مرکز منطقه ای اطلاع رسانی علوم و فناوری Journal of Information Systems and Telecommunication (JIST) 2322-1437 12 46 2024 6 24 A Comparison Analysis of Conventional Classifiers and Deep Learning Model for Activity Recognition in Smart Homes based on Multi-label Classification A Comparison Analysis of Conventional Classifiers and Deep Learning Model for Activity Recognition in Smart Homes based on Multi-label Classification 127 137 10.61186/jist.36294.12.46.127 en John Kasubi The University of Dodoma Manjaiah D. Huchaiah Mangalore University Ibrahim Gad Tanta Mohammad Kazim Hooshmand Mangalore University 2022 3 15 Activity Recognition is essential for exploring the various activities that humans engage in within Smart Homes in the presence of multiple sensors as residents interact with household appliances. Smart homes use intelligent IoT devices linked to residents' homes to track changes in human behavior as the humans interact with the home's equipment, which may improve healthcare and security issues for the residents. This study presents a research work that compares conventional classifiers such as DT, LDA, Adaboost, GB, XGBoost, MPL, KNN, and DL, focusing on recognizing human activities in Smart Homes using Activity Recognizing Ambient Sensing (ARAS). The experimental results demonstrated that DL Model outperformed with excellent accuracy compared to conventional classifiers in recognizing human activities in Smart Homes. This work proves that DL Models perform best in analyzing ARAS datasets compared to traditional machine learning algorithms. http://jist.ir/ar/Article/Download/36294