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Dergiler / Avrupa Bilim ve Teknoloji Dergisi / 2022 / Sayı: 44 Özel Sayı

Violence Detection with Machine Learning: A Sociodemographic Approach

Tolga Ensari(Arkansas Tech University)0000-0003-0896-3058
Betul Ensari(Arkansas Tech University)0000-0003-0425-7252
Mustafa Dağtekin(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, MÜHENDİSLİK FAKÜLTESİ)0000-0002-0797-9392
Sayfa
104–107
Yayın Tarihi
2022-12-31
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

According to our analysis, the decision tree (DT) performs the best performance in terms of accuracy.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study suggests that by implementing machine learning methods on a sociodemographic data set can be helpful in preventing domestic violence.
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Temel Bulgular & Veri:We provide the comparison of the classifiers with precision, recall, F1 score, and accuracy performance measures.
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Sonuç & Katkı:According to our analysis, the decision tree (DT) performs the best performance in terms of accuracy.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Violence detection · machine learning · sociodemographic

Özet

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.

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.

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Bu Makaleye Atıf Yap

Ensari, T., Ensari, B., Dağtekin, M. (2022). Violence Detection with Machine Learning: A Sociodemographic Approach. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 44 Özel Sayı, 104–107. https://doi.org/10.31590/ejosat.1225896

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Kaynakça (22)

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