INTELLIGENT HOME: EMPOWERING SMART HOME WITH MACHINE LEARNING FOR USER ACTION PREDICTION
Keywords: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.
Abbas, A. F., & Abdullah, M. Z. (2021). Design and Implementation of Tracking a user’s Behavior in a Smart Home. IOP Conference Series: Materials Science and Engineering, 1094(1), 012008. https://doi.org/10.1088/1757-899x/1094/1/012008
Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021). Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications, 175(February), 114820. https://doi.org/10.1016/j.eswa.2021.114820
Car, Z., Baressi Šegota, S., Anđelić, N., Lorencin, I., & Mrzljak, V. (2020). Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron. Computational and Mathematical Methods in Medicine, 2020. https://doi.org/10.1155/2020/5714714
Choi, W., Kim, J., Lee, S. E., & Park, E. (2021). Smart home and internet of things: A bibliometic study. Journal of Cleaner Production, 301, 126908. https://doi.org/10.1016/j.jclepro.2021.126908
Crisnapati, P. N., Wardana, I. N. K., & Aryanto, I. K. A. A. (2016). Rudas: Energy and sensor devices management system in home automation. Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016, 184–187. https://doi.org/10.1109/TENCONSpring.2016.7519401
Desai, M., & Shah, M. (2021). An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical EHealth, 4, 1–11. https://doi.org/10.1016/j.ceh.2020.11.002
Gao, X., & Li, G. (2020). A KNN Model Based on Manhattan Distance to Identify the SNARE Proteins. IEEE Access, 8, 112922–112931. https://doi.org/10.1109/ACCESS.2020.3003086
Géron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. In O’Reilly Media, Inc (Second Edi). O’Reilly Media, Inc.
Gota, D. I., Puscasiu, A., Fanca, A., Miclea, L., & Valean, H. (2020). Smart home automation system using Arduino microcontrollers. 2020 22nd IEEE International Conference on Automation, Quality and Testing, Robotics - THETA, AQTR 2020 - Proceedings. https://doi.org/10.1109/AQTR49680.2020.9129989
Heidari, A. A., Faris, H., Mirjalili, S., Aljarah, I., & Mafarja, M. (2020). Ant lion optimizer: Theory, literature review, and application in multi-layer perceptron neural networks. Studies in Computational Intelligence, 811, 23–46. https://doi.org/10.1007/978-3-030-12127-3_3
Ibrahim, A. K., Hassan, M. M., & Ali, I. A. (2022). Smart Homes for Disabled People: A Review Study. Science Journal of University of Zakho, 10(4), 213–221. https://doi.org/10.25271/sjuoz.2022.10.4.1038
Iqbal, A., Ullah, F., Anwar, H., Kwak, K. S., Imran, M., Jamal, W., & Rahman, A. ur. (2018). Interoperable Internet-of-Things platform for smart home system using Web-of-Objects and cloud. Sustainable Cities and Society, 38, 636–646. https://doi.org/10.1016/j.scs.2018.01.044
Jabbar, W. A., Alsibai, M. H., Amran, N. S. S., & Mahayadin, S. K. (2018). Design and Implementation of IoT-Based Automation System for Smart Home. 2018 International Symposium on Networks, Computers and Communications, ISNCC 2018, November 2018, 1–6. https://doi.org/10.1109/ISNCC.2018.8531006
Jabbar, W. A., Kian, T. K., Ramli, R. M., Zubir, S. N., Zamrizaman, N. S. M., Balfaqih, M., Shepelev, V., & Alharbi, S. (2019). Design and Fabrication of Smart Home with Internet of Things Enabled Automation System. IEEE Access, 7, 144059–144074. https://doi.org/10.1109/ACCESS.2019.2942846
Jolles, J. W. (2021). Broad-scale applications of the Raspberry Pi: A review and guide for biologists. Methods in Ecology and Evolution, 12(9), 1562–1579. https://doi.org/10.1111/2041-210X.13652
Jung, A. (2022). Machine Learning: Foundations, Methodologies, and Applications. Springer Nature.
