{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:49:20Z","timestamp":1701478160711},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>Identifying wild mushroom species are an important way to prevent poisoning from consuming toxic wild mushrooms. Therefore, an attention-based method for fine-grained classification of wild mushrooms is proposed, in which an attention mechanism is incorporated and a new network model structure is constructed in combination with a residual module. Firstly, nine diseased wild mushroom image samples were collected, and in order to make the model have better generalization ability, the images were pre-processed so that the number of samples reached 7200, and the experimental results showed that the improved deep residual network model achieved about 99.12% accuracy in classifying and recognizing the established wild mushroom database, and the improved neural network had a great improvement in classification accuracy.<\/jats:p>","DOI":"10.3233\/faia230862","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:55:12Z","timestamp":1701446112000},"source":"Crossref","is-referenced-by-count":0,"title":["Fine-Grained Classification of Wild Fungi Based on Attention Residual Mechanism"],"prefix":"10.3233","author":[{"given":"Shuoyi","family":"Wen","sequence":"first","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Yang","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailong","family":"Duan","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230862","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:55:14Z","timestamp":1701446114000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230862","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}