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Machine learning classification to detect unattended child in vehicle using sensor signal : A review

A significant number of children die each year in the United States and around the world as a result of being left in hot vehicles. Numerous studies aimed at reducing the number of unattended children in vehicles have employed a variety of strategies. The majority of studies use sensors to detect un...

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Main Authors: Ida Fadliza, Abu Zarin, Ngahzaifa, Ab Ghani, Syafiq Fauzi, Kamarulzaman
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/40372/1/Machine%20learning%20classification%20to%20detect%20unattended%20child.pdf
http://umpir.ump.edu.my/id/eprint/40372/2/Machine%20learning%20classification%20to%20detect%20unattended%20child%20in%20vehicle%20using%20sensor%20signal_A%20review_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40372/
https://doi.org/10.1109/ICSECS58457.2023.10256369
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spelling my.ump.umpir.403722024-04-16T04:17:16Z http://umpir.ump.edu.my/id/eprint/40372/ Machine learning classification to detect unattended child in vehicle using sensor signal : A review Ida Fadliza, Abu Zarin Ngahzaifa, Ab Ghani Syafiq Fauzi, Kamarulzaman QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) A significant number of children die each year in the United States and around the world as a result of being left in hot vehicles. Numerous studies aimed at reducing the number of unattended children in vehicles have employed a variety of strategies. The majority of studies use sensors to detect unattended children, while only a few integrate machine learning with the sensors. The efficacy of a sensor's system is improved by machine learning. This paper reviews the implementation of machine learning classification in child detection systems and reviews the research conducted to detect unattended children. For the majority of the research, the machine learning algorithms SVM, KNN, and Random Forest effectively classified the occupants into a few classifications with accuracies greater than 90%. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40372/1/Machine%20learning%20classification%20to%20detect%20unattended%20child.pdf pdf en http://umpir.ump.edu.my/id/eprint/40372/2/Machine%20learning%20classification%20to%20detect%20unattended%20child%20in%20vehicle%20using%20sensor%20signal_A%20review_ABS.pdf Ida Fadliza, Abu Zarin and Ngahzaifa, Ab Ghani and Syafiq Fauzi, Kamarulzaman (2023) Machine learning classification to detect unattended child in vehicle using sensor signal : A review. In: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25-27 August 2023 , Penang. pp. 414-418. (192961). ISBN 979-835031093-1 https://doi.org/10.1109/ICSECS58457.2023.10256369
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Ida Fadliza, Abu Zarin
Ngahzaifa, Ab Ghani
Syafiq Fauzi, Kamarulzaman
Machine learning classification to detect unattended child in vehicle using sensor signal : A review
description A significant number of children die each year in the United States and around the world as a result of being left in hot vehicles. Numerous studies aimed at reducing the number of unattended children in vehicles have employed a variety of strategies. The majority of studies use sensors to detect unattended children, while only a few integrate machine learning with the sensors. The efficacy of a sensor's system is improved by machine learning. This paper reviews the implementation of machine learning classification in child detection systems and reviews the research conducted to detect unattended children. For the majority of the research, the machine learning algorithms SVM, KNN, and Random Forest effectively classified the occupants into a few classifications with accuracies greater than 90%.
format Conference or Workshop Item
author Ida Fadliza, Abu Zarin
Ngahzaifa, Ab Ghani
Syafiq Fauzi, Kamarulzaman
author_facet Ida Fadliza, Abu Zarin
Ngahzaifa, Ab Ghani
Syafiq Fauzi, Kamarulzaman
author_sort Ida Fadliza, Abu Zarin
title Machine learning classification to detect unattended child in vehicle using sensor signal : A review
title_short Machine learning classification to detect unattended child in vehicle using sensor signal : A review
title_full Machine learning classification to detect unattended child in vehicle using sensor signal : A review
title_fullStr Machine learning classification to detect unattended child in vehicle using sensor signal : A review
title_full_unstemmed Machine learning classification to detect unattended child in vehicle using sensor signal : A review
title_sort machine learning classification to detect unattended child in vehicle using sensor signal : a review
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40372/1/Machine%20learning%20classification%20to%20detect%20unattended%20child.pdf
http://umpir.ump.edu.my/id/eprint/40372/2/Machine%20learning%20classification%20to%20detect%20unattended%20child%20in%20vehicle%20using%20sensor%20signal_A%20review_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40372/
https://doi.org/10.1109/ICSECS58457.2023.10256369
_version_ 1822924225139179520
score 13.235362

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