{"title":"一种利用各种机器学习分类器预测能力和潜在风险因素的新方法","authors":"Stalin M., K. S","doi":"10.1080/00051144.2023.2200347","DOIUrl":null,"url":null,"abstract":"In a survey conducted in the year 2020, we came to know that India’s around 50% of population includes young people of the age group of 25 and students. Guiding this young mass in the right way and strengthening their future is a huge responsibility put over the head of the elder citizens of India such as their parents teachers and professors. This paper aims to build a model that can predict the students’ competency level and the risk factors or the fields where he needs to put their effort to improve themselves, and this model also helps the parents, professors and Educational institutes to know about their children’s and students in which zone they stand, are they ready to compete with others. This analysis is done by using different ML bifurcation algorithms. Also we aim to find the best classifier which can emerge with the highest predicting accuracy among all other classifiers to the above-said problem. The accuracy of 88.5% is achieved through the proposed machine learning algorithm for particular education datasets which have been taken into consideration.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"64 1","pages":"550 - 564"},"PeriodicalIF":1.7000,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel approach to predict competency and the hidden risk factor by using various machine learning classifiers\",\"authors\":\"Stalin M., K. S\",\"doi\":\"10.1080/00051144.2023.2200347\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In a survey conducted in the year 2020, we came to know that India’s around 50% of population includes young people of the age group of 25 and students. Guiding this young mass in the right way and strengthening their future is a huge responsibility put over the head of the elder citizens of India such as their parents teachers and professors. This paper aims to build a model that can predict the students’ competency level and the risk factors or the fields where he needs to put their effort to improve themselves, and this model also helps the parents, professors and Educational institutes to know about their children’s and students in which zone they stand, are they ready to compete with others. This analysis is done by using different ML bifurcation algorithms. Also we aim to find the best classifier which can emerge with the highest predicting accuracy among all other classifiers to the above-said problem. The accuracy of 88.5% is achieved through the proposed machine learning algorithm for particular education datasets which have been taken into consideration.\",\"PeriodicalId\":55412,\"journal\":{\"name\":\"Automatika\",\"volume\":\"64 1\",\"pages\":\"550 - 564\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Automatika\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1080/00051144.2023.2200347\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automatika","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/00051144.2023.2200347","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
A novel approach to predict competency and the hidden risk factor by using various machine learning classifiers
In a survey conducted in the year 2020, we came to know that India’s around 50% of population includes young people of the age group of 25 and students. Guiding this young mass in the right way and strengthening their future is a huge responsibility put over the head of the elder citizens of India such as their parents teachers and professors. This paper aims to build a model that can predict the students’ competency level and the risk factors or the fields where he needs to put their effort to improve themselves, and this model also helps the parents, professors and Educational institutes to know about their children’s and students in which zone they stand, are they ready to compete with others. This analysis is done by using different ML bifurcation algorithms. Also we aim to find the best classifier which can emerge with the highest predicting accuracy among all other classifiers to the above-said problem. The accuracy of 88.5% is achieved through the proposed machine learning algorithm for particular education datasets which have been taken into consideration.
AutomatikaAUTOMATION & CONTROL SYSTEMS-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
4.00
自引率
5.30%
发文量
65
审稿时长
4.5 months
期刊介绍:
AUTOMATIKA – Journal for Control, Measurement, Electronics, Computing and Communications is an international scientific journal that publishes scientific and professional papers in the field of automatic control, robotics, measurements, electronics, computing, communications and related areas. Click here for full Focus & Scope.
AUTOMATIKA is published since 1960, and since 1991 by KoREMA - Croatian Society for Communications, Computing, Electronics, Measurement and Control, Member of IMEKO and IFAC.