{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:29:04Z","timestamp":1762514944865,"version":"build-2065373602"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s13042-025-02743-5","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T18:13:51Z","timestamp":1752776031000},"page":"9079-9093","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-model fusion method for detecting the feeding behavior of sea bass"],"prefix":"10.1007","volume":"16","author":[{"given":"Wan Yi","family":"Li","sequence":"first","affiliation":[]},{"given":"Teng Ke","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zi Heng","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Qiu Xian","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jun Quan","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Li","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Hai Xia","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Ling","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Juan","family":"Zou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"2743_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.54216\/JAIM.080101","volume":"8","author":"M Ahmed","year":"2024","unstructured":"Ahmed M, Gamal M, Ismail I, El Kenawy E, El-Din H (2024) An AI-based system for predicting renewable energy power output using advanced optimization algorithms. J Artif Intell Metaheuristics 8:1\u20138. https:\/\/doi.org\/10.54216\/JAIM.080101","journal-title":"J Artif Intell Metaheuristics"},{"key":"2743_CR2","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., & Sun, J. (2021). Repvgg: making vgg-style convnets great again. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"2743_CR3","doi-asserted-by":"publisher","first-page":"122147","DOI":"10.1016\/j.eswa.2023.122147","volume":"238","author":"E-SM El-kenawy","year":"2024","unstructured":"El-kenawy E-SM, Khodadadi N, Mirjalili S, Abdelhamid AA, Eid MM, Ibrahim A (2024) Greylag goose optimization: nature-inspired optimization algorithm. Expert Syst Appl 238:122147. https:\/\/doi.org\/10.1016\/j.eswa.2023.122147","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2743_CR4","doi-asserted-by":"publisher","first-page":"24","DOI":"10.20517\/ais.2021.15","volume":"2","author":"E Elyan","year":"2022","unstructured":"Elyan E, Vuttipittayamongkol P, Johnston P, Martin K, McPherson K, Moreno-Garc\u00eda CF, Jayne C, Sarker MMK (2022) Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. Artif Intell Surg 2(1):24\u201345. https:\/\/doi.org\/10.20517\/ais.2021.15","journal-title":"Artif Intell Surg"},{"key":"2743_CR5","doi-asserted-by":"publisher","unstructured":"Guo Qiang GQ, Yang XinTing YX, Zhou Chao ZC, Lin Kai LK, Sun ChuanHeng SC, Chen Ming CM (2018) Fish feeding behavior detection method based on shape and texture features. https:\/\/doi.org\/10.12024\/jsou.20170802112","DOI":"10.12024\/jsou.20170802112"},{"key":"2743_CR6","doi-asserted-by":"crossref","unstructured":"Lan H-Y, Cheng S-C, Lu H-Y, Chang C-C, Li S-Y, Huang C-T (2024) A novel process-based digital twin for intelligent fish feeding management using multi-mode sensors and smart feeding machine. https:\/\/www.preprints.org\/manuscript\/202401.0927","DOI":"10.20944\/preprints202401.0927.v1"},{"key":"2743_CR7","doi-asserted-by":"publisher","first-page":"735508","DOI":"10.1016\/j.aquaculture.2020.735508","volume":"528","author":"D Li","year":"2020","unstructured":"Li D, Wang Z, Wu S, Miao Z, Du L, Duan Y (2020) Automatic recognition methods of fish feeding behavior in aquaculture: a review. Aquaculture 528:735508. https:\/\/doi.org\/10.1016\/j.aquaculture.2020.735508","journal-title":"Aquaculture"},{"issue":"12","key":"2743_CR8","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2021","unstructured":"Li Z, Liu F, Yang W, Peng S, Zhou J (2021) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst 33(12):6999\u20137019. https:\/\/doi.org\/10.1109\/TNNLS.2021.3084827","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2743_CR9","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.tifs.2021.04.042","volume":"113","author":"Y Liu","year":"2021","unstructured":"Liu Y, Pu H, Sun D-W (2021) Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices. Trends Food Sci Technol 