Citation

Lim, Ke Yin and Yogarayan, Sumendra and Abdul Razak, Siti Fatimah and Ali Bukar, Umar and Sayeed, Md. Shohel (2024) Heat stroke prediction: a perspective from the internet of things and machine learning approach. International Journal of Electrical and Computer Engineering (IJECE), 14 (3). p. 3427. ISSN 2088-8708

Text Heat stroke prediction_ a perspective from the internet of things and machine learning approach _ Ke Yin _ International Journal of Electrical and Computer Engineering (IJECE).pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

With the increasing occurrence of heat-related illnesses due to rising temperatures worldwide, there is a need for effective detection and prediction systems to mitigate the risks. Heat stroke, a life-threateningcondition occurs when the body’stemperature exceeds 104 °F(40 °C). It can happen due to prolonged exposure to temperatures. When the body struggles to cool itself down adequately. The internet of things (IoT) and machine learning (ML) are two advancing technologies that have the potential to revolutionize industries and enhance our lives in numerous ways. Currently,monitoring devices are primarily used to diagnose when individuals suffering from heatstroke are at the location. This paper delves into the exploration of utilizing the IoT and ML algorithms to predict heat strokes. It reviews existing studies in this field, focusing on how IoT has been deployed and the application of machine learning techniques. The research aims to define the integration of IoT devices and ML algorithms that has a great potential to detect and predict heat-related illnesses such as heatstroke at an early stage

Item Type:	Article
Uncontrolled Keywords:	Heat stroke, Internet of things, Machine learning, Prediction, Technique
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	29 May 2024 04:13
Last Modified:	29 May 2024 04:13
URII:	http://shdl.mmu.edu.my/id/eprint/12472

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