Abstract :

Ensuring Public Safety In Rapidly Evolving Situations Requires The Ability To Detect And Respond To Critical Events As They Unfold. Traditional Monitoring Systems Often Fail To Capture Such Incidents In Real Time Due To Limited Coverage Or Delayed Reporting. To Address This Challenge, This Study Proposes An Intelligent Framework For Live Event Detection That Leverages Natural Language Processing (NLP) And Deep Learning Techniques To Identify Safety-related Occurrences From Streaming Data Sources. The System Processes Continuous Text Inputs, Applies Language Understanding Models To Extract Semantic And Contextual Cues, And Classifies Them Into Relevant Event Categories Such As Accidents, Natural Disasters, Or Violent Activities. Keywords: Live Event Detection, People’s Safety, Natural Language Processing (NLP), Deep Learning, Real-Time Monitoring, Emergency Detection, Situational Awareness, Machine Learning, Intelligent Systems, Event Recognition

Published:

24-12-2025

Issue:

Vol. 25 No. 12 (2025)

Page Nos:

445-449

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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