Human stress detection in and through sleep patterns using machine learning algorithms (Record no. 22862)

MARC details
000 -LEADER
fixed length control field a
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250515113258.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250515b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 26203
Author Geetha, R.
245 ## - TITLE STATEMENT
Title Human stress detection in and through sleep patterns using machine learning algorithms
250 ## - EDITION STATEMENT
Volume, Issue number Vol.105(6), Dec
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Mumbai
Name of publisher, distributor, etc. Springer
Year 2024
300 ## - PHYSICAL DESCRIPTION
Pagination 1691-1713p.
520 ## - SUMMARY, ETC.
Summary, etc. Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, and problem-solving abilities. As a result, stress detection and modeling have become important areas of study in both psychology and computer science. This study links the fields of psychology and machine learning to deal with the urgent requirement of accurate stress detection methodologies and highlights sleep patterns as a key indicator for stress detection, discussing a novel approach to understand and determine stress levels. Psychologists use affective states to measure stress, which refers to a sense of feeling an underlying emotional state. However, most stress classification work has been limited to user-dependent models, which new users cannot use without additional training. This can be a significant time burden for new users trying to predict their affective states. Therefore, it is critical to address basic mental health issues in children and adults to prevent them from developing more complex problems on account of undergoing stress.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4619
Topical term or geographic name entry element EXTC Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 26204
Co-Author Gunanandhini, S.
773 0# - HOST ITEM ENTRY
International Standard Serial Number 2250-2106
Title Journal of the institution of engineers (India): Series B
856 ## - ELECTRONIC LOCATION AND ACCESS
URL https://link.springer.com/article/10.1007/s40031-024-01079-y
Link text Click here
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Articles Abstract Database
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     School of Engineering & Technology School of Engineering & Technology Archieval Section 15/05/2025   2025-0834 15/05/2025 15/05/2025 Articles Abstract Database
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