Oghenevwegba T. Emuowhochere, E. Salawu, Samson O. Ongbali, O. Ajayi
{"title":"Future of Artificial Intelligence in Developing a Sustainable Intelligent Engineering Systems: A Review","authors":"Oghenevwegba T. Emuowhochere, E. Salawu, Samson O. Ongbali, O. Ajayi","doi":"10.4028/p-0wnidr","DOIUrl":null,"url":null,"abstract":"Studying the behaviour of engineering systems and processes from the perspective of applications of artificial intelligence provides an invaluable reference to improve their productivity and industrial development at large. This study comprehensively unveiled the problems faced by engineering systems and how artificial intelligence could be deployed as a technique for the future advancement of the industry. A brief background of the application of artificial intelligence in some selected engineering fields revealed that insufficient operational and process data from both plants and processes are major problems causing the survival of sustainable intelligent systems thereby, leading to incessant system failure. Furthermore, it was equally discovered that artificial intelligent for specific application are based on the data obtained from such application. Thus, there is no universally agreed artificial intelligent for a specific application. This made it a bit complex in developing intelligent systems. Keywords: Artificial Neural Network, Applications, Engineering, Training, Data.","PeriodicalId":518456,"journal":{"name":"International Conference on Sustainable Engineering and Materials Development (ICSEMD)","volume":"58 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Sustainable Engineering and Materials Development (ICSEMD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4028/p-0wnidr","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
Studying the behaviour of engineering systems and processes from the perspective of applications of artificial intelligence provides an invaluable reference to improve their productivity and industrial development at large. This study comprehensively unveiled the problems faced by engineering systems and how artificial intelligence could be deployed as a technique for the future advancement of the industry. A brief background of the application of artificial intelligence in some selected engineering fields revealed that insufficient operational and process data from both plants and processes are major problems causing the survival of sustainable intelligent systems thereby, leading to incessant system failure. Furthermore, it was equally discovered that artificial intelligent for specific application are based on the data obtained from such application. Thus, there is no universally agreed artificial intelligent for a specific application. This made it a bit complex in developing intelligent systems. Keywords: Artificial Neural Network, Applications, Engineering, Training, Data.