{"title":"Ant Algorithm Based on Internet of Things in Image Recognition System","authors":"Hang Yu, Yujie Wang, Jiajia Song","doi":"10.1109/ITNEC56291.2023.10082625","DOIUrl":null,"url":null,"abstract":"With the growth of society, people have higher and higher requirements for the quality of life, and the Internet has become an indispensable part of our daily life. Ants are very typical, common, convenient, creative and convenient. This paper mainly introduces the methods based on the Internet of Things and ant colony computing. By analyzing the research status of ant algorithm at home and abroad and relevant literature, we draw conclusions, and propose improvement plans to improve the theoretical system in this field, further optimize the image recognition application of medical equipments and medicine materials in the Internet of Things environment. Then, according to the system functions to be achieved in this paper, we determine the objective function, design indicators and parameters, so as to extract features. Finally, we get the optimal solution of the feature vector, and then send the data to the background database to obtain the recognition results, And verify the model in the experiment This paper designs a simple, low-cost, efficient and high-precision recognition system based on the ant algorithm to test. Through the image recognition experiment for medical equipments and drug materials in the Internet of Things environment, the image recognition system based on the ant algorithm achieves the feature extraction time within 20 seconds, while driving the system recognition time to reach 26 seconds, with a feature matching rate of more than 82%, which can fully meet the user’s image recognition needs, The scheme not only saves resources, but also has high practical value.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITNEC56291.2023.10082625","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
With the growth of society, people have higher and higher requirements for the quality of life, and the Internet has become an indispensable part of our daily life. Ants are very typical, common, convenient, creative and convenient. This paper mainly introduces the methods based on the Internet of Things and ant colony computing. By analyzing the research status of ant algorithm at home and abroad and relevant literature, we draw conclusions, and propose improvement plans to improve the theoretical system in this field, further optimize the image recognition application of medical equipments and medicine materials in the Internet of Things environment. Then, according to the system functions to be achieved in this paper, we determine the objective function, design indicators and parameters, so as to extract features. Finally, we get the optimal solution of the feature vector, and then send the data to the background database to obtain the recognition results, And verify the model in the experiment This paper designs a simple, low-cost, efficient and high-precision recognition system based on the ant algorithm to test. Through the image recognition experiment for medical equipments and drug materials in the Internet of Things environment, the image recognition system based on the ant algorithm achieves the feature extraction time within 20 seconds, while driving the system recognition time to reach 26 seconds, with a feature matching rate of more than 82%, which can fully meet the user’s image recognition needs, The scheme not only saves resources, but also has high practical value.