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- W4313703070 abstract "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." @default.
- W4313703070 created "2023-01-08" @default.
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- W4313703070 date "2023-01-07" @default.
- W4313703070 modified "2023-09-30" @default.
- W4313703070 title "Violence Detection with Machine Learning: A Sociodemographic Approach" @default.
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- W4313703070 doi "https://doi.org/10.31590/ejosat.1225896" @default.
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