{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:19:57Z","timestamp":1750220397321,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":10,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,23]],"date-time":"2021-10-23T00:00:00Z","timestamp":1634947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"This study was supported by Education Science Research Project of Guangdong Province, China. (No.2018GXJK376)","award":["No.2018GXJK376"],"award-info":[{"award-number":["No.2018GXJK376"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,23]]},"DOI":"10.1145\/3495018.3495325","type":"proceedings-article","created":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T17:33:51Z","timestamp":1647279231000},"page":"1021-1024","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An Artificial Neural Network Model for Predicting the Competition Level of Vocational Students"],"prefix":"10.1145","author":[{"given":"Weijun","family":"Dai","sequence":"first","affiliation":[{"name":"Heyuan Polytechnic College, China"}]},{"given":"Yanni","family":"Tang","sequence":"additional","affiliation":[{"name":"Heyuan Polytechnic College, China"}]},{"given":"Chuanyi","family":"Chen","sequence":"additional","affiliation":[{"name":"City University of Macau, China"}]}],"member":"320","published-online":{"date-parts":[[2022,3,14]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"58","volume":"202","author":"Salihoun M.","unstructured":"M. Salihoun , State of Art of Data Mining and Learning Analytics Tools in Higher Education. International Journal of Emerging Technologies in Learning , 202 0. 15: p. 58 - 76 . M. Salihoun, State of Art of Data Mining and Learning Analytics Tools in Higher Education. International Journal of Emerging Technologies in Learning, 2020. 15: p. 58-76.","journal-title":"Learning"},{"key":"e_1_3_2_1_2_1","first-page":"4","volume":"201","author":"Zhu Y. Q.","unstructured":"Y. Q. Zhu , A Data Driven Educational Decision Support System . International Journal of Emerging Technologies in Learning , 201 8. 13: p. 4 - 16 . Y. Q. Zhu, A Data Driven Educational Decision Support System. International Journal of Emerging Technologies in Learning, 2018. 13: p. 4-16.","journal-title":"Learning"},{"key":"e_1_3_2_1_3_1","first-page":"3026","volume":"201","author":"Zhou Q.","unstructured":"Q. Zhou , C. Mou , and D. Yang , Research Progress on Educational Data Mining: A Survey . Journal of Software , 201 5. 26(11): p. 3026 - 3042 . Q. Zhou, C. Mou, and D. Yang, Research Progress on Educational Data Mining: A Survey. Journal of Software, 2015. 26(11): p. 3026-3042.","journal-title":"Software"},{"key":"e_1_3_2_1_4_1","volume-title":"A Survey of Educational Data Mining and Learning Analysis","author":"Yang G. M.","year":"2020","unstructured":"G. M. Yang , , A Survey of Educational Data Mining and Learning Analysis . Journal of Mudanjiang Normal University , 2020 (03): p. 57-61. G. M. Yang, , A Survey of Educational Data Mining and Learning Analysis. Journal of Mudanjiang Normal University, 2020(03): p. 57-61