113:193\u2013204. https:\/\/doi.org\/10.1016\/j.tifs.2021.04.042","journal-title":"Trends Food Sci Technol"},{"issue":"2","key":"2743_CR10","doi-asserted-by":"publisher","first-page":"01","DOI":"10.54216\/MOR.010201","volume":"1","author":"ESM El-kenawy","year":"2024","unstructured":"El-kenawy ESM, Eid MM, Abualigah LJMOR (2024) Machine learning in public health forecasting and monitoring the Zika virus. Metaheur Optim Rev 1(2):01\u201311. https:\/\/doi.org\/10.54216\/MOR.010201","journal-title":"Metaheur Optim Rev"},{"issue":"6","key":"2743_CR11","doi-asserted-by":"publisher","first-page":"2063","DOI":"10.1109\/TNNLS.2018.2790388","volume":"29","author":"M Mahmud","year":"2018","unstructured":"Mahmud M, Kaiser MS, Hussain A, Vassanelli S (2018) Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst 29(6):2063\u20132079. https:\/\/doi.org\/10.1109\/TNNLS.2018.2790388","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2743_CR12","doi-asserted-by":"publisher","first-page":"105087","DOI":"10.1016\/j.compag.2019.105087","volume":"167","author":"H M\u00e5l\u00f8y","year":"2019","unstructured":"M\u00e5l\u00f8y H, Aamodt A, Misimi E (2019) A spatio-temporal recurrent network for salmon feeding action recognition from underwater videos in aquaculture. Comput Electron Agric 167:105087. https:\/\/doi.org\/10.1016\/j.compag.2019.105087","journal-title":"Comput Electron Agric"},{"issue":"3","key":"2743_CR13","doi-asserted-by":"publisher","first-page":"2791","DOI":"10.1007\/s10499-023-01297-z","volume":"32","author":"A Mandal","year":"2024","unstructured":"Mandal A, Ghosh AR (2024) Role of artificial intelligence (AI) in fish growth and health status monitoring: a review on sustainable aquaculture. Aquacult Int 32(3):2791\u20132820. https:\/\/doi.org\/10.1007\/s10499-023-01297-z","journal-title":"Aquacult Int"},{"key":"2743_CR14","doi-asserted-by":"crossref","unstructured":"Mi Y, Bipin PK, Shah RK (2019). Using Lucas\u2013Kanade algorithms to measure human movement. information, communication and computing technology: third international conference, ICICCT 2018, New Delhi, India, May 12, 2018, Revised Selected Papers 3","DOI":"10.1007\/978-981-13-5992-7_10"},{"issue":"1","key":"2743_CR15","doi-asserted-by":"publisher","first-page":"82","DOI":"10.14321\/aehm.024.01.12","volume":"24","author":"J Munguti","year":"2021","unstructured":"Munguti J, Odame H, Kirimi J, Obiero K, Ogello E, Liti D (2021) Fish feeds and feed management practices in the Kenyan aquaculture sector: Challenges and opportunities. Aquat Ecosyst Health Manag 24(1):82\u201389. https:\/\/doi.org\/10.14321\/aehm.024.01.12","journal-title":"Aquat Ecosyst Health Manag"},{"issue":"4","key":"2743_CR16","doi-asserted-by":"publisher","first-page":"2076","DOI":"10.1111\/raq.12559","volume":"13","author":"UF Mustapha","year":"2021","unstructured":"Mustapha UF, Alhassan AW, Jiang DN, Li GL (2021) Sustainable aquaculture development: a review on the roles of cloud computing, internet of things and artificial intelligence (CIA). Rev Aquac 13(4):2076\u20132091. https:\/\/doi.org\/10.1111\/raq.12559","journal-title":"Rev Aquac"},{"issue":"3","key":"2743_CR17","first-page":"11","volume":"9","author":"HD Ranasinghe","year":"2024","unstructured":"Ranasinghe HD (2024) Balancing efficiency, accuracy, and ethical concerns in the development and implementation of computer vision machine learning solutions. Int J Soc Anal 9(3):11\u201320","journal-title":"Int J Soc Anal"},{"key":"2743_CR18","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.aquaculture.2014.04.008","volume":"430","author":"B Sadoul","year":"2014","unstructured":"Sadoul B, Mengues PE, Friggens NC, Prunet P, Colson V (2014) A new method for measuring group behaviours of fish shoals from recorded videos taken in near aquaculture conditions. Aquaculture 430:179\u2013187. https:\/\/doi.org\/10.1016\/j.aquaculture.2014.04.008","journal-title":"Aquaculture"},{"key":"2743_CR19","doi-asserted-by":"publisher","first-page":"107500","DOI":"10.1016\/j.compag.2022.107500","volume":"203","author":"L Sun","year":"2022","unstructured":"Sun L, Wang B, Yang P, Wang X, Li D, Wang J (2022) Water quality parameter analysis model based on fish behavior. Comput Electron Agric 