C
Chinmay Chakraborty
Researcher at Birla Institute of Technology, Mesra
Publications - 146
Citations - 2644
Chinmay Chakraborty is an academic researcher from Birla Institute of Technology, Mesra. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 14, co-authored 97 publications receiving 900 citations. Previous affiliations of Chinmay Chakraborty include Birla Institute of Technology and Science & Indian Institute of Technology Kharagpur.
Papers
More filters
Journal ArticleDOI
Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset
L. J. Muhammad,Ebrahem A. Algehyne,Sani Sharif Usman,Abdulkadir Ahmad,Chinmay Chakraborty,Ismail A. Mohammed +5 more
TL;DR: Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative CO VID-19 cases of Mexico.
Journal ArticleDOI
Anonymity Preserving IoT-Based COVID-19 and Other Infectious Disease Contact Tracing Model
TL;DR: A novel privacy anonymous IoT model that leverages blockchain’s trust-oriented decentralization for on-chain data logging and retrieval that will make it easy to identify clusters of infection contacts and help deliver a notification for mass isolation while preserving individual privacy is designed and presented.
Journal ArticleDOI
A review on telemedicine-based WBAN framework for patient monitoring.
TL;DR: The framework for integrating body area networks on telemedicine systems for patient monitoring in different scenarios is designed and the important aspects like major characteristics, research issues, and challenges with body area sensor networks in telemedics systems are described.
Journal ArticleDOI
Artificial Intelligence and Internet of Things Based Healthcare 4.0 Monitoring System
Amit Kishor,Chinmay Chakraborty +1 more
TL;DR: The main aim of the work is to propose a Machine learning-based healthcare model to early and accurately predict the different diseases and help doctors to diagnose the disease early.
Journal ArticleDOI
Grape Disease Detection Network Based on Multi-Task Learning and Attention Features
TL;DR: A grape leaf disease detection network (GLDDN) is proposed that utilizes dual attention mechanisms for feature evaluation, detection, and classification and achieves an overall accuracy of 99.93% for esca, black-rot and isariopsis detection.