{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:05:17Z","timestamp":1753884317263,"version":"3.41.2"},"reference-count":43,"publisher":"World Scientific Pub Co Pte Ltd","issue":"10","funder":[{"name":"Major Project of Chongqing Municipal Education Commission","award":["KJZD-M202000801"],"award-info":[{"award-number":["KJZD-M202000801"]}]},{"name":"National Key Research and Development Program of China","award":["2016YFE0205600"],"award-info":[{"award-number":["2016YFE0205600"]}]},{"name":"Innovation Group of New Technologies","award":["CXQT19023"],"award-info":[{"award-number":["CXQT19023"]}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJQN201900833"],"award-info":[{"award-number":["KJQN201900833"]}]},{"name":"Key Research Project of Chongqing Technology and Business University","award":["ZDPTTD201917"],"award-info":[{"award-number":["ZDPTTD201917"]}]},{"name":"Key Research Project of Chongqing Technology and Business University","award":["1953013"],"award-info":[{"award-number":["1953013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2023,7,15]]},"abstract":"<jats:p> With the development of emerging information technology, the traditional management methods of marine fishes are slowly replaced by new methods due to high cost, time-consumption and inaccurate management. The update of marine fishes management technology is also a great help for the creation of smart cities. However, some new methods have been studied that are too specific, which are not applicable for the other marine fishes, and the accuracy of identification is generally low. Therefore, this paper proposes an ecological Internet of Things (IoT) framework, in which a lightweight Deep Neural Networks model is implemented as a image recognition model for marine fishes, which is recorded as Fish-CNN. In this study, multi-training and evaluation of Fish-CNN is accomplished, and the accuracy of the final classification can be fixed to 89.89%\u201399.83%. Moreover, the final evaluation compared with Rem-CNN, Linear Regression and Multilayer Perceptron also verify the stability and advantage of our method. <\/jats:p>","DOI":"10.1142\/s0218126623501694","type":"journal-article","created":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T15:51:05Z","timestamp":1674661865000},"source":"Crossref","is-referenced-by-count":3,"title":["Lightweight CNN-Based Image Recognition with Ecological IoT Framework for Management of Marine Fishes"],"prefix":"10.1142","volume":"32","author":[{"given":"Lulu","family":"Jia","sequence":"first","affiliation":[{"name":"National Research Base of Intelligent Manufacturing Service, School of Environment and Resources, Chongqing Technology and Business University, Chongqing 400067, P. R. 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