{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:24:03Z","timestamp":1742927043227,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031578694"},{"type":"electronic","value":"9783031578700"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-57870-0_9","type":"book-chapter","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T04:01:52Z","timestamp":1712635312000},"page":"97-107","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimising Water Quality Classification in\u00a0Aquaculture Using a\u00a0New Parameter Pre-selection Approach"],"prefix":"10.1007","author":[{"given":"Mahdi","family":"Hamzaoui","sequence":"first","affiliation":[]},{"given":"Mohamed Ould-Elhassen","family":"Aoueileyine","sequence":"additional","affiliation":[]},{"given":"Lamia","family":"Romdhani","sequence":"additional","affiliation":[]},{"given":"Ridha","family":"Bouallegue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,10]]},"reference":[{"key":"9_CR1","first-page":"1","volume":"22","author":"J Nakai","year":"2018","unstructured":"Nakai, J.: Food and agriculture organization of the united nations and the sustainable development goals. Sustain. Dev. 22, 1\u2013450 (2018)","journal-title":"Sustain. Dev."},{"key":"9_CR2","unstructured":"Delgado, C.L.: Fish to 2020: Supply and Demand in Changing Global Markets, vol. 62. WorldFish (2003)"},{"issue":"9\u201310","key":"9_CR3","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/S0964-5691(01)00074-6","volume":"44","author":"JF Caddy","year":"2001","unstructured":"Caddy, J.F., Cochrane, K.L.: A review of fisheries management past and present and some future perspectives for the third millennium. Ocean Coast. Manag. 44(9\u201310), 653\u2013682 (2001)","journal-title":"Ocean Coast. Manag."},{"key":"9_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2021.112271","volume":"287","author":"A Ahmad","year":"2021","unstructured":"Ahmad, A., Abdullah, S.R.S., Hasan, H.A., Othman, A.R., Ismail, N.I.: Aquaculture industry: supply and demand, best practices, effluent and its current issues and treatment technology. J. Environ. Manag. 287, 112271 (2021)","journal-title":"J. Environ. Manag."},{"issue":"19","key":"9_CR5","doi-asserted-by":"publisher","first-page":"10685","DOI":"10.3390\/su131910685","volume":"13","author":"T Kassem","year":"2021","unstructured":"Kassem, T., Shahrour, I., El Khattabi, J., Raslan, A.: Smart and sustainable aquaculture farms. Sustainability 13(19), 10685 (2021)","journal-title":"Sustainability"},{"key":"9_CR6","doi-asserted-by":"publisher","first-page":"4967870","DOI":"10.1155\/2017\/4967870","volume":"2017","author":"Z Xiao","year":"2017","unstructured":"Xiao, Z., Peng, L., Chen, Y., Liu, H., Wang, J., Nie, Y.: The dissolved oxygen prediction method based on neural network. Complexity 2017, 4967870 (2017)","journal-title":"Complexity"},{"key":"9_CR7","doi-asserted-by":"publisher","first-page":"24784","DOI":"10.1109\/ACCESS.2020.2971253","volume":"8","author":"J Liu","year":"2020","unstructured":"Liu, J., et al.: Accurate prediction scheme of water quality in smart mariculture with deep Bi-S-SRU learning network. IEEE Access 8, 24784\u201324798 (2020)","journal-title":"IEEE Access"},{"key":"9_CR8","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/s40808-017-0406-9","volume":"4","author":"D Dezfooli","year":"2018","unstructured":"Dezfooli, D., Hosseini-Moghari, S.M., Ebrahimi, K., Araghinejad, S.: Classification of water quality status based on minimum quality parameters: application of machine learning techniques. Model. Earth Syst. Environ. 4, 311\u2013324 (2018)","journal-title":"Model. Earth Syst. Environ."},{"issue":"18","key":"9_CR9","doi-asserted-by":"publisher","first-page":"2836","DOI":"10.3390\/w14182836","volume":"14","author":"T Li","year":"2022","unstructured":"Li, T., Lu, J., Wu, J., Zhang, Z., Chen, L.: Predicting aquaculture water quality using machine learning approaches. Water 14(18), 2836 (2022)","journal-title":"Water"},{"issue":"5","key":"9_CR10","doi-asserted-by":"publisher","first-page":"4855","DOI":"10.1007\/s12652-020-01900-8","volume":"12","author":"AP Rozario","year":"2021","unstructured":"Rozario, A.P., Devarajan, N.: Monitoring the quality of water in shrimp ponds and forecasting of dissolved oxygen using Fuzzy C means clustering based radial basis function neural networks. J. Ambient. Intell. Humaniz. Comput. 