INTELLIGENT HOME - EMPOWERING SMART HOME WITH MACHINE LEARNING FOR USER ACTION PREDICTION

Aug 3, 2026

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Smart homes represent an emerging technological shift that changes how people live and interact with their households by combining interconnected devices for control, monitoring, and automation. To improve efficiency, machine learning algorithms are used to analyze environmental data and adapt to user behavior. This paper presents a smart home system with three operational modes—manual, automatic, and intelligent—aimed at maximizing security, reducing human effort, lowering power consumption, and improving interaction. The intelligent mode applies classification models including decision tree, K-nearest neighbors, and multi-layer perceptron to track and predict actions. Experiments report high accuracies, indicating strong potential for enhanced comfort, energy savings, and safer living.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Internet of Things and smart home context

Motivations: automation, energy efficiency, and security

Industry 4.0 and machine learning foundations

INTELLIGENT HOME: EMPOWERING SMART HOME WITH MACHINE LEARNING FOR USER ACTION PREDICTION Ayad A. Saleem a,*, Masoud M. Hassan b, Ismael A. Ali b a Technical College of Petroleum and Mineral Sciences, Duhok Polytechnic University, Zakho, Kurdistan Region, Iraq [ayad.abdulrahman@dpu.edu.krd](ayad.abdulrahman@dpu.edu.krd) b CCNP Research Lab, Department of Computer Science, Faculty of Science, Zakho, Kurdistan Region, Iraq- (masoud.hassan, ismael.ali)@[uoz.edu.krd](uoz.edu.krd) Received: 28 Mar., 2023 / Accepted: 14 May, 2023 / Published: 15 Aug. 2023 [https://doi.org/10.25271/sjuoz.2023.11.3.1145](https://doi.org/10.25271/sjuoz.2023.11.3.1145) ABSTRACT: 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. KEYWORDS: Smart home, Machine Learning, Raspberry Pi, Decision Tree, K-Nearest Neighbors, Multi-Layer Perceptron, ANN, User Behavior. 1. INTRODUCTION The remarkable advancement in technology has facilitated the ability to establish a connection between any device and the Internet, thus giving rise to the notion of the Internet of Things (IoT) . The IoT refers to a network of internet-connected devices and objects that range from simple household appliances to complex machinery in various settings (Saleem et al., 2022) . These devices collect and share data, which enables them to function seamlessly and offer insights into different aspects of human life (Ibrahim et al., 2022; Taiwo et al., 2022) . The integration of IoT has led to enhanced efficiency, convenience, and optimization in both residential and commercial settings, resulting in increased revenue and improved customer service for businesses. The application areas for the IoT encompass various fields such as healthcare, smart homes, smart cities, industrial automation, and transportation. Among these, and since the smart home pertains more closely to individuals' daily lives, the smart home has garnered significant interest from both the industrial and academic communities (Jabbar et al., 2018) . The use of smart homes is growing rapidly and is expected to continue to grow in the future as technology continues to advance and become more accessible. The development of smart homes is being driven by the increasing demand for home automation and the growing need for energy efficiency. However, there are challenges associated with the implementation of smart home

FAQs

What is the main goal of the proposed intelligent home system?

To use machine learning to predict user actions and automate home functions while maximizing security, minimizing human effort, reducing power consumption, and improving user interaction.

What operating modes does the system include?

The system is designed with three modes: manual, automatic, and intelligent.

Which machine learning classifiers are used for user action prediction?

Decision tree, K-nearest neighbors, and multi-layer perceptron are used to track and predict user actions.

Aug 3, 2026

Upload by Patrick