scispace - formally typeset
S

Sudeep Tanwar

Researcher at Nirma University of Science and Technology

Publications -  410
Citations -  11253

Sudeep Tanwar is an academic researcher from Nirma University of Science and Technology. The author has contributed to research in topics: Computer science & Smart grid. The author has an hindex of 43, co-authored 263 publications receiving 5402 citations. Previous affiliations of Sudeep Tanwar include Bharat Institute of Technology & University Institute of Technology, Burdwan University.

Papers
More filters
Journal ArticleDOI

UpHaaR: Blockchain-based charity donation scheme to handle financial irregularities

TL;DR: Wang et al. as discussed by the authors proposed a blockchain-based charity scheme, UpHaaR, where donators and beneficiaries register in blockchain, managed for diverse fundraisers by charity organizations.
Journal ArticleDOI

ADYTIA: Adaptive and Dynamic TCP Interface Architecture for heterogeneous networks

TL;DR: An Adaptive and Dynamic TCP Interface Architecture (ADYTIA) is proposed, which allows the usage of different TCP variants based on application and link characteristics, irrespective of the physical links of the entire path.
Journal ArticleDOI

A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure

TL;DR: In this paper , the authors present a comprehensive survey on standardizing EV charging infrastructure and propose an architecture for standardized EV community charging infrastructure to provide adaptability for EVs with different charging standards.
Journal ArticleDOI

Stochastic Neural Networks-Based Algorithmic Trading for the Cryptocurrency Market

TL;DR: In this paper , the authors proposed hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks to predict prices based on market data and social sentiment.
Proceedings ArticleDOI

Tensor Decomposition of Biometric Data using Singular Value Decomposition

TL;DR: This paper has applied the Tensor Decomposition technique (TD), i.e., an SVD to reduce dimensionally of data, and compares the performance of SVD on using a different dataset of varying size data.