• Ayad A. Saleem Technical College of Petroleum and Mineral Sciences, Duhok Polytechnic University, Zakho, Kurdistan Region, Iraq
  • Masoud M. Hassan CCNP Research Lab, Department of Computer Science, Faculty of Science, Zakho, Kurdistan Region, Iraq
  • Ismael A. Ali CCNP Research Lab, Department of Computer Science, Faculty of Science, Zakho, Kurdistan Region, Iraq



Smart home, Machine Learning, Raspberry Pi, Decision Tree, K-Nearest Neighbors, Multi-Layer Perceptron, ANN, User Behavior


Smart homes is an emerging technology that is transforming the way people live and interact with their homes. These homes are equipped with various devices and technologies that allow the homeowner to control, monitor, and automate various aspects of their home. This can include lighting, heating and cooling, security systems, and appliances. However, to enhance the efficiency of these homes, machine learning algorithms can be utilized to analyze the data generated from the home environment and adapt to user behaviors. This paper proposes a smart home system empowered by machine learning algorithms for enhanced user behavior prediction and automation. The proposed system is composed of three modes, including manual, automatic, and intelligent, with the objectives of maximizing security, minimizing human effort, reducing power consumption, and facilitating user interaction. The manual mode offers control and monitoring capabilities through a web-based user interface, accessible from anywhere and at any time. The automatic mode provides security alerts and appliances control to minimize human intervention. Additionally, the intelligent mode employs machine learning classification algorithms, such as decision tree, K-nearest neighbors, and multi-layer perceptron, to track and predict user actions, thereby reducing user intervention and providing additional comfort to homeowners. Experiments conducted employing the three classifiers resulted in accuracies of 97.4%, 97.22%, and 97.36%, respectively. The proposed smart home system can potentially enhance the quality of life for homeowners while reducing energy consumption and increasing security.

Author Biographies

Ayad A. Saleem, Technical College of Petroleum and Mineral Sciences, Duhok Polytechnic University, Zakho, Kurdistan Region, Iraq

Ayad Abdulrahman Saleem was born in the city of Mosul, northern Iraq, in 1989. He obtained his bachelor's degree in computer science from the University of Zakho, Iraq, in 2016 and is currently pursuing his master's degree in the same field at the same university, expected to be completed in 2023. He works as a lecturer in the Department of Information Technology at the Duhok Polytechnic University, Duhok, Iraq, and his research interests include IoT, smart homes, and machine learning.

Masoud M. Hassan, CCNP Research Lab, Department of Computer Science, Faculty of Science, Zakho, Kurdistan Region, Iraq

Masoud M. Hassan was born in Zakho, Kurdistan Region of Iraq in 1982. He received the B.Sc. degree in Statistics and Informatic in 2005 from Salahaddin University, and the M.Sc. degree from the School of Mathematics and Computer Science, University of Mosul, Iraq, in 2008. He received his Ph.D. degree in Mathematics and Statistics from University of Sheffield, Sheffield, UK, in 2016. Dr. Masoud M. Hassan is currently working as a lecturer at the Department of Computer Science, University of Zakho. He does research in Statistical modelling, Machine Learning, Information Science, Data Mining and Artificial Intelligence.

Ismael A. Ali, CCNP Research Lab, Department of Computer Science, Faculty of Science, Zakho, Kurdistan Region, Iraq

Dr. Ismael A. Ali is a computer science lecturer at the department of computer science at the University of Zakho (UoZ). Before joining the UoZ, Dr. Ismael Ali earned his BSc and MSc degrees in computer science from the University of Mosul (UOM) in 2007 and 2010 respectively. After that, he joined the UoZ as a faculty member in 2010, in the department of computer science, teaching programming and computer network courses. In 2012, Dr. Ismael Ali was awarded with the HCDP-KRG scholarship to study his PhD degree abroad. Afterward, he earned his PhD degree in computer science from Kent State University (KSU) in Ohio-USA. Dr. Ismael Ali has been an active organizer and workshop presenter for multiple conferences and journals locally and internationally. His research of interest includes; Semantic Computing, Brain-Computer Interface (BCI), Social Network Analysis and AI. He is currently the principal researcher at the CCNP research lab at the UoZ.


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How to Cite

Saleem, A. A., Hassan, M. M., & Ali, I. A. (2023). INTELLIGENT HOME: EMPOWERING SMART HOME WITH MACHINE LEARNING FOR USER ACTION PREDICTION. Science Journal of University of Zakho, 11(3), 403–420.



Science Journal of University of Zakho