Alam, Md. Golam RabiulRahman, RafeedUz Zaman, NurAkther, AfrozaTabassum, NowshinSamrat, Md. Khaliduzzaman KhanKhan, Swad Mustasin2023-12-062023-12-0620232023-05ID 19301052ID 19301076ID 19301251ID 19301114ID 19101599http://hdl.handle.net/10361/21930Cataloged from PDF version of thesis.Includes bibliographical references (pages 51-54).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.The widespread use of smartphones has raised concerns about problematic smartphone use or addiction, which has become a significant issue in today’s society. Despite the recognition of this research area, detecting smartphone addiction remains a challenge. Therefore, it is crucial to identify the primary causes of smartphone addiction and understand how individuals’ lifestyles contribute to this behavior. Most of the methods in research area are self assessment based and detected via different addiction scales. Moreover, in previous studies daily human activities was never considered as a factor in problematic smartphone use. This study aims to explore a new approach in detecting excessive smartphone usage by considering the impact of sensor based daily activities and smartphone app usage. By examining addictive characteristics of smartphone usage and clustering them based on various independent variables, we sought to determine smartphone addiction and investigate the influence of daily activities. To collect reliable and accurate data, we utilized apps for seven days to capture information on the participants’ smartphone usage. Leveraging sensor data and LSTM models, we identified participants’ activities and correlated them with daily app usage duration to detect smartphone addiction using clustering methods such as K-Means and K-Medoids. Our analysis revealed that around 28% participants showed addicted behaviour. To validate these findings, we compared our result with survey results using diverse evaluation metrics (RI,FMI), which exhibited 87% accuracy.54 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.SmartphoneAddictionAppUsageActivitySensorClusterMobile computingHuman activity recognitionLocation-based servicesThesis