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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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Issue Published : 119
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| Paper Title: | AI-Driven Behavioural Analytics and Temporal Pattern Recognition for Time-Leak Detection and Productivity Optimization |
| Authors Name: | Dr. ShivaKumar C , Suyog Lal Shrestha , Sampurna Khaparde , Anjali Shreya |
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IJRTI_211143
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| Published Paper Id: | IJRTI2604050 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Nowadays keeping track of hours feels harder, especially with phones buzzing nonstop. Social feeds, video clips, games - they pull attention without warning. Hours slip away while tasks pile up quietly nearby. Performance dips, grades dip too, tension builds slowly like fog at dawn. Some call it time bleeding out - tiny moments lost to things that add little value. What fills the day does not always fill purpose. One way to tackle the problem begins with a method called ATLDA, mixing stats-based features and machine learning to examine how people act. User actions feed into the setup - things like studying, playing games, scrolling social apps, working out, resting, or watching videos. What matters comes next: a score named TLS emerges through math that weighs activities by their impact on output. Behaviour shifts get noticed thanks to a built-in clock-like element adjusting as patterns evolve across days. One way this setup works is by using a Decision Tree to guess how productive someone might be, while K-Means spots habits in how people act. Pie charts show up here, bar graphs there - anything to make the data easier to grasp at a glance. Tests ran long enough to confirm it catches where minutes slip away, then suggests small changes to handle time better. It grows without breaking, smart without showing off, fits classrooms just as well as offices. What stands out isn’t speed or flashiness - it’s staying useful when real days play out. |
| Keywords: | Time Management, ATLDA, Time Leakage Score (TLS), Machine Learning, Decision Tree, K-Means Clustering, Productivity Analysis, Behavioural Analytics, Adaptive Systems |
| Cite Article: | "AI-Driven Behavioural Analytics and Temporal Pattern Recognition for Time-Leak Detection and Productivity Optimization", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a358-a367, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604050.pdf |
| Downloads: | 00081 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604050
Registration ID:211143
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a358-a367 Country: Ramanagar, Karnataka, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604050 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604050 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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