Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2075 2019 2019 Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq Zainab.R Zainab.R.Abdulsada Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq Waleed Hameed Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq Zainab.R Zainab.R.Abdulsada Computer Technologies Engineering, Al-Turath University College, Baghdad,Iraq Noora Hani Sherif Department of Computer Technical Engineering, Technical Engineering College, Al-Ayen University, Thi- Qar, Iraq Noor Hanoon Haroon Laboratory of Biophysics and Medical Technology, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, Tunis 1006, Tunisia Narjes Benameur Faculty of Information & Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia M. A. Burhanuddin Recognition and modelling of driver behavior (DB) have lately been crucial in intelligence transportation systems (ITS), human-vehicle, and intelligent vehicle systems (IVS). The evidence that drivers are distracted most often causes accidents and incidents involving vehicles is growing. Using camera sensors in the vehicle or sensors worn by the driver can help detect and prevent drivers from engaging in distracting behaviors like talking on the phone, eating, drinking, adjusting the radio, interacting with navigation systems, or even combing their hair while behind the wheel. However, this system requires a lightweight data processing module and a powerful training module for real-time detection. Data must be collected from certain cameras or wearable sensors to detect distracted drivers and ensure a rapid reaction from the administrator on safe driving. Therefore, this paper suggests a Machine Learning Driver Distraction Prediction Model (MLDDPM) with a decision-support system (DSS) that can alert the driver to possible dangers on the road by analyzing both internal (vehicle parameters) and external (road infrastructure messages) data. This research MLDDPM employed semi-supervised algorithms to reduce the expense of labelling training data for driver attention detection in actual driving scenarios. Two attentive and cognitively distracted driving states were used to assess support vector machines: i) as a supplementary parameter for the aggregate risk assessment of driving and ii) as a parameter for providing the driver with the most appropriate message type on possible road dangers. Finding the optimal approach to driver assistance to guarantee secure transportation is the primary goal of this work. 2023 2023 108 119 10.54216/JISIoT.090208 https://www.americaspg.com/articleinfo/18/show/2075