{"title":"Implementation and optimization of ORB-SLAM2 algorithm based on ROS on mobile robots (Erratum)","authors":"Guimao Zhang, Bo Liu, Zhihong Liang","doi":"10.1117/12.2689253","DOIUrl":"https://doi.org/10.1117/12.2689253","url":null,"abstract":"","PeriodicalId":380630,"journal":{"name":"Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125465769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous advancement of science and technology, network data dissemination technology has been rapidly developed, but at the same time, the problem of spam is becoming more and more serious. This paper aims to analyse the classification principle of each machine learning classifier and compare the differences in the classification effect of each classifier. For the classification of spam, this paper mainly does the following aspects of the work: first, the selection of mail datasets and the pre-processing of email text information, the second is the extraction of the text characteristics of the email, The data is then divided into two parts, one for training and one for testing, using each machine to train the model, using the test set to test the model. Finally, parameters such as accuracy, precision, recall, and F1 score are calculated according to the mail classification results, comparing each model. The results show that the best classification effect is the neural network model and the naïve Bayes model, which can obtain better generalization ability in the test data and should be applied more in practice.
{"title":"Comparison and analysis of the accuracy of multiple machine learning algorithms in the field of spam classification","authors":"liu junchen","doi":"10.1117/12.2675259","DOIUrl":"https://doi.org/10.1117/12.2675259","url":null,"abstract":"With the continuous advancement of science and technology, network data dissemination technology has been rapidly developed, but at the same time, the problem of spam is becoming more and more serious. This paper aims to analyse the classification principle of each machine learning classifier and compare the differences in the classification effect of each classifier. For the classification of spam, this paper mainly does the following aspects of the work: first, the selection of mail datasets and the pre-processing of email text information, the second is the extraction of the text characteristics of the email, The data is then divided into two parts, one for training and one for testing, using each machine to train the model, using the test set to test the model. Finally, parameters such as accuracy, precision, recall, and F1 score are calculated according to the mail classification results, comparing each model. The results show that the best classification effect is the neural network model and the naïve Bayes model, which can obtain better generalization ability in the test data and should be applied more in practice.","PeriodicalId":380630,"journal":{"name":"Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126746262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ji Yawen, Zhou Jie, Liu Bingqin, Shi Xiaomin, Yang Yuxiao, Wang Hongyan, Fan Youchen
For soldier recognition in the battlefield environment, there are factors such as camouflage and object occlusion, thus leading to incomplete feature information and poor recognition effect. In this paper, we first construct a soldier target dataset conforming to the characteristics of the battlefield environment by analyzing the factors influencing the battlefield environment. Then this paper improves the yolov5 algorithm to detect soldier recognition quickly by adding a channel attention mechanism and improving the spatial pyramid pooling structure. The implementation results show that the predicted mAP value can reach 0.946 with a 3% improvement, the recall rate reaches 0.86, and the detection speed is improved by 5%. It achieves better recognition of soldiers in the battlefield environment.
{"title":"Soldier identification based on improved YOLOv5 algorithm in battlefield environment","authors":"Ji Yawen, Zhou Jie, Liu Bingqin, Shi Xiaomin, Yang Yuxiao, Wang Hongyan, Fan Youchen","doi":"10.1117/12.2675309","DOIUrl":"https://doi.org/10.1117/12.2675309","url":null,"abstract":"For soldier recognition in the battlefield environment, there are factors such as camouflage and object occlusion, thus leading to incomplete feature information and poor recognition effect. In this paper, we first construct a soldier target dataset conforming to the characteristics of the battlefield environment by analyzing the factors influencing the battlefield environment. Then this paper improves the yolov5 algorithm to detect soldier recognition quickly by adding a channel attention mechanism and improving the spatial pyramid pooling structure. The implementation results show that the predicted mAP value can reach 0.946 with a 3% improvement, the recall rate reaches 0.86, and the detection speed is improved by 5%. It achieves better recognition of soldiers in the battlefield environment.","PeriodicalId":380630,"journal":{"name":"Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115522863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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