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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM38764

Women’s Security and Smart Surveillance

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM38764

Volume:

8 |

11 |

Nov 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM38764

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MPS

Mhaske Pragati Sambhaji

PSB

Patil Sakshi Balraje

SAR

Sable Aishwarya Ramhari

SDB

Sawant Dipali Bhagwat

DBH

Dinesh Bhagwan Hanchate

SAK

Swati Amol Kadam

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Abstract

In a time where women’s safety is concerned, there is this recommendation of having a total system of security for women with a deep learning technique, helping increase hazard detection and personal protection that uses powerful algorithms of computer vision along with machine learning models suggested system monitors the identification of a threat in real-time the primary constituents of this system are the facial recognition which identifies the known threats, the object detection that recognizes the suspicious activity, an alarm mechanism alerting authorities or emergency contact in case there is any potential hazard scanned the deep learning framework helps it learn through big data; this is how its accuracy and adaptability come along with several situations and more importantly user- friendly interface of access in order to enjoy safe features of location tracking and prime alerts.

KEYWORDS

Women’s Security Facial recognition Object detection Location tracking Deep learning

Cite This Article

Mhaske Pragati Sambhaji, Patil Sakshi Balraje, Sable Aishwarya Ramhari, Sawant Dipali Bhagwat, Dinesh Bhagwan Hanchate, Swati Amol Kadam, "Women’s Security and Smart Surveillance," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 11, Nov 2024. https://doi.org/10.55041/IJSREM38764

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Publication Date

11/15/2024

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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