scispace - formally typeset
G

Gaurav Sharma

Researcher at Shenzhen University

Publications -  1520
Citations -  40824

Gaurav Sharma is an academic researcher from Shenzhen University. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 82, co-authored 1244 publications receiving 31482 citations. Previous affiliations of Gaurav Sharma include Northeastern University & D. E. Shaw & Co..

Papers
More filters
Proceedings ArticleDOI

Universal Image Steganalysis Using Rate-Distortion Curves

TL;DR: This paper proposes an alternative feature set for steganalysis based on rate-distortion characteristics of images based on two key observations: i) data hiding methods typically increase the image entropy in order to encode hidden messages; ii) data hide methods are limited to the set of small, imperceptible distortions.
Journal ArticleDOI

Towards a secure incremental proxy re‐encryption for e‐healthcare data sharing in mobile cloud computing

TL;DR: This paper intends to propose a pairing‐free incremental proxy re‐encryption scheme, without certificates, which would run proportionate to the number of modifications in time, instead of the document length for improvement in the file modification tasks.
Journal ArticleDOI

Management of scalp arterio-venous malformation: case series and review of literature

TL;DR: Surgical excision has excellent outcome in treatment of scalp AVM and pre-operative embolization reduces vascularity and helps in easy identification of AVM during surgery thus achieving complete excision.
Journal ArticleDOI

Designing of bentonite based nanocomposite hydrogel for the adsorptive removal and controlled release of ampicillin

TL;DR: In this article, the authors focused on batch experiments for adsorptive removal of ampicillin (AMP) and its cumulative release in different solutions using xanthan gum-cl-poly(itaconic acid)/bentonite (XG-clpoly(IA)/BN) nanocomposite hydrogel.
Journal ArticleDOI

Modified Energy-Efficient Range-Free Localization Using Teaching–Learning-Based Optimization for Wireless Sensor Networks

TL;DR: Simulation results show that the proposed algorithm achieves better localization accuracy, high positioning coverage with less energy consumption in comparison of the existing improved DV-Hop algorithms.