Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Women Safety App to Detect Danger And Prevent Automatically Using Machine Learning

Authors
Kopanati Shankar1, Siripurapu Chalice Prajwal1, *, Vallem Govardhan Kumar1, Penaganti Anusha1, Relli Chandra Sekhara Kameswar1, Sunkari Bhanu Prakashn1
1Dept of CSE, Nadimpalli Satyanarayana Raju Institute of Technology, Visakhapatnam, 531173, A.P, India
*Corresponding author. Email: chaliceprajwal2@gmail.com
Corresponding Author
Siripurapu Chalice Prajwal
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_140How to use a DOI?
Keywords
women's safety; threat detection; machine learning
Abstract

This work follows the Software Development Life Cycle (SDLC) to develop a women's safety application using Java within the Android Studio environment. The application integrates audio sensing, machine learning, cloud storage, and geolocation to address women's safety concerns. It utilizes audio sensors to monitor the surroundings, employing a TensorFlow Lite model trained on audio samples to detect potential threats. The Java-based Android app responds to triggers by issuing alerts and initiating audio-video recording if unacknowledged. Geolocation determines the user's location, shared with nearby police along with cloud-stored data. Emergency contacts aid communication during crises. The app displays nearby safe places, and rigorous testing ensures accuracy, reliability, and security. In conclusion, this application enhances women's security by autonomously detecting threats, recording evidence, and notifying authorities. The integration of safe locations and systematic SDLC approach ensures reliability.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_140How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Kopanati Shankar
AU  - Siripurapu Chalice Prajwal
AU  - Vallem Govardhan Kumar
AU  - Penaganti Anusha
AU  - Relli Chandra Sekhara Kameswar
AU  - Sunkari Bhanu Prakashn
PY  - 2024
DA  - 2024/07/30
TI  - Women Safety App to Detect Danger And Prevent Automatically Using Machine Learning
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1443
EP  - 1452
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_140
DO  - 10.2991/978-94-6463-471-6_140
ID  - Shankar2024
ER  -