R. Al-kasasbeh, N. Korenevskiy, M. Alshamasin, Osama, O., M. Al-Habahbeh, A. Shaqadan, S. Rodionova, S. Filist
{"title":"Fuzzy Mathematical Models for Predicting and Diagnosing Occupational Diseases of Workers in the Agro-industrial Complex in Contact with Pesticides","authors":"R. Al-kasasbeh, N. Korenevskiy, M. Alshamasin, Osama, O., M. Al-Habahbeh, A. Shaqadan, S. Rodionova, S. Filist","doi":"10.1109/ICNISC57059.2022.00065","DOIUrl":null,"url":null,"abstract":"Objective is to improving quality of medical care for workers in the agriculture related industries like pesticides by using fuzzy mathematical models implemented by modern information and intellectual technologies. In the course of the research, it was found that from a modelling perspective, the problems of predicting and identifying suitable class is type of poorly defined conditions with intersecting boundaries. Therefore, building hybrid fuzzy decision rules combines clinical experience (natural intelligence) with artificial intelligence, which allows to achieve a new quality in solving complex systemic problems and is innovative.","PeriodicalId":286467,"journal":{"name":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNISC57059.2022.00065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Objective is to improving quality of medical care for workers in the agriculture related industries like pesticides by using fuzzy mathematical models implemented by modern information and intellectual technologies. In the course of the research, it was found that from a modelling perspective, the problems of predicting and identifying suitable class is type of poorly defined conditions with intersecting boundaries. Therefore, building hybrid fuzzy decision rules combines clinical experience (natural intelligence) with artificial intelligence, which allows to achieve a new quality in solving complex systemic problems and is innovative.