Klok, H., & Nazarathy, Y. (2021). Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. Springer Nature. https://statisticswithjulia.org
Kubat, M., & An. (2021). An Introduction to Machine Learning. In Springer Nature (Third Edit). Springer Nature. https://doi.org/10.1002/9781119720492.ch7
Kurniawan, A. (2019). Introduction to Raspberry Pi. In Raspbian OS Programming with the Raspberry Pi (pp. 1–25). Apress, Berkeley, CA. https://doi.org/https://doi.org/10.1007/978-1-4842-4212-4_1
Li, J., Gao, F., Lin, S., Guo, M., Li, Y., Liu, H., Qin, S., & Wen, Q. (2023). Quantum K-fold cross-validation for nearest neighbor classification algorithm. Physica A: Statistical Mechanics and Its Applications, 611, 128435. https://doi.org/10.1016/j.physa.2022.128435
Mahmoodzadeh, A., Mohammadi, M., Daraei, A., Farid Hama Ali, H., Kameran Al-Salihi, N., & Mohammed Dler Omer, R. (2020). Forecasting maximum surface settlement caused by urban tunneling. Automation in Construction, 120(July), 103375. https://doi.org/10.1016/j.autcon.2020.103375
Marikyan, D., Papagiannidis, S., & Alamanos, E. (2019). A systematic review of the smart home literature: A user perspective. Technological Forecasting and Social Change, 138(November 2017), 139–154. https://doi.org/10.1016/j.techfore.2018.08.015
Mehmood, F., Ullah, I., Ahmad, S., & Kim, D. H. (2019). Object detection mechanism based on deep learning algorithm using embedded IoT devices for smart home appliances control in CoT. Journal of Ambient Intelligence and Humanized Computing, 0123456789. https://doi.org/10.1007/s12652-019-01272-8
Mehmood, R., & Selwal, A. (2020). Fingerprint biometric template security schemes: Attacks and countermeasures. In Lecture Notes in Electrical Engineering (Vol. 597). https://doi.org/10.1007/978-3-030-29407-6_33
Mienye, I. D., Sun, Y., & Wang, Z. (2019). Prediction performance of improved decision tree-based algorithms: A review. Procedia Manufacturing, 35, 698–703. https://doi.org/10.1016/j.promfg.2019.06.011
Mukherjee, A., Mondal, S., Chaki, N., & Khatua, S. (2019). Naive bayes and decision tree classifier for streaming data using hbase. In Advances in Intelligent Systems and Computing (Vol. 897). Springer Singapore. https://doi.org/10.1007/978-981-13-3250-0_8
Nikou, S. (2019). Factors driving the adoption of smart home technology: An empirical assessment. Telematics and Informatics, 45(September), 101283. https://doi.org/10.1016/j.tele.2019.101283
Okorie, P. U., Ibraim, A. A., & Auwal, D. (2020). Design and Implementation of an Arduino Based Smart Home. HORA 2020 - 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings, October 2012. https://doi.org/10.1109/HORA49412.2020.9152922
Pajankar, A. (2021). Introduction to Raspberry Pi. In Practical Linux with Raspberry Pi OS. Apress, Berkeley, CA. https://doi.org/https://doi.org/10.1007/978-1-4842-6510-9_1
Paredes-Valverde, M. A., Alor-Hernández, G., García-Alcaráz, J. L., Salas-Zárate, M. del P., Colombo-Mendoza, L. O., & Sánchez-Cervantes, J. L. (2020). IntelliHome: An internet of things-based system for electrical energy saving in smart home environment. Computational Intelligence, 36(1), 203–224. https://doi.org/10.1111/coin.12252
Patel, H. H., & Prajapati, P. (2018). Study and Analysis of Decision Tree Based Classification Algorithms. International Journal of Computer Sciences and Engineering.
Pavithra, D., & Balakrishnan, R. (2015). IoT based monitoring and control system for home automation. 2015 Global Conference on Communication Technologies (GCCT). https://doi.org/10.1109/GCCT.2015.7342646
Peng, Y., Peng, J., Li, J., & Yu, L. (2019). Smart Home System Based on Deep Learning Algorithm. Journal of Physics: Conference Series, 1187(3). https://doi.org/10.1088/1742-6596/1187/3/032086
Raju, L., Sowmya, G., Srividhya, S., Surabhi, S., Retika, M. K., & Reshmika Janani, M. (2021). Advanced home automation using raspberry pi and machine learning. Proceedings of the 7th International Conference on Electrical Energy Systems, ICEES 2021, 600–605. https://doi.org/10.1109/ICEES51510.2021.9383738
Raspberry Pi Documentation. (n.d.). Retrieved December 14, 2022, from https://www.raspberrypi.com/documentation/computers/raspberry-pi.html#gpio-and-the-40-pin-header
Saleem, A. A., Hassan, M. M., & Ali, I. A. (2022). Smart Homes Powered by Machine Learning: A Review. Proceedings of the 2nd 2022 International Conference on Computer Science and Software Engineering, CSASE 2022, 355–361. https://doi.org/10.1109/CSASE51777.2022.9759682
Sarker, I. H. (2021a). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3). https://doi.org/10.1007/s42979-021-00592-x
Sarker, I. H. (2021b). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 1–21. https://doi.org/10.1007/s42979-021-00592-x
Sunrise and sunset Zakho Dahuk Iraq. (n.d.). Retrieved January 7, 2022, from https://www.weatheravenue.com/en/asia/iq/dahuk/zakho-sunrise.html
Taiwo, O., Ezugwu, A. E., Oyelade, O. N., & Almutairi, M. S. (2022). Enhanced Intelligent Smart Home Control and Security System Based on Deep Learning Model. Wireless Communications and Mobile Computing, 2022. https://doi.org/10.1155/2022/9307961
Tatsat, H., Puri, S., & Lookabaugh, B. (2020). Machine Learning and Data Science Blueprints for Finance. In O’Reilly Media (First Edit). O’Reilly Media, Inc.
van Engelen, J. E., & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2), 373–440. https://doi.org/10.1007/s10994-019-05855-6
How to Cite
Copyright (c) 2023 Ayad A. Saleem, Masoud M. Hassan, Ismael A. Ali
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-SA 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work, with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online.