Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Call for Paper Volume 7, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
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Volume 7 Issue 2
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Clustering and Risk Prediction of Gen Z’s Digital Presence

Author(s) Saketh Muthyapuwar, Koneti Sanjana, Bollam Tharun, P. Santosh Chandrika
Country India
Abstract In today's connected world, young people from Generation Z face unique challenges online, from data leaks to tricky scams. This work explores a practical system that uses smart algorithms to sort users into groups based on their habits and spot potential dangers early on. We built a setup that looks at two main areas: how much time people spend on screens and social sites, plus their basic security habits like strong passwords. By feeding this info into a decision-making tool called Random Forest, we can label risks as low, medium, or high. Meanwhile, a grouping method known as K-Means helps spot common patterns, like folks who click too many ads without thinking. We tested everything on a made-up set of over 19,000 examples, cleaning it up first to make sure the results are solid. The whole thing runs on a simple web app made with Flask, where users get easy-to read charts and tips tailored just for them. Our tests showed it gets things right about 88% of the time, proving that mixing these learning styles can really help keep kids safer online. This could grow into bigger tools that watch threats in real time, making the web a better place for the next generation.
Keywords Digital Behaviour Tracking, Youth Online Safety, Algorithm-Driven Analysis, Decision Tree Ensembles, Pattern Grouping Methods, Web-Based Interfaces, User Habits, Security Habits, Threat Forecasting, Web Monitoring Tools.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-03-31
DOI https://doi.org/10.63363/aijfr.2026.v07i02.2727
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E-ISSN 3048-7641
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