{"title":"Application of Artificial Neural Network with Various Algorithms Tools in Numerous Sectors – A Review","authors":"D. T. Kamatchi, D. B. V. Kumar","doi":"10.37896/pd91.4/91411","DOIUrl":null,"url":null,"abstract":"This study reports collective information of the Artificial Neural Network-(ANN) model implemented to improve the management and provide suggestions to the government and corporates. The ANN tool is an awesome architect model that helps collect a high volume of information from independent such as civilians, input parameters in manufacturing, etc to dependent such as government, corporates, machinery, etc. The ANN model has various architect tools that help to regulate and monitor the demand for government and the public through the weighted link, like brain neurons. It’s an optimized computation study to predict less uncertainty than experimental values. This literature study reports diverse methods of algorithms available in ANN models and briefly disseminated the procedure specifically to collect a high volume of inputs transferred to hidden neurons and passed to output terminal neurons with accurate solutions to the enhancement of government decision making. Besides, the ANN is a perfect substitute tool for experimental and numerical analysis procedures to reduce high expenses for collecting data in high volume. This literature report briefly disseminates different methods of ANN tools implemented in various applications with significant conclusions, considering many techniques for collecting data and suggestions provided for future research work.","PeriodicalId":20006,"journal":{"name":"Periodico Di Mineralogia","volume":"49 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Periodico Di Mineralogia","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.37896/pd91.4/91411","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
This study reports collective information of the Artificial Neural Network-(ANN) model implemented to improve the management and provide suggestions to the government and corporates. The ANN tool is an awesome architect model that helps collect a high volume of information from independent such as civilians, input parameters in manufacturing, etc to dependent such as government, corporates, machinery, etc. The ANN model has various architect tools that help to regulate and monitor the demand for government and the public through the weighted link, like brain neurons. It’s an optimized computation study to predict less uncertainty than experimental values. This literature study reports diverse methods of algorithms available in ANN models and briefly disseminated the procedure specifically to collect a high volume of inputs transferred to hidden neurons and passed to output terminal neurons with accurate solutions to the enhancement of government decision making. Besides, the ANN is a perfect substitute tool for experimental and numerical analysis procedures to reduce high expenses for collecting data in high volume. This literature report briefly disseminates different methods of ANN tools implemented in various applications with significant conclusions, considering many techniques for collecting data and suggestions provided for future research work.
期刊介绍:
Periodico di Mineralogia is an international peer-reviewed Open Access journal publishing Research Articles, Letters and Reviews in Mineralogy, Crystallography, Geochemistry, Ore Deposits, Petrology, Volcanology and applied topics on Environment, Archaeometry and Cultural Heritage. The journal aims at encouraging scientists to publish their experimental and theoretical results in as much detail as possible. Accordingly, there is no restriction on article length. Additional data may be hosted on the web sites as Supplementary Information. The journal does not have article submission and processing charges. Colour is free of charges both on line and printed and no Open Access fees are requested. Short publication time is assured.
Periodico di Mineralogia is property of Sapienza Università di Roma and is published, both online and printed, three times a year.