Abstract
In this modern era, women have shown excellence in all walks of life and stand equally with men in all sectors. So has increased violence against women due to this exposure. Hence, women security during travel is one of the critical issues faced by society, from the past and even today. The paper aims at providing a technological solution to this problem. For this, a system for forewarning and safeguarding the female cab users using machine learning is developed. An Android application is developed to collect data, and a virtual panic environment was created to collect the anomalies. The accelerometer data along the three coordinates was captured corresponding to normal and anomalies. Three machine learning models viz support vector machines, logistic regression, and Naïve Bayes are trained on data collected, and their test performance is compared in terms of accuracy. This trained model is further integrated with an Android application for real-time data collection and feedback.
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References
Is Uber Safe? How Safe are Uber and and Lyft for Women, January 14. https://www.alarms.org/uber-lyft-womens-safety-report/ (2020)
Only 9% Indian Women Feel Public Transport is Safe, But They Still Use It: Report, NEWS18. March 7. https://www.news18.com/news/buzz/indian-women-feel-public-transport-is-unsafe-but-they-still-use-it-report-2059233.html (2019)
Suma, V.: Computer vision for human-machine interaction-review. J. Trends Comput. Sci. Smart Technol. (TCSST) 1(02), 131–139 (2019)
Smys, S.: DDOS attack detection in telecommunication network using machine learning. J. Ubiquit. Comput. Commun. Technol. (UCCT) 1(01), 33–44 (2019)
Manoharan, S.: An improved safety algorithm for artificial intelligence enabled processors in self driving cars. J. Artif. Intell. 1(02), 95–104 (2019)
Nirbhaya: Be Fearless. https://digitalindia.gov.in/content/nirbhaya-app
Pandey, S., Jain, N., Bhardwaj, A., Kaur, G., Kumar, V.: Reach360: A comprehensive safety solution. In: 2017 Tenth International Conference on Contemporary Computing (IC3) 2017 Aug 10, pp. 1–3. IEEE (2017)
Yarrabothu, R.S., Thota, B.: Abhaya: An Android App for the safety of women. In: 2015 Annual IEEE India Conference (INDICON) 2015 Dec 17, pp. 1–4. IEEE (2015)
Hameed, S.A., Abdulla, S.: Effective car monitoring and tracking model. Australian. J. Basic Appl. Sci. 6(1), 1–8 (2012)
Akash, S.A., Al-Zihad, M., Adhikary, T., Razzaque, M.A., Sharmin, A.: Hearme: A smart mobile application for mitigating women harassment. In: 2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) 2016 Dec 19, pp. 87–90. IEEE (2016)
Shah, K., Patel, H., Sanghvi, D., Shah, M.A.: Comparative analysis of logistic regression, random forest and knn models for the text classification. Augmented Hum. Res. 5(1), 1–6 (2020)
Dey, S., Wasif, S., Tonmoy, D.S., Sultana, S., Sarkar, J., Dey, M.A.: Comparative study of support vector machine and naive bayes classifier for sentiment analysis on amazon product reviews. In: 2020 International Conference on Contemporary Computing and Applications (IC3A) Feb 5, pp. 217–220. IEEE (2020)
Poorna, S.S., Reddy,M.R.K., Akhil,N., Kamath,S., Mohan, L., Anuraj K.: Computer vision aided study for melanoma detection: a deep learning versus conventional supervised learning approach. In: Pati, B., Panigrahi, C., Buyya, R., Li, K.C. (eds.) Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol. 1082, Springer, Singapore (2020)
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Hariharan, K. et al. (2021). A Comprehensive Study Toward Women Safety Using Machine Learning Along with Android App Development. In: Karuppusamy, P., Perikos, I., Shi, F., Nguyen, T.N. (eds) Sustainable Communication Networks and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 55. Springer, Singapore. https://doi.org/10.1007/978-981-15-8677-4_26
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DOI: https://doi.org/10.1007/978-981-15-8677-4_26
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