{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:06:05Z","timestamp":1750309565493,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":9,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,15]]},"DOI":"10.1145\/3718751.3718944","type":"proceedings-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T06:29:37Z","timestamp":1745821777000},"page":"1173-1178","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A case about container shipping market early warning based on PSO-LSSVM"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4250-0021","authenticated-orcid":false,"given":"Yu","family":"Tang","sequence":"first","affiliation":[{"name":"Institute of Port and Maritime Research, Zhejiang Institute of Transportation Science, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9438-1861","authenticated-orcid":false,"given":"Jing","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Port and Maritime Research, Zhejiang Institute of Transportation Science, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0683-341X","authenticated-orcid":false,"given":"Yuhang","family":"Che","sequence":"additional","affiliation":[{"name":"Dalian Maritime University of Transportation Engineering, Dalian, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1747-7106","authenticated-orcid":false,"given":"Aiqing","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Port and Maritime Research, Zhejiang Institute of Transportation Science, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8027-593X","authenticated-orcid":false,"given":"Shijun","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Port and Maritime Research, Zhejiang Institute of Transportation Science, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,4,27]]},"reference":[{"key":"e_1_3_3_1_1_2","volume-title":"Forecast of China's export container freight index based on LSTM-ARIMA combination model","author":"Wang L","year":"2023","unstructured":"Wang L, Liang JY, Zhou Y., \u201cForecast of China's export container freight index based on LSTM-ARIMA combination model,\u201d China Association for Science and Technology, Ministry of Transport, Chinese Academy of Engineering, Hubei Provincial People's Government.2023 World Transportation Congress WTC2023) Proceedings (Volume II). China Communications Press Co., Ltd, 2023:6."},{"key":"e_1_3_3_1_2_2","volume-title":"Research on early warning of shipping market based on prosperity index","author":"Chen J.","year":"2016","unstructured":"Chen J., \u201cResearch on early warning of shipping market based on prosperity index,\u201d Shanghai Jiao Tong University, 2016."},{"key":"e_1_3_3_1_3_2","unstructured":"Huang ML Xiao K Yuan XM et al. \u201cEvaluation of happy rivers and lakes based on entropy weight-TOPSIS multi-objective evaluation model \u201d Water Conservancy and Hydropower Bulletin:1-11(2024-01-30)."},{"key":"e_1_3_3_1_4_2","volume-title":"Container shipping market early warning based on LSTM neural network","author":"Tang Y","year":"2021","unstructured":"Tang Y, \u201cContainer shipping market early warning based on LSTM neural network\u201d. Dalian Maritime University, 2021."},{"key":"e_1_3_3_1_5_2","volume-title":"Technical supervision of water conservancy","author":"Wang J","year":"2024","unstructured":"Wang J, Li FS, Zhou ZM., \u201cResearch on safety monitoring of earth-rock dam based on optimization support vector machine,\u201d Technical supervision of water conservancy, 2024(01):33-35+58."},{"key":"e_1_3_3_1_6_2","volume-title":"Energy storage science and technology","author":"Wang YY","year":"2020","unstructured":"Wang YY, Li JB, Zhang F., \u201cLeast Squares Support Vector Machine Battery State Estimation Based on Particle Swarm Optimization Algorithm,\u201d Energy storage science and technology, 2020,(04):1153-1158."},{"key":"e_1_3_3_1_7_2","first-page":"e2","volume-title":"LS-SVM lab: a Matlab\/c Toolbox for Least Squares Support Vector Machines","author":"Packman","year":"2002","unstructured":"K. Packman, et al., \u201cLS-SVM lab: a Matlab\/c Toolbox for Least Squares Support Vector Machines,\u201d vol. 142, Tutorial. KULeuven-ESAT, Leuven, Belgium,2002,pp.le2."},{"key":"e_1_3_3_1_8_2","first-page":"7033","volume":"2021","author":"Issam A","unstructured":"M. S A, Issam A, Reza D, et al.An insight into the estimation of drilling fluid density at HPHT condition using PSO-, ICA-, and GA-LSSVM strategies[J]. Scientific Reports,2021,11(1):7033-7033.","journal-title":"Scientific Reports"},{"key":"e_1_3_3_1_9_2","first-page":"7647","volume":"2024","author":"Modeling","unstructured":"V. K, M. E E, M. O K. Modeling of discharge capacity of H-weir using experiments, bio-inspired optimization and data preprocess based on SVM[J]. International Journal of Environmental Science and Technology,2024,21(11):7647-7666.","journal-title":"International Journal of Environmental Science and Technology"}],"event":{"name":"ICBAR 2024: 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management","acronym":"ICBAR 2024","location":"Chengdu Guangdong China"},"container-title":["Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3718751.3718944","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3718751.3718944","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:19:07Z","timestamp":1750295947000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3718751.3718944"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,15]]},"references-count":9,"alternative-id":["10.1145\/3718751.3718944","10.1145\/3718751"],"URL":"https:\/\/doi.org\/10.1145\/3718751.3718944","relation":{},"subject":[],"published":{"date-parts":[[2024,11,15]]},"assertion":[{"value":"2025-04-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}