Rakshit Agrawal

Papers

Total Citations

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About

Rakshit Agrawal is a leading researcher at the intersection of artificial intelligence, audio signal processing, and smart healthcare systems. His most influential work, "Comparative analysis of audio classification with MFCC and STFT features using machine learning techniques" (2024, 66 citations), provides a definitive benchmark for audio feature extraction methods, demonstrating how Mel-frequency cepstral coefficients and short-time Fourier transform features can be optimally leveraged for machine learning-based audio classification. This foundational study has become essential reading for researchers developing automated audio analysis tools. Agrawal has also made significant contributions to healthcare technology through his work on "AI and IoT Enabled Smart Hospital Management Systems" (2022, 16 citations), where he explores the integration of artificial intelligence with Internet of Things devices to create more efficient, data-driven hospital environments. His research demonstrates a unique ability to bridge theoretical machine learning concepts with practical, real-world applications. By combining rigorous comparative analysis with innovative system design, Agrawal continues to advance both the technical foundations and applied implementations of intelligent audio processing and smart healthcare solutions.