Öz
This study suggests that by implementing machine learning methods on a sociodemographic data set can be helpful in preventing domestic violence. This approach is important in predicting high-risk factors that an offender may cause and it offers treatment, and financial or mental health aids in order to prevent domestic violence. In this sense, this proposal is critical at a personal and social level in creating a secure and healthy environment as well as empowering an equal society. In our study, we use k-nearest neighbor (k-nn), support vector machine (SVM), decision tree (DT), and Gaussian Naive Bayes (GNB) machine learning algorithms for the prediction analysis. We provide the comparison of the classifiers with precision, recall, F1 score, and accuracy performance measures. According to our analysis, the decision tree (DT) performs the best performance in terms of accuracy.
Anahtar Kelimeler
Violence detection machine learning sociodemographic classification prediction
Kaynakça
- Boserup, B., McKenney, M., & Elkbuli, A. (2020). Alarming trends in US domestic violence during the COVID-19 pandemic. The American Journal of Emergency Medicine, 38(12), 2753-2755.
- Wahi A, Zaleski KL, Lampe J, Bevan P, Koski A. The Lived Experience of Child Marriage in the United States. Soc Work Public Health. 2019; 34(3):201-213.
- Harland KK, Peek-Asa C, Saftlas AF. Intimate Partner Violence and Controlling Behaviors Experienced by Emergency Department Patients: Differences by Sexual Orientation and Gender Identification. J Interpers Violence. 2021 Jun; 36(11-12): NP6125-NP6143.
- Subramani, S., Wang, H., Vu, H. Q., & Li, G. (2018). Domestic violence crisis identification from facebook posts based on deep learning. IEEE Access, 6, 54075-54085.
- Subramani, S., Michalska, S., Wang, H., Du, J., Zhang, Y., & Shakeel, H. (2019). Deep learning for multi-class identification from domestic violence online posts. IEEE Access, 7, 46210-46224.
- Le Glaz, A., Haralambous, Y., Kim-Dufor, D. H., Lenca, P., Billot, R., Ryan, T. C., and Lemey, C. (2021). Machine learning and natural language processing in mental health: systematic review. Journal of Medical Internet Research, 23(5).
- Xue, J., Chen, J., and Gelles, R. (2017). Using Data Mining Techniques to Examine Domestic Violence Topics on Twitter. Violence and Gender, 6 (2), 105–114.
- Siddique, M., Islam, M., Sinthy, R., Mohima, K., Kabir, M., Jibon, A. H., and Biswas, M. (2022). State-of-the-Art Violence Detection Techniques: A review. Asian Journal of Research in Computer Science, 29-42.
- Ye, L., Wang, L., Wang, P., Ferdinando, H., Seppänen, T., Alasaarela, E. (2018). Physical Violence Detection with Movement Sensors. Machine Learning and Intelligent Communications. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham.
- Vashistha, P., Singh, J.P., Khan, M.A. (2020). A Comparative Analysis of Different Violence Detection Algorithms from Videos. In: Kolhe, M., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 94.
- Deepak, K., Vignesh, L. K. P., and Chandrakala, S. J. I. E. (2020). Autocorrelation of gradients based violence detection in surveillance videos. ICT Express, 6(3), pp 155-159.
- S. Das, A. Sarker and T. Mahmud, "Violence Detection from Videos using HOG Features," 2019 4th International Conference on Electrical Information and Communication Technology, 2019, pp. 1-5.
- A. Jain and D. K. Vishwakarma, "State-of-the-arts Violence Detection using ConvNets," 2020 International Conference on Communication and Signal Processing, 2020, pp. 0813-0817.
- Chaudhary, D., Kumar, S., and Dhaka, V. S. (2022). Video based human crowd analysis using machine learning: a survey. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 10(2), 113-131.
- Jiang Y, DeBare D, Colomer I, Wesley J, Seaberry J, Viner-Brown S. Characteristics of Victims and Suspects in Domestic Violence-Related Homicide- Rhode Island Violent Death Reporting System, 2004-2015. R I Med J (2013). 2018 Dec 03; 101(10):58-61.
- Rituerto-González, E., Mínguez-Sánchez, A., Gallardo-Antolín, A., & Peláez-Moreno, C. (2019). Data augmentation for speaker identification under stress conditions to combat gender-based violence. Applied Sciences, 9(11), 2298.
- Evans, S. (2005). Beyond gender: Class, poverty and domestic violence. Australian Social Work, 58(1), 36-43.
- Wallach, H. S., and Sela, T. (2008). The importance of male batters’ attributions in understanding and preventing domestic violence. Journal of Family Violence, 23(7), 655-660.
- Guerrero, A., Cárdenas, J. G., Romero, V., & Ayma, V. H. (2020). Comparison of Classifiers Models for Prediction of Intimate Partner Violence. In Proceedings of the Future Technologies Conference, pp. 469-488, Springer, Cham.
- Abramsky, T., Watts, C. H., Garcia-Moreno, C., Devries, K., Kiss, L., Ellsberg, M., and Heise, L. (2011). What factors are associated with recent intimate partner violence, Findings from the WHO multi-country study on women's health and domestic violence. BMC public health, 11(1), 1-17.
- Vyas, S., and Watts, C. (2009). How does economic empowerment affect women's risk of intimate partner violence in low and middle income countries. A systematic review of published evidence. Journal of International Development: The Journal of the Development Studies Association, 21(5), 577-602.
- Leonard, K. E. (2005). Alcohol and intimate partner violence: when can we say that heavy drinking is a contributing cause of violence, Addiction, vol 100. no. 4, pp 422-425.