12(5), 4855\u20134862 (2021)","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"9_CR11","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.engappai.2013.09.019","volume":"29","author":"S Liu","year":"2014","unstructured":"Liu, S., Xu, L., Jiang, Y., Li, D., Chen, Y., Li, Z.: A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture. Eng. Appl. Artif. Intell. 29, 114\u2013124 (2014)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"5","key":"9_CR12","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1080\/10798587.2012.10643265","volume":"18","author":"Z Li","year":"2012","unstructured":"Li, Z., Jiang, Y., Yue, J., Zhang, L., Li, D.: An improved gray model for aquaculture water quality prediction. Intell. Autom. Soft Comput. 18(5), 557\u2013567 (2012)","journal-title":"Intell. Autom. Soft Comput."},{"issue":"3\u20134","key":"9_CR13","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.mcm.2011.11.021","volume":"58","author":"S Liu","year":"2013","unstructured":"Liu, S., Tai, H., Ding, Q., Li, D., Xu, L., Wei, Y.: A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math. Comput. Model. 58(3\u20134), 458\u2013465 (2013)","journal-title":"Math. Comput. Model."},{"issue":"13","key":"9_CR14","doi-asserted-by":"publisher","first-page":"1782","DOI":"10.3390\/w13131782","volume":"13","author":"E Eze","year":"2021","unstructured":"Eze, E., Halse, S., Ajmal, T.: Developing a novel water quality prediction model for a South African aquaculture farm. Water 13(13), 1782 (2021)","journal-title":"Water"},{"issue":"1","key":"9_CR15","first-page":"11","volume":"5","author":"C Li","year":"2018","unstructured":"Li, C., Li, Z., Wu, J., Zhu, L., Yue, J.: A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features. Inf. Process. Agric. 5(1), 11\u201320 (2018)","journal-title":"Inf. Process. Agric."},{"key":"9_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2021.104329","volume":"214","author":"S Dilmi","year":"2021","unstructured":"Dilmi, S., Ladjal, M.: A novel approach for water quality classification based on the integration of deep learning and feature extraction techniques. Chemom. Intell. Lab. Syst. 214, 104329 (2021)","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"9_CR17","series-title":"CCIS","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/978-3-031-41774-0_23","volume-title":"ICCCI 2023","author":"M Hamzaoui","year":"2023","unstructured":"Hamzaoui, M., Aoueileyine, M.O.E., Bouallegue, R.: A hybrid method of K-nearest neighbors with decision tree for water quality classification in aquaculture. In: Nguyen, N.T., et al. (eds.) ICCCI 2023. CCIS, vol. 1864, pp. 287\u2013299. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-41774-0_23"},{"issue":"10","key":"9_CR18","doi-asserted-by":"publisher","first-page":"505","DOI":"10.3390\/fishes8100505","volume":"8","author":"M Hamzaoui","year":"2023","unstructured":"Hamzaoui, M., Aoueileyine, M.O.E., Romdhani, L., Bouallegue, R.: Optimizing XGBoost performance for fish weight prediction through parameter pre-selection. Fishes 8(10), 505 (2023)","journal-title":"Fishes"}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Advanced Information Networking and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-57870-0_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T04:08:31Z","timestamp":1712635711000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-57870-0_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031578694","9783031578700"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-57870-0_9","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"type":"print","value":"2367-4512"},{"type":"electronic","value":"2367-4520"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"10 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AINA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Information Networking and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kitakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 April 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"38","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aina2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/voyager.ce.fit.ac.jp\/conf\/aina\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}