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To reduce these issues, we developed a lightweight Deep Learning model that can be integrated into a school\u2019s surveillance camera system to quickly detect violent fighting behaviors for timely intervention by school staff. The proposed FightNet model consists of three components: MobileNetV2 backbone, Feature Pyramid Network (FPN) neck, and Centernet Object as a Point (COaP) head. By optimizing the hyperparameters of the model to extract keypoints in image frames from the COCO dataset, we applied an LSTM model to determine the temporal dependence of actions and classify them as \u201cfighting\u201d or \u201cnormal\u201d using the UBI-Fights dataset. The FightNet model achieved mAP@0.5 of 45.34% and mAP@0.95 of 55.89% in estimating keypoints, and 72.68% accuracy and 71.69% F1-score in predicting actions. Based on these results, we conclude that the proposed model can effectively address the issue of school violence.<\/jats:p>","DOI":"10.3233\/jifs-232480","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T12:39:01Z","timestamp":1691152741000},"page":"6469-6483","source":"Crossref","is-referenced-by-count":7,"title":["FightNet deep learning strategy: An innovative solution to prevent school fighting violence"],"prefix":"10.1177","volume":"45","author":[{"given":"Le Quang","family":"Thao","sequence":"first","affiliation":[{"name":"Faculty of Physics, VNU University of Science, Hanoi, Vietnam"},{"name":"Vietnam National University, Hanoi, Vietnam"}]},{"given":"Nguyen Thi Bich","family":"Diep","sequence":"additional","affiliation":[{"name":"Ivycation Company, Hanoi, Vietnam"}]},{"given":"Ngo Chi","family":"Bach","sequence":"additional","affiliation":[{"name":"Faculty of Physics, VNU University of Science, Hanoi, Vietnam"},{"name":"Vietnam National University, Hanoi, Vietnam"}]},{"given":"Le Khanh","family":"Linh","sequence":"additional","affiliation":[{"name":"Reigate Grammar School of Vietnam, Hanoi, Vietnam"}]},{"given":"Nguyen Do Hoang","family":"Giang","sequence":"additional","affiliation":[{"name":"VNU-HUS High School for the Gifted Students, Hanoi, Vietnam"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-232480_ref1","doi-asserted-by":"crossref","unstructured":"Elgar F.J. , McKinnon B. , Walsh S.D. , Freeman J. , Donnelly D. et al., Structural determinants of youth bullying and fighting in 79 countries, Journal of Adolescent Health 57(6) (2015), 643\u2013650. https:\/\/doi.org\/10.1016\/j.jadohealth.2015.08.007","DOI":"10.1016\/j.jadohealth.2015.08.007"},{"key":"10.3233\/JIFS-232480_ref2","doi-asserted-by":"crossref","unstructured":"Nansel T.R. , Overpeck M. , Pilla R.S. , Ruan W.J. , Simons-Morton B. et al., Bullying behaviors among US youth, Prevalence and Association with Psychosocial Adjustment 285(16) (2001), 2094\u20132100. https:\/\/doi.org\/10.1001\/jama.285.16.2094","DOI":"10.1001\/jama.285.16.2094"},{"key":"10.3233\/JIFS-232480_ref3","doi-asserted-by":"crossref","unstructured":"Hinduja S. and Patchin J.W. , Bullying, cyberbullying, and suicide, Archives of Suicide Research 14(3) (2010) 206\u2013221. https:\/\/doi.org\/10.1080\/13811118.2010.494133","DOI":"10.1080\/13811118.2010.494133"},{"key":"10.3233\/JIFS-232480_ref4","unstructured":"International day against violence and bullying at school including cyberbullying, online at https:\/\/www.unesco.org, Accessed Jan, 2023."},{"key":"10.3233\/JIFS-232480_ref5","doi-asserted-by":"crossref","unstructured":"Biswas T. , Scott J.G. , Munir K. , Thomas H.J. , Huda M.M. et al., Global variation in the prevalence of bullying victimisation amongst adolescents: Role of peer and parental supports, E Clinical Medicine 20 (2020), 1\u20138. 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International Journal of Multicultural and Multireligious Understanding 9(3) (2022) 421\u2013425. https:\/\/dx.doi.org\/10.18415\/ijmmu.v9i3.3591"},{"key":"10.3233\/JIFS-232480_ref10","doi-asserted-by":"crossref","unstructured":"Berkowitz R. , Bar-on N. , Tzafrir S. and Enosh G. , Teachers\u2019 safety and workplace victimization: A socioecological analysis of teachers\u2019 perspective, Journal of School Violence 24(4) (2022) 397\u2013412. https:\/\/doi.org\/10.1080\/15388220.2022.2105857","DOI":"10.1080\/15388220.2022.2105857"},{"key":"10.3233\/JIFS-232480_ref11","doi-asserted-by":"crossref","unstructured":"Seo C. and Kruis N.E. , The impact of school\u2019s security and restorative justice measures on school violence, Children and Youth Services Review 132 (2022). https:\/\/doi.org\/10.1016\/j.childyouth.2021.106305","DOI":"10.1016\/j.childyouth.2021.106305"},{"key":"10.3233\/JIFS-232480_ref12","doi-asserted-by":"crossref","unstructured":"Sanders J.E. , Coping with the impact of systemic racism, inequity, school and community violence among high school students who are suspended or expelled, Journal of Interpersonal Violence 37(21-22) (2022) 21217\u201321243. https:\/\/doi.org\/10.1177\/08862605211056724","DOI":"10.1177\/08862605211056724"},{"key":"10.3233\/JIFS-232480_ref13","doi-asserted-by":"crossref","unstructured":"Cornu C. , Abduvahobov P. , Laoufi R. , Liu Y. and S\u00e9guy S. , An introduction to a whole-education approach toschool bullying: Recommendations from UNESCO scientific committee on school violence and bullying including cyber bullying, International Journal of Bullying Prevention (2022). https:\/\/doi.org\/10.1007\/s42380-021-00093-8","DOI":"10.1007\/s42380-021-00093-8"},{"key":"10.3233\/JIFS-232480_ref14","doi-asserted-by":"crossref","unstructured":"Mpeis P. , Hadjichristodoulou A. , Vicario J.B. and Yazti D.Z. , SMAS: A smart alert system for localization and first response to fires on ro-ro vessels, in Proceedings of the 16th ACM 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