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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.

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Journal ArticleDOI

Secrecy-ensured NOMA-based cooperative D2D-aided fog computing under imperfect CSI

TL;DR: Simulation results scrutinize the superiority of the proposed NOMA-based D2D-aided FC system over sum rate, secrecy capacity, and processing delay parameters and improve the secrecy capacity of the network and spectral efficiency using a coalition game theory.
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Deep learning-based scheme to diagnose Parkinson's disease

TL;DR: Two novel approaches using deep learning (DL) techniques to identify biomarkers that can be used to know how the disease spreads, leading to a cure in the future and compared the models' results using different evaluation parameters.
Book ChapterDOI

Markov Model for Password Attack Prevention

TL;DR: In this article, a Markov model-based password strength meter is used to evaluate the strength of a password in a more accurate way than the existing state-of-the-art methods.
Journal ArticleDOI

Methods for the evaluation of biomarkers in patients with kidney and liver diseases: multicentre research programme including ELUCIDATE RCT

TL;DR: The ELUCIDATE trial was an ‘exemplar’ trial that has demonstrated the challenges of evaluating biomarker strategies in ‘end-to-end’ RCTs and will inform future study designs and synthesise a strategy and framework for future biomarker evaluations incorporating innovations in study design, health economics and health informatics.
Journal ArticleDOI

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

TL;DR: This survey extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies, along with their security aspects and solutions for the smart healthcare system, and proposes an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners.