P
Partha Pratim Roy
Researcher at Indian Institute of Technology Roorkee
Publications - 509
Citations - 8436
Partha Pratim Roy is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Chemistry & Medicine. The author has an hindex of 36, co-authored 404 publications receiving 5505 citations. Previous affiliations of Partha Pratim Roy include Samsung & Indian Statistical Institute.
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Hypoglycemic and hypolipidemic effects of flavonoid rich extract from Eugenia jambolana seeds on streptozotocin induced diabetic rats
TL;DR: The present data suggests that the flavonoid rich extract from EJ plant has both hypoglycemic and hypolipidemic effects which can help the cure and management of diabetes.
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Efficient Facial Expression Recognition Algorithm Based on Hierarchical Deep Neural Network Structure
TL;DR: A new scheme for FER system based on hierarchical deep learning, which combines the result of the softmax function of two features by considering the error associated with the second highest emotion (Top-2) prediction result, and a technique to generate facial images with neutral emotion using the autoencoder technique.
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Coupled HMM-based multi-sensor data fusion for sign language recognition
TL;DR: A novel multi-sensor fusion framework for Sign Language Recognition (SLR) using Coupled Hidden Markov Model (CHMM), which provides interaction in state-space instead of observation states as used in classical HMM that fails to model correlation between inter-modal dependencies.
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Anti-diabetic potential of alkaloid rich fraction from Capparis decidua on diabetic mice.
TL;DR: AR fraction showed promising results in terms of anti-diabetic activities establishing its candidacy for further purification and characterization of the individual alkaloids, in order to understand their mechanism of action.
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Analysis of EEG signals and its application to neuromarketing
TL;DR: A predictive modeling framework to understand consumer choice towards E-commerce products in terms of “likes’ and “dislikes” by analyzing EEG signals is proposed and the framework can be used for better business model.