Utilizing Digital Data and Soft Computing Techniques for Comprehensive Behavioral Addiction Analysis and Monitoring

This research investigates into the topic of behavior addiction, specifically focusing on millennials’ reliance on smartphones and their fear of being without a home. It highlights how kids, even without smartphones and with moderate usage, are at risk for developing common mental health issues like a phobia toward. The reason for this is because traditional methods of diagnosis rely on lengthy and sometimes biased surveys. This chapter instead examines options based on apps which offer present time usage statistics for a more accurate evaluation. In addition to utilizing algorithms for machine learning to forecast cases of nomophobia, it tests were conducted to produce checklists for measuring dependence on smartphones. For example, “UsageStats” is an app that tracks many elements of smartphone usage for a period of time. The information accumulated was used to refine the battery of inquiries that assesses cellphone dependency; the result was a 98.61% success rate. To further improve the nomophobia forecasting systems, we also used an app called “Activity Tracker” to gather data from the individuals’ smartphones analyses together with to they’re their own assessments via an interview. According to this, SA and Nomophobia in may be better diagnosed with the use of current data from cellphone use in addition to conventional self-assess Considering how many undergraduates used their cell phones more often throughout the COVID-19 quarantine, the situation was particularly concerning.