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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Emerging Science Journal
Sayeed, Md. Shohel
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Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science

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Heat stroke prediction: a perspective from the internet of things and machine learning approach Ke Yin, Lim; Yogarayan, Sumendra; Abdul Razak, Siti Fatimah; Ali Bukar, Umar; Sayeed, Md. Shohel
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3427-3433

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-threatening condition occurs when the body’s temperature exceeds 104 degrees Fahrenheit (40 degrees Celsius). 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 heat stroke at an early stage.
IoT-enabled Edge Impulse approach for heat stress prediction in outdoor settings Ke Yin, Lim; Yogarayan, Sumendra; Abdul Razak, Siti Fatimah; Sayeed, Md. Shohel; Bukar, Umar Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3934-3944

Abstract

Several international organizations of public health or countries have predicted the rise of heat-related illness cases due to climate change, which result high environment temperature. Previous studies of heat-related illness prediction using internet of things (IoT) and machine learning (ML) are mainly focusing on early detection or prediction of heat stroke incidence. To overcome the problem of heat stress prediction in outdoor settings, especially for an individual, the objective of this study is to identify a prediction method for heat stress using IoT technology and analyze the accuracy of the identified prediction model. Arduino nano 33 BLE sense with Bluetooth low energy (BLE) connectivity, HTS221 embedded environment temperature and humidity sensor, MLX90614 skin temperature sensor, and MAX30100 heart rate sensor were used to build IoT based wearable device. Besides, Python language is used for data pre-processing and data labelling after getting the sensor data from wearable device. Lastly, model training using neural network algorithms was directed in Edge Impulse. The result shows 94.6% of training accuracy with the loss of 0.27 and 90.22% of accuracy in testing set.
Assessing the Impact of Ghost Car Attacks on Traffic Flow in Vehicular Ad Hoc Networks Drahman, Isyraf Nazmi; Yogarayan, Sumendra; Abdul Razak, Siti Fatimah; Sayeed, Md. Shohel; Abdullah, Mohd. Fikri Azli; Kannan, Subarmaniam; Azman, Afizan
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-024

Abstract

Vehicular Ad Hoc Networks (VANETs) play a crucial role in enhancing road safety, traffic management, and driving efficiency through real-time communication between vehicles and infrastructure. However, VANETs are vulnerable to various security threats, one of which is the “ghost car” attack. In this attack, a malicious entity injects false information into the network, simulating the presence of a non-existent or “ghost” vehicle. This can lead to severe consequences such as traffic disruptions, accidents, and a compromised trust in the system’s reliability. This study aims to simulate and analyze the impacts of ghost car attacks on Vehicular Ad Hoc Networks (VANETs), focusing specifically on intersection waiting times and overall traffic flow. We used Simulation of Urban Mobility (SUMO) integrated with ns-3 for realistic VANET simulations, introducing varying numbers of ghost vehicles. Results indicate significant increases in waiting times and vehicle counts at intersections due to ghost cars, leading to traffic disruptions. This study evaluates ghost car attacks within realistic urban scenarios and proposes targeted detection and mitigation strategies, leveraging authentication, machine learning, and blockchain technologies.
Co-Authors Abdul Razak, Siti Fatimah Abdullah, Mohd. Fikri Azli Ali Bukar, Umar Azman, Afizan Bukar, Umar Ali Drahman, Isyraf Nazmi Kannan, Subarmaniam Ke Yin, Lim Yogarayan, Sumendra