Mohamed Hamada, Abdelrahman Abdallah, M. Kasem, Mohamed Abokhalil
{"title":"优化软件项目时间和进度的神经网络估计模型","authors":"Mohamed Hamada, Abdelrahman Abdallah, M. Kasem, Mohamed Abokhalil","doi":"10.1109/SIST50301.2021.9465887","DOIUrl":null,"url":null,"abstract":"Software projects have a probability of high failure rates that appear to linger around 60% for significant IT projects. Estimating time and project schedule are crucial tasks and extremely influence the project outcomes. Artificial Intelligence now can provide multiple solutions for most problems of software projects. This article aims to develop a Neural Network estimation model to manipulate the problem of timing for software projects. The model can predict the estimation value of project time which optimizes the scheduling process, the developed model achieved high accuracy after testing through the test datasets.","PeriodicalId":318915,"journal":{"name":"2021 IEEE International Conference on Smart Information Systems and Technologies (SIST)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Neural Network Estimation Model to Optimize Timing and Schedule of Software Projects\",\"authors\":\"Mohamed Hamada, Abdelrahman Abdallah, M. Kasem, Mohamed Abokhalil\",\"doi\":\"10.1109/SIST50301.2021.9465887\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Software projects have a probability of high failure rates that appear to linger around 60% for significant IT projects. Estimating time and project schedule are crucial tasks and extremely influence the project outcomes. Artificial Intelligence now can provide multiple solutions for most problems of software projects. This article aims to develop a Neural Network estimation model to manipulate the problem of timing for software projects. The model can predict the estimation value of project time which optimizes the scheduling process, the developed model achieved high accuracy after testing through the test datasets.\",\"PeriodicalId\":318915,\"journal\":{\"name\":\"2021 IEEE International Conference on Smart Information Systems and Technologies (SIST)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Smart Information Systems and Technologies (SIST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIST50301.2021.9465887\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Smart Information Systems and Technologies (SIST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIST50301.2021.9465887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural Network Estimation Model to Optimize Timing and Schedule of Software Projects
Software projects have a probability of high failure rates that appear to linger around 60% for significant IT projects. Estimating time and project schedule are crucial tasks and extremely influence the project outcomes. Artificial Intelligence now can provide multiple solutions for most problems of software projects. This article aims to develop a Neural Network estimation model to manipulate the problem of timing for software projects. The model can predict the estimation value of project time which optimizes the scheduling process, the developed model achieved high accuracy after testing through the test datasets.