Öz
This study suggests that by implementing machine learning methods on a sociodemographic data set can be helpful in preventing domestic violence. This approach is important in predicting high-risk factors that an offender may cause and it offers treatment, and financial or mental health aids in order to prevent domestic violence. In this sense, this proposal is critical at a personal and social level in creating a secure and healthy environment as well as empowering an equal society. In our study, we use k-nearest neighbor (k-nn), support vector machine (SVM), decision tree (DT), and Gaussian Naive Bayes (GNB) machine learning algorithms for the prediction analysis. We provide the comparison of the classifiers with precision, recall, F1 score, and accuracy performance measures. According to our analysis, the decision tree (DT) performs the best performance in terms of accuracy.
Anahtar Kelimeler
Violence detection machine learning sociodemographic classification prediction
Kaynakça
- Boserup, B., McKenney, M., & Elkbuli, A. (2020). Alarming trends in US domestic violence during the COVID-19 pandemic. The American Journal of Emergency Medicine, 38(12), 2753-2755.
- Wahi A, Zaleski KL, Lampe J, Bevan P, Koski A. The Lived Experience of Child Marriage in the United States. Soc Work Public Health. 2019; 34(3):201-213.
- Harland KK, Peek-Asa C, Saftlas AF. Intimate Partner Violence and Controlling Behaviors Experienced by Emergency Department Patients: Differences by Sexual Orientation and Gender Identification. J Interpers Violence. 2021 Jun; 36(11-12): NP6125-NP6143.
- Subramani, S., Wang, H., Vu, H. Q., & Li, G. (2018). Domestic violence crisis identification from facebook posts based on deep learning. IEEE Access, 6, 54075-54085.
- Subramani, S., Michalska, S., Wang, H., Du, J., Zhang, Y., & Shakeel, H. (2019). Deep learning for multi-class identification from domestic violence online posts. IEEE Access, 7, 46210-46224.
- Le Glaz, A., Haralambous, Y., Kim-Dufor, D. H., Lenca, P., Billot, R., Ryan, T. C., and Lemey, C. (2021). Machine learning and natural language processing in mental health: systematic review. Journal of Medical Internet Research, 23(5).
- Xue, J., Chen, J., and Gelles, R. (2017). Using Data Mining Techniques to Examine Domestic Violence Topics on Twitter. Violence and Gender, 6 (2), 105–114.
- Siddique, M., Islam, M., Sinthy, R., Mohima, K., Kabir, M., Jibon, A. H., and Biswas, M. (2022). State-of-the-Art Violence Detection Techniques: A review. Asian Journal of Research in Computer Science, 29-42.
- Ye, L., Wang, L., Wang, P., Ferdinando, H., Seppänen, T., Alasaarela, E. (2018). Physical Violence Detection with Movement Sensors. Machine Learning and Intelligent Communications. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham.
- Vashistha, P., Singh, J.P., Khan, M.A. (2020). A Comparative Analysis of Different Violence Detection Algorithms from Videos. In: Kolhe, M., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 94.
- Deepak, K., Vignesh, L. K. P., and Chandrakala, S. J. I. E. (2020). Autocorrelation of gradients based violence detection in surveillance videos. ICT Express, 6(3), pp 155-159.
- S. Das, A. Sarker and T. Mahmud, "Violence Detection from Videos using HOG Features," 2019 4th International Conference on Electrical Information and Communication Technology, 2019, pp. 1-5.
- A. Jain and D. K. Vishwakarma, "State-of-the-arts Violence Detection using ConvNets," 2020 International Conference on Communication and Signal Processing, 2020, pp. 0813-0817.
- Chaudhary, D., Kumar, S., and Dhaka, V. S. (2022). Video based human crowd analysis using machine learning: a survey. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 10(2), 113-131.
- Jiang Y, DeBare D, Colomer I, Wesley J, Seaberry J, Viner-Brown S. Characteristics of Victims and Suspects in Domestic Violence-Related Homicide- Rhode Island Violent Death Reporting System, 2004-2015. R I Med J (2013). 2018 Dec 03; 101(10):58-61.
- Rituerto-González, E., Mínguez-Sánchez, A., Gallardo-Antolín, A., & Peláez-Moreno, C. (2019). Data augmentation for speaker identification under stress conditions to combat gender-based violence. Applied Sciences, 9(11), 2298.
- Evans, S. (2005). Beyond gender: Class, poverty and domestic violence. Australian Social Work, 58(1), 36-43.
- Wallach, H. S., and Sela, T. (2008). The importance of male batters’ attributions in understanding and preventing domestic violence. Journal of Family Violence, 23(7), 655-660.
- Guerrero, A., Cárdenas, J. G., Romero, V., & Ayma, V. H. (2020). Comparison of Classifiers Models for Prediction of Intimate Partner Violence. In Proceedings of the Future Technologies Conference, pp. 469-488, Springer, Cham.
- Abramsky, T., Watts, C. H., Garcia-Moreno, C., Devries, K., Kiss, L., Ellsberg, M., and Heise, L. (2011). What factors are associated with recent intimate partner violence, Findings from the WHO multi-country study on women's health and domestic violence. BMC public health, 11(1), 1-17.
- Vyas, S., and Watts, C. (2009). How does economic empowerment affect women's risk of intimate partner violence in low and middle income countries. A systematic review of published evidence. Journal of International Development: The Journal of the Development Studies Association, 21(5), 577-602.
- Leonard, K. E. (2005). Alcohol and intimate partner violence: when can we say that heavy drinking is a contributing cause of violence, Addiction, vol 100. no. 4, pp 422-425.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Erken Görünüm Tarihi | 31 Aralık 2022 |
| Yayımlanma Tarihi | 31 Aralık 2022 |
| Yayımlandığı Sayı | Yıl 2022 Sayı: 44 |
- Makale Dosyaları