P. Vidya Sagar, C. Krishna, Nageswara Rao Moparthi
{"title":"基于计算神经网络的软件项目时间预测估计","authors":"P. Vidya Sagar, C. Krishna, Nageswara Rao Moparthi","doi":"10.1109/AEEICB.2018.8480948","DOIUrl":null,"url":null,"abstract":"Programming venture administration (SPM) will be a standout amongst the grade variables will program victory alternately disappointment. Prediction for product improvement chance may be the key errand for the successful SPM. Those correctness and unwavering quality of prediction components may be likewise imperative. In this project, we look at distinctive machine Taking in strategies in place should faultlessly foresee the programming chance. The point when chronicled venture exert data, span information Furthermore other task information would utilized for exertion or span prediction, the essential inspiration behind this endeavor will be that those information put away in the recorded datasets might be used to create predictive models, Eventually Tom’s perusing Possibly Factual systems for example, straight relapse Also correspondence Investigation or machine Taking in strategies for example, ANN (Artificial neural Network) Also SVM (Support vector Machine), with anticipate the exert alternately span about ventures.","PeriodicalId":423671,"journal":{"name":"2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Estimation of software project time prediction using Computational Neural Networks\",\"authors\":\"P. Vidya Sagar, C. Krishna, Nageswara Rao Moparthi\",\"doi\":\"10.1109/AEEICB.2018.8480948\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Programming venture administration (SPM) will be a standout amongst the grade variables will program victory alternately disappointment. Prediction for product improvement chance may be the key errand for the successful SPM. Those correctness and unwavering quality of prediction components may be likewise imperative. In this project, we look at distinctive machine Taking in strategies in place should faultlessly foresee the programming chance. The point when chronicled venture exert data, span information Furthermore other task information would utilized for exertion or span prediction, the essential inspiration behind this endeavor will be that those information put away in the recorded datasets might be used to create predictive models, Eventually Tom’s perusing Possibly Factual systems for example, straight relapse Also correspondence Investigation or machine Taking in strategies for example, ANN (Artificial neural Network) Also SVM (Support vector Machine), with anticipate the exert alternately span about ventures.\",\"PeriodicalId\":423671,\"journal\":{\"name\":\"2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AEEICB.2018.8480948\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AEEICB.2018.8480948","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimation of software project time prediction using Computational Neural Networks
Programming venture administration (SPM) will be a standout amongst the grade variables will program victory alternately disappointment. Prediction for product improvement chance may be the key errand for the successful SPM. Those correctness and unwavering quality of prediction components may be likewise imperative. In this project, we look at distinctive machine Taking in strategies in place should faultlessly foresee the programming chance. The point when chronicled venture exert data, span information Furthermore other task information would utilized for exertion or span prediction, the essential inspiration behind this endeavor will be that those information put away in the recorded datasets might be used to create predictive models, Eventually Tom’s perusing Possibly Factual systems for example, straight relapse Also correspondence Investigation or machine Taking in strategies for example, ANN (Artificial neural Network) Also SVM (Support vector Machine), with anticipate the exert alternately span about ventures.