UGC Approved Journal no 63975(19)
New UGC Peer-Reviewed Rules

ISSN: 2349-5162 | ESTD Year : 2014
Volume 12 | Issue 10 | October 2025

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Published in:

Volume 12 Issue 5
May-2025
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

Unique Identifier

Published Paper ID:
JETIR2505098


Registration ID:
561100

Page Number

a904-a907

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Title

Ai-Driven Women Safety Surveillance: Real-Time Threat Identification And Automated Emergency Response.

Abstract

Women’s safety remains a critical concern in modern society, necessitating intelligent, technology-driven solutions to ensure real-time threat detection and response. AI-powered women safety analytics leverages artificial intelligence, deep learning, and computer vision to enhance security measures by identifying potential risks such as weapon possession, distress signals, and unsafe social environments. The system integrates YOLO-based object detection for recognizing weapons , Media pipe -based gesture recognition for detecting distress signals, and deep learning-based gender classification to analyze social contexts. Additionally, real-time alerts are dispatched via Telegram notifications to ensure immediate intervention, reducing potential harm. By combining these AI-driven security analytics techniques, this system offers a proactive and intelligent approach to women’s safety, minimizing response time and increasing accuracy in threat detection. Future advancements will incorporate multimodal analysis, including behavioral pattern recognition, anomaly detection, and enhanced facial recognition, to further improve security effectiveness. This AI-powered solution represents a crucial step toward safer public environments by providing real-time, data-driven security insights and automated intervention mechanisms.

Key Words

Fraud prevention, AI security, women safety, gesture recognition, deep learning, YOLO, real-time monitoring, intelligent surveillance, anomaly detection, behavioral analytics, IoT security, edge, facial recognition, crowd density analysis, smart surveillance, emergency response, cloud-based monitoring, AI-driven safety solutions.

Cite This Article

"Ai-Driven Women Safety Surveillance: Real-Time Threat Identification And Automated Emergency Response.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.a904-a907, May-2025, Available :http://www.jetir.org/papers/JETIR2505098.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Ai-Driven Women Safety Surveillance: Real-Time Threat Identification And Automated Emergency Response.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppa904-a907, May-2025, Available at : http://www.jetir.org/papers/JETIR2505098.pdf

Publication Details

Published Paper ID: JETIR2505098
Registration ID: 561100
Published In: Volume 12 | Issue 5 | Year May-2025
DOI (Digital Object Identifier):
Page No: a904-a907
Country: MEDCHAL, TELANGANA, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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