{"title":"预测韦拉克鲁斯州的杀戮女性事件:使用扩展的 MFM-FEM-VER-CP-2024 模型的模糊逻辑方法","authors":"Carlos Medel-Ramírez, Hilario Medel-López","doi":"arxiv-2409.00359","DOIUrl":null,"url":null,"abstract":"The article focuses on the urgent issue of femicide in Veracruz, Mexico, and\nthe development of the MFM_FEM_VER_CP_2024 model, a mathematical framework\ndesigned to predict femicide risk using fuzzy logic. This model addresses the\ncomplexity and uncertainty inherent in gender based violence by formalizing\nrisk factors such as coercive control, dehumanization, and the cycle of\nviolence. These factors are mathematically modeled through membership functions\nthat assess the degree of risk associated with various conditions, including\npersonal relationships and specific acts of violence. The study enhances the\noriginal model by incorporating new rules and refining existing membership\nfunctions, which significantly improve the model predictive accuracy.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting Femicide in Veracruz: A Fuzzy Logic Approach with the Expanded MFM-FEM-VER-CP-2024 Model\",\"authors\":\"Carlos Medel-Ramírez, Hilario Medel-López\",\"doi\":\"arxiv-2409.00359\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article focuses on the urgent issue of femicide in Veracruz, Mexico, and\\nthe development of the MFM_FEM_VER_CP_2024 model, a mathematical framework\\ndesigned to predict femicide risk using fuzzy logic. This model addresses the\\ncomplexity and uncertainty inherent in gender based violence by formalizing\\nrisk factors such as coercive control, dehumanization, and the cycle of\\nviolence. These factors are mathematically modeled through membership functions\\nthat assess the degree of risk associated with various conditions, including\\npersonal relationships and specific acts of violence. The study enhances the\\noriginal model by incorporating new rules and refining existing membership\\nfunctions, which significantly improve the model predictive accuracy.\",\"PeriodicalId\":501479,\"journal\":{\"name\":\"arXiv - CS - Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.00359\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.00359","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting Femicide in Veracruz: A Fuzzy Logic Approach with the Expanded MFM-FEM-VER-CP-2024 Model
The article focuses on the urgent issue of femicide in Veracruz, Mexico, and
the development of the MFM_FEM_VER_CP_2024 model, a mathematical framework
designed to predict femicide risk using fuzzy logic. This model addresses the
complexity and uncertainty inherent in gender based violence by formalizing
risk factors such as coercive control, dehumanization, and the cycle of
violence. These factors are mathematically modeled through membership functions
that assess the degree of risk associated with various conditions, including
personal relationships and specific acts of violence. The study enhances the
original model by incorporating new rules and refining existing membership
functions, which significantly improve the model predictive accuracy.