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Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4

Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
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ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS
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Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert
Volume-3 | Issue-2

Home / Archives / Volume-2 / Issue-4 / Article-4

Volume - 2 | Issue - 4 | december 2020

A Comparative Machine Learning Study on IT Sector Edge Nearer to Working From Home (WFH) Contract Category for Improving Productivity
Pages: 217-225
Published
08 February, 2021
Abstract

Many private companies in India offered working from home (WFH) for employees due to COVID'19 lockdown. The WFH has both merits and demerits for the employees as well as employer when it compared within office working environment. Many research works is showing many opinions about increases or decreases of productivity in the real time for any industries. This works talks about WFH impression is leads to edge nearer for the efficient productivity to any employer. In addition, the research article is providing survey of the benefits and demerits of WFH in India. In the view of the higher capacity, ultra very low level inactivity for better security is in the internetwork domain, there are lots of benefits in telework, and internet based work. The predicting development is done by Random Forest, Decision Tree, and Naïve Bayes for future with the help of three datasets. The datasets has taken from three types of general public such as city, town, and village for this research analysis. This research article is weighing up the rate of changes of productivity from the employees. Finally, this research work compares the learning method analysis includes prediction of rate of change of productivity from employees at city region. This prediction is computed by ML algorithm. Based on this prediction employers can improve and plan for their production and control the system in a better way.

Keywords

Machine learning prediction

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