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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| Language: | en en |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| 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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| 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 |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.ump.umpir.40372 |
| institution | Universiti Malaysia Pahang |
| language | en en |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | eprints |
| 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 (Published) https://doi.org/10.1109/ICSECS58457.2023.10256369 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| 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 |
| url_provider | http://umpir.ump.edu.my/ |