203:107500","journal-title":"Comput Electron Agric"},{"key":"2743_CR20","doi-asserted-by":"publisher","first-page":"102178","DOI":"10.1016\/j.aquaeng.2021.102178","volume":"94","author":"N Ubina","year":"2021","unstructured":"Ubina N, Cheng S-C, Chang C-C, Chen H-Y (2021) Evaluating fish feeding intensity in aquaculture with convolutional neural networks. Aquacult Eng 94:102178. https:\/\/doi.org\/10.1016\/j.aquaeng.2021.102178","journal-title":"Aquacult Eng"},{"key":"2743_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s10499-021-00773-8","author":"C Wang","year":"2021","unstructured":"Wang C, Li Z, Wang T, Xu X, Zhang X, Li D (2021) Intelligent fish farm\u2014the future of aquaculture. Aquac Int. https:\/\/doi.org\/10.1007\/s10499-021-00773-8","journal-title":"Aquac Int"},{"issue":"10","key":"2743_CR22","doi-asserted-by":"publisher","first-page":"236","DOI":"10.6041\/j.issn.1000-1298.2022.10.025","volume":"53","author":"L Xu","year":"2021","unstructured":"Xu L, Huang X, Liu S (2021) Recognition of fish feeding intensity based on improved LRCN. Trans Chin Soc Agric Mach 53(10):236\u2013241. https:\/\/doi.org\/10.6041\/j.issn.1000-1298.2022.10.025","journal-title":"Trans Chin Soc Agric Mach"},{"key":"2743_CR23","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.neucom.2023.01.056","volume":"527","author":"S Xu","year":"2023","unstructured":"Xu S, Zhang M, Song W, Mei H, He Q, Liotta A (2023) A systematic review and analysis of deep learning-based underwater object detection. Neurocomputing 527:204\u2013232. https:\/\/doi.org\/10.1016\/j.neucom.2023.01.056","journal-title":"Neurocomputing"},{"key":"2743_CR24","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.patrec.2018.05.018","volume":"118","author":"G Yao","year":"2019","unstructured":"Yao G, Lei T, Zhong J (2019) A review of convolutional-neural-network-based action recognition. Pattern Recogn Lett 118:14\u201322. https:\/\/doi.org\/10.1016\/j.patrec.2018.05.018","journal-title":"Pattern Recogn Lett"},{"key":"2743_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107580","volume":"204","author":"Y Zeng","year":"2023","unstructured":"Zeng Y, Yang X, Pan L, Zhu W, Wang D, Zhao Z, Liu J, Sun C, Zhou C (2023) Fish school feeding behavior quantification using acoustic signal and improved Swin Transformer. Comput Electron Agric 204:107580","journal-title":"Comput Electron Agric"},{"key":"2743_CR26","doi-asserted-by":"crossref","unstructured":"Zhang QL, Yang YB (2021) Sa-net: shuffle attention for deep convolutional neural networks. ICASSP 2021\u20132021 IEEE international conference on acoustics, speech and signal processing (ICASSP)","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"2743_CR27","doi-asserted-by":"publisher","first-page":"736724","DOI":"10.1016\/j.aquaculture.2021.736724","volume":"540","author":"S Zhao","year":"2021","unstructured":"Zhao S, Zhang S, Liu J, Wang H, Zhu J, Li D, Zhao R (2021) Application of machine learning in intelligent fish aquaculture: A review. Aquaculture 540:736724. https:\/\/doi.org\/10.1016\/j.aquaculture.2021.736724","journal-title":"Aquaculture"},{"issue":"7","key":"2743_CR28","doi-asserted-by":"publisher","first-page":"38","DOI":"10.11975\/j.issn.1002-6819.2022.07.005","volume":"38","author":"Z Ming","year":"2022","unstructured":"Ming Z, Zhenfu Z, Huang H, Hao C, Xinwei C, Tao D (2022) Research progress on intelligent feeding methods in fish farming. Trans Chin Soc Agric Eng 38(7):38\u201347. https:\/\/doi.org\/10.11975\/j.issn.1002-6819.2022.07.005","journal-title":"Trans Chin Soc Agric Eng"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02743-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02743-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02743-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:24:13Z","timestamp":1762514653000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02743-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,17]]},"references-count":28,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["2743"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02743-5","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,7,17]]},"assertion":[{"value":"2 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}