies. By integrating deep learning with explainability, he addresses the "black box" problem, enabling users and clinicians to trust and understand AI-driven decisions. Beyond this flagship work, his research spans deep learning applications in computer vision, natural language processing, and robotics, consistently pushing the boundaries of how machines perceive and interact with human communication. Dr. Bhatt’s work has significant implications for accessibility, bridging the gap between advanced AI and practical, inclusive solutions. His growing citation impact underscores his role as a rising thought leader in ethical and applied artificial intelligence.
Research Focus
Key Achievements
Top Papers
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