A Machine Learning Approach for Digital Addiction Pattern Analysis

Description

The digital content and distribution channels are increasing day by day. Due to this, people are utilizing different digital devices to access digital information and other services. However, digital devices are utilized in many ways personal or professional use. But, in both conditions, excessive and extreme usage of digital devices is subject to mental and physical health. This misfunctioning of human behavior is sometimes also called Digital Addiction. In this project work, we learned about the excessive utilization of digital device issues by evaluating the device usage or device access log. First, a device access log is created by using an Android application. The access log consists of a time stamp, application name, and type of application (personal or professional). Next, the collected user behavior data is stored in a common database. The collected data is further preprocessed to transform the data into transaction sets. Finally, a frequent pattern analysis has been carried out to identify the frequently accessed applications, time of usage, and category of application,