Seyed Muhammad Hossein Mousavi, S. Mirinezhad, Mehrdad Shafaei Mosleh, M. Dezfoulian
{"title":"一种粒子群模糊专家系统:作为用NOET方法确定博士级验收的辅助","authors":"Seyed Muhammad Hossein Mousavi, S. Mirinezhad, Mehrdad Shafaei Mosleh, M. Dezfoulian","doi":"10.13140/RG.2.2.27541.22247","DOIUrl":null,"url":null,"abstract":"The intelligent decision making systems are useful tools for the assistance of human expert, and or as a perfect alternative for expert in a variety of auto-decision making fields. The use of such systems in education, agriculture, industry, fishery, animal husbandry etc., can decrease manpower errors or need of it; In the other hand, it can increase the quality and the pace of service giving. The interview at the PH.D level or even Master's degree, due to the high sensitivity in scoring to the candidates, is of high importance. Therefore, creating a system for storing these scores, and inferring the results can be beneficial when there is a large number of candidates. In this paper, the expert system has an educational use, and classifies the probability of acceptance or unacceptance of PH.D candidates in the exam and interview, based on the (National Organization of Educational Testing) NOET measures, also estimates scientific level of candidates. The proposed fuzzy-expert system takes advantage of the particle swarm optimization (PSO) evolutionary algorithm to specifying the score of each variable, and eventually the final condition of the candidate. The acquired results of evaluating the fuzzy-expert system proves its functionality. This system is also able to function well in scoring similar educational cases to specify acceptance.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"286 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A PSO fuzzy-expert system: As an assistant for specifying the acceptance by NOET measures, at PH.D level\",\"authors\":\"Seyed Muhammad Hossein Mousavi, S. Mirinezhad, Mehrdad Shafaei Mosleh, M. Dezfoulian\",\"doi\":\"10.13140/RG.2.2.27541.22247\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The intelligent decision making systems are useful tools for the assistance of human expert, and or as a perfect alternative for expert in a variety of auto-decision making fields. The use of such systems in education, agriculture, industry, fishery, animal husbandry etc., can decrease manpower errors or need of it; In the other hand, it can increase the quality and the pace of service giving. The interview at the PH.D level or even Master's degree, due to the high sensitivity in scoring to the candidates, is of high importance. Therefore, creating a system for storing these scores, and inferring the results can be beneficial when there is a large number of candidates. In this paper, the expert system has an educational use, and classifies the probability of acceptance or unacceptance of PH.D candidates in the exam and interview, based on the (National Organization of Educational Testing) NOET measures, also estimates scientific level of candidates. The proposed fuzzy-expert system takes advantage of the particle swarm optimization (PSO) evolutionary algorithm to specifying the score of each variable, and eventually the final condition of the candidate. The acquired results of evaluating the fuzzy-expert system proves its functionality. This system is also able to function well in scoring similar educational cases to specify acceptance.\",\"PeriodicalId\":386952,\"journal\":{\"name\":\"2017 Artificial Intelligence and Signal Processing Conference (AISP)\",\"volume\":\"286 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Artificial Intelligence and Signal Processing Conference (AISP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.13140/RG.2.2.27541.22247\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.13140/RG.2.2.27541.22247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A PSO fuzzy-expert system: As an assistant for specifying the acceptance by NOET measures, at PH.D level
The intelligent decision making systems are useful tools for the assistance of human expert, and or as a perfect alternative for expert in a variety of auto-decision making fields. The use of such systems in education, agriculture, industry, fishery, animal husbandry etc., can decrease manpower errors or need of it; In the other hand, it can increase the quality and the pace of service giving. The interview at the PH.D level or even Master's degree, due to the high sensitivity in scoring to the candidates, is of high importance. Therefore, creating a system for storing these scores, and inferring the results can be beneficial when there is a large number of candidates. In this paper, the expert system has an educational use, and classifies the probability of acceptance or unacceptance of PH.D candidates in the exam and interview, based on the (National Organization of Educational Testing) NOET measures, also estimates scientific level of candidates. The proposed fuzzy-expert system takes advantage of the particle swarm optimization (PSO) evolutionary algorithm to specifying the score of each variable, and eventually the final condition of the candidate. The acquired results of evaluating the fuzzy-expert system proves its functionality. This system is also able to function well in scoring similar educational cases to specify acceptance.