Predicting adolescent violence in Wartegg-ZeichenTest drawing images based on deep learning
This thesis deals with the problem of negative behaviour due to changes in mental and physical stress in adolescence. In particular, it is a study to solve the health care problem of students exposed to violence. Among the problematic behaviours, students exposed to violence, especially, have many p...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2023.2286186 |
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| _version_ | 1850213251290234880 |
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| author | Kyung-yeul Kim Young-bo Yang Mi-ra Kim Ji Su Park Jihie Kim |
| author_facet | Kyung-yeul Kim Young-bo Yang Mi-ra Kim Ji Su Park Jihie Kim |
| author_sort | Kyung-yeul Kim |
| collection | DOAJ |
| description | This thesis deals with the problem of negative behaviour due to changes in mental and physical stress in adolescence. In particular, it is a study to solve the health care problem of students exposed to violence. Among the problematic behaviours, students exposed to violence, especially, have many problems with healthcare. A projective test using pictures can elicit information from adolescents through direct experiences represented by pictures to which the subject unconsciously reacts. Few methods analyse images drawn by adolescents as image data. This study analyses data from 134 violent students who received fifth-degree punishment for violent behaviour and 134 nonviolent students. We use the convolutional neural network (CNN)(softmax), CNN (support vector machine (SVM)), with the style transfer generative adversarial network, and ensemble techniques to analyse drawn images using WZT and predict violence through deep learning. We predict violence from pictures with an accuracy of 93%–98%. This study is the first to automatically analyze and predict violence with a deep learning model in images drawn by adolescents on WZT. It also features WZT to proactively conduct violence investigations to improve health care for students. Advances in deep learning for image feature extraction are expected to provide more research opportunities. |
| format | Article |
| id | doaj-art-8868e8676e0244c694bb62a5ebad4a0b |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-8868e8676e0244c694bb62a5ebad4a0b2025-08-20T02:09:11ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.2286186Predicting adolescent violence in Wartegg-ZeichenTest drawing images based on deep learningKyung-yeul Kim0Young-bo Yang1Mi-ra Kim2Ji Su Park3Jihie Kim4Department of Artificial intelligence, Dongguk University, Seoul, Republic of KoreaDepartment of Data Science, Kookmin University, Seoul, Republic of KoreaDepartment of Liberal Arts Convergence, Honam University, KwangJu, Republic of KoreaDepartment of Computer Science, Jonju University, Jonju, Republic of KoreaDepartment of Artificial intelligence, Dongguk University, Seoul, Republic of KoreaThis thesis deals with the problem of negative behaviour due to changes in mental and physical stress in adolescence. In particular, it is a study to solve the health care problem of students exposed to violence. Among the problematic behaviours, students exposed to violence, especially, have many problems with healthcare. A projective test using pictures can elicit information from adolescents through direct experiences represented by pictures to which the subject unconsciously reacts. Few methods analyse images drawn by adolescents as image data. This study analyses data from 134 violent students who received fifth-degree punishment for violent behaviour and 134 nonviolent students. We use the convolutional neural network (CNN)(softmax), CNN (support vector machine (SVM)), with the style transfer generative adversarial network, and ensemble techniques to analyse drawn images using WZT and predict violence through deep learning. We predict violence from pictures with an accuracy of 93%–98%. This study is the first to automatically analyze and predict violence with a deep learning model in images drawn by adolescents on WZT. It also features WZT to proactively conduct violence investigations to improve health care for students. Advances in deep learning for image feature extraction are expected to provide more research opportunities.https://www.tandfonline.com/doi/10.1080/09540091.2023.2286186Wartegg–ZeichenTestdeep learning-based violence predictionconvolutional neural network (CNN) (softmax)CNN (SVM)healthcareArtificial intelligence |
| spellingShingle | Kyung-yeul Kim Young-bo Yang Mi-ra Kim Ji Su Park Jihie Kim Predicting adolescent violence in Wartegg-ZeichenTest drawing images based on deep learning Connection Science Wartegg–ZeichenTest deep learning-based violence prediction convolutional neural network (CNN) (softmax) CNN (SVM) healthcare Artificial intelligence |
| title | Predicting adolescent violence in Wartegg-ZeichenTest drawing images based on deep learning |
| title_full | Predicting adolescent violence in Wartegg-ZeichenTest drawing images based on deep learning |
| title_fullStr | Predicting adolescent violence in Wartegg-ZeichenTest drawing images based on deep learning |
| title_full_unstemmed | Predicting adolescent violence in Wartegg-ZeichenTest drawing images based on deep learning |
| title_short | Predicting adolescent violence in Wartegg-ZeichenTest drawing images based on deep learning |
| title_sort | predicting adolescent violence in wartegg zeichentest drawing images based on deep learning |
| topic | Wartegg–ZeichenTest deep learning-based violence prediction convolutional neural network (CNN) (softmax) CNN (SVM) healthcare Artificial intelligence |
| url | https://www.tandfonline.com/doi/10.1080/09540091.2023.2286186 |
| work_keys_str_mv | AT kyungyeulkim predictingadolescentviolenceinwarteggzeichentestdrawingimagesbasedondeeplearning AT youngboyang predictingadolescentviolenceinwarteggzeichentestdrawingimagesbasedondeeplearning AT mirakim predictingadolescentviolenceinwarteggzeichentestdrawingimagesbasedondeeplearning AT jisupark predictingadolescentviolenceinwarteggzeichentestdrawingimagesbasedondeeplearning AT jihiekim predictingadolescentviolenceinwarteggzeichentestdrawingimagesbasedondeeplearning |