eprintid: 10145
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/01/45
datestamp: 2023-11-09 16:36:46
lastmod: 2023-11-09 16:36:46
status_changed: 2023-11-09 16:30:42
type: conference_item
metadata_visibility: show
creators_name: Faraz, S.
creators_name: Ali, S.S.A.
creators_name: Adil, S.H.
title: Machine Learning and Stress Assessment: A Review
ispublished: pub
keywords: Brain; Computer aided diagnosis; Computer aided instruction; Neurology; Risk assessment, Brain activity; Heart attack; Learning assessment; Machine-learning; Medical experts; Stress assessment, Machine learning
note: cited By 5; Conference of 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018 ; Conference Date: 21 December 2018 Through 22 December 2018; Conference Code:145373
abstract: Stress assessment has been considered essentials in the early stages because stress-related abnormalities tend to increase the risk of strokes, heart attacks, depression, and hypertension. This may also induce suicidal thought within the victims of this neurological state. The CAD (Computer Aided Diagnosis) have been a way forward for both medical experts and people with complications. The recent development of Machine learning revolution has proved to be substantial for medical diagnosis and prediction. This approach can further be used with neurological tools. The initial status of the brain activities would act as a window into the brain; which can be used as an insight. With the influence of machine learning more generalized way of discriminating stress activities with other normal activities can be possible. © 2018 IEEE.
date: 2018
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063463913&doi=10.1109%2fICEEST.2018.8643313&partnerID=40&md5=13f210f328c47ecb580086e7d3a348da
id_number: 10.1109/ICEEST.2018.8643313
full_text_status: none
publication: 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018
refereed: TRUE
isbn: 9781538682494
citation:   Faraz, S. and Ali, S.S.A. and Adil, S.H.  (2018) Machine Learning and Stress Assessment: A Review.  In: UNSPECIFIED.