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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Journal ArticleDOI
Synthesis, anti-inflammatory, and cytotoxicity evaluation of 9,10-dihydroanthracene-9,10-α,β-succinimide and bis-succinimide derivatives
TL;DR: In this paper, 9,10,Dihydroanthracene-9,10-α,β-succinimide derivatives 4a, 6, e, e and 4p were evaluated for anti-inflammatory and cytotoxicity against five human cancer cell lines: T47D, NCI H-522, HCT-15, PA1, and HepG-2.
Journal ArticleDOI
Spatial distribution and accumulation of calcium in different tissues, developing spikes and seeds of finger millet genotypes
TL;DR: A clear understanding of differential accumulation, spatial distribution and the variation of calcium within finger millet genotypes/seeds could be exploited for breeding for their bio-fortification and development of calcium rich functional foods.
Proceedings ArticleDOI
End-to-end Triplet Loss based Emotion Embedding System for Speech Emotion Recognition
TL;DR: In this article, an end-to-end neural embedding system based on triplet loss and residual learning has been proposed for speech emotion recognition, which learns the embeddings from the emotional information of the speech utterances.
Journal ArticleDOI
Solvent-free synthesis and anticancer activity evaluation of benzimidazole and perimidine derivatives.
TL;DR: Two series of diversified heterocyclic molecules, tetracyclic benzimidazoles and perimidines have been synthesized in good yields by condensation of acid anhydrides and diacids with various diamines using microwave irradiation for in vitro antiproliferative activity against five human cancer cell lines.
Journal ArticleDOI
Fast recognition and verification of 3D air signatures using convex hulls
TL;DR: This paper presents a methodology to analyse 3D signatures captured using Leap motion sensor with the help of a new feature-set extracted using convex hull vertices enclosing the signature, using k-NN and HMM classifiers to classify signatures.