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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/19834
Title: MM Big Data Applications: Statistical Resultant Analysis of Psychosomatic Survey on Various Human Personality Indicators
Authors: Rastogi R.
Chaturvedi D.K.
Satya S.
Arora, Navneet
Trivedi P.
Gupta M.
Singhal P.
Gulati M.
Sahana S.K.
Bhattacharjee V.
Published in: Advances in Intelligent Systems and Computing
Proceedings of 2nd International Conference on Computational Intelligence, ICCI 2018
Abstract: Machines are getting intelligent day by day. Nowadays, the human being is so much complex that nobody can do any concrete or accurate behavioural or action-related predictions. The mood swing has been also an epidemic in modern India and all metro cities of the developed countries. We have grown rich in terms of money and physical facilities; modern science has gifted us many boons, but simultaneously the mental, physical and spiritual disorders have surprisingly disturbed the smile, peace and definite attitude and life style of individual and all human beings. The 3 M—Mobile, Money and Marriage are essential in one’s life, but the over addiction and misuse of resources, facilities and lack of right understanding with selfishness and ungrateful conduct have changed all the social parameters. So, presently the stress has been the biggest challenge against mankind like nuclear weapons, global warming and epidemics. It leads towards tension, frustration and depression and ultimately in extreme cases towards the self-suicide or the murder of innocents. The happiness index, safety of individual, living parameters have been drastically challenged us, and India specially has pathetic situation among global quality of life (QoL) index. The present paper is an effort to define a simulated model and framework for the subjective quality of stress into quantitative parameters and mathematically analysing it with the help of popular machine learning tools and applied methods. With the help of machine intelligence, authors are trying to establish a framework which may work as an expert system and may help the individual to grow self as a better human being.
Citation: Advances in Intelligent Systems and Computing, 2020, 303- 325
URI: https://doi.org/10.1007/978-981-13-8222-2_25
http://repository.iitr.ac.in/handle/123456789/19834
Issue Date: 2020
Publisher: Springer Verlag
Keywords: Anaconda
Machine learning
Multinomial logistic regression
Pycham
Python
Stress
Use case
Variance
Big data
Expert systems
Global warming
Learning systems
Machine learning
Stresses
Anaconda
Multinomial logistic regression
Pycham
Python
Variance
Nuclear weapons
ISBN: 9.78981E+12
ISSN: 21945357
Author Scopus IDs: 57192103103
12809516500
55897679400
15130971300
57210121940
57209260997
57213963786
57209249944
Author Affiliations: Rastogi, R., Department of Computer Science and Engineering, ABESEC, Ghaziabad, India
Chaturvedi, D.K., Department of Electrical Engineering, DEI, Agra, India
Satya, S., Department of Rural Development, IIT-Delhi, New Delhi, India
Arora, N., Department
Corresponding Author: Rastogi, R.; Department of Computer Science and Engineering, India; email: rohit.rastogi@abes.ac.in
Appears in Collections:Conference Publications [ME]

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