{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T08:08:27Z","timestamp":1767168507993,"version":"build-2238731810"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T00:00:00Z","timestamp":1623801600000},"content-version":"vor","delay-in-days":166,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Image recognition is an important field of artificial intelligence. Its basic idea is to use computers to automatically classify different scenes in the acquired images, instead of traditional manual classification tasks. In this paper, through the analysis of rough set theory and artificial intelligence network, as well as the role of the two in image recognition, the rough set theory and artificial intelligence network are organically combined, and a network based on rough set theory and artificial intelligence network is proposed. Using BP artificial intelligence network model, improved BP artificial intelligence network model, and improved PSO\u2010SVM model to identify and classify the extracted characteristic signals and compare the results, all reached 85% correct rate. The PCA and SVM are combined and applied to the MNIST handwritten digit collection for recognition and classification. At the data level, dimensionality reduction is performed on high\u2010dimensional image data to compress the data. This greatly improves the performance of the algorithm, the recognition accuracy rate is as high as 98%, and the running time is shortened by about 90%. The model first preprocesses the original image data and then uses rough set theory to select features, which reduces the input dimension of the artificial intelligence network, improves the learning and recognition speed of the artificial intelligence network, and further improves the accuracy of recognition. The paper applies the model to handwritten digital image recognition, and the experimental results show that the model is effective and feasible. The system has the characteristics of easy deployment and easy maintenance and integration. Experiments show that the system has good time characteristics in the process of multialgorithm parallel image fusion processing.<\/jats:p>","DOI":"10.1155\/2021\/8328532","type":"journal-article","created":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T17:05:07Z","timestamp":1623863107000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["[Retracted] Using an Improved PSO\u2010SVM Model to Recognize and Classify the Image Signals"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4516-4327","authenticated-orcid":false,"given":"Ying","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Guocheng","family":"Wei","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6,16]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2020.03.056"},{"key":"e_1_2_8_2_2","first-page":"4730","article-title":"Image recognition method based on deep learning","volume":"4","author":"Jia X.","year":"2019","journal-title":"Control and Decision"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00155579"},{"key":"e_1_2_8_4_2","first-page":"18","article-title":"Distributed artificial intelligence and knowledge management: ontologies and multi-agent systems for a corporate semantic web","volume":"2","author":"Gandon F.","year":"2020","journal-title":"IEEE Antipolis"},{"key":"e_1_2_8_5_2","first-page":"542","article-title":"Image recognition based on deep learning","volume":"6","author":"Wu M.","year":"2019","journal-title":"Automation Content"},{"key":"e_1_2_8_6_2","first-page":"21","article-title":"Siamese neural networks for one-shot image recognition","volume":"2","author":"Koch G.","year":"2018","journal-title":"ICML Deep Learning Workshop"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3006097"},{"key":"e_1_2_8_8_2","first-page":"1","article-title":"PCANN: distributed ANN architecture for image recognition in resource-constrained IoT devices","volume":"4","author":"Bi T.","year":"2019","journal-title":"Intelligent Environments"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0235783"},{"key":"e_1_2_8_10_2","first-page":"664","article-title":"Lightweight classification of IoT malware based on image recognition","volume":"2","author":"Su J.","year":"2018","journal-title":"Computer Software"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.3233\/ica-170551"},{"key":"e_1_2_8_12_2","first-page":"296","article-title":"Image recognition of 85 food categories by feature fusion","volume":"3","author":"Hoashi H.","year":"2019","journal-title":"International Symposium on Multimedia"},{"key":"e_1_2_8_13_2","first-page":"8697","article-title":"Learning transferable architectures for scalable image recognition","volume":"3","author":"Zoph B.","year":"2018","journal-title":"Proceedings of the Computer Vision and Pattern Recognition"},{"key":"e_1_2_8_14_2","doi-asserted-by":"crossref","unstructured":"ZhangY. 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