J
Jyotir Moy Chatterjee
Researcher at Kathmandu
Publications - 47
Citations - 1469
Jyotir Moy Chatterjee is an academic researcher from Kathmandu. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 13, co-authored 29 publications receiving 710 citations. Previous affiliations of Jyotir Moy Chatterjee include Asia Pacific University of Technology & Innovation.
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Journal ArticleDOI
COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm
Celestine Iwendi,Ali Kashif Bashir,Atharva Peshkar,R. Sujatha,Jyotir Moy Chatterjee,Swetha Pasupuleti,Rishita Mishra,Sofia Pillai,Ohyun Jo +8 more
TL;DR: A fine-tuned Random Forest model boosted by the AdaBoost algorithm is proposed that uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death.
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A machine learning forecasting model for COVID-19 pandemic in India.
TL;DR: This work has performed linear regression, Multilayer perceptron and Vector autoregression method for desire on the CO VID-19 Kaggle data to anticipate the epidemiological example of the ailment and pace of COVID-2019 cases in India.
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Performance of deep learning vs machine learning in plant leaf disease detection
TL;DR: This article is comparing the performance of ML (Support Vector Machine, Random Forest), Random Forest, Stochastic Gradient Descent (SGD), & DL (Inception-v3, V GG-16, VGG-19) in terms of citrus plant disease detection as DL methods perform better than that of ML methods in case of disease detection.
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Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling
Vu Viet Nguyen,Binh Thai Pham,Ba Thao Vu,Indra Prakash,Sudan Jha,Himan Shahabi,Ataollah Shirzadi,Dong Nguyen Ba,Raghvendra Kumar,Jyotir Moy Chatterjee,Dieu Tien Bui +10 more
TL;DR: Results indicate that the RFBFDT is a promising hybrid machine learning approach for landslide susceptibility modeling and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction.
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Collaborative handshaking approaches between internet of computing and internet of things towards a smart world: a review from 2009–2017
TL;DR: It has been concluded that for the social consensus, IoC and IoT cooperating together are truly beneficial for upcoming years rather than just devices equipped with many sensor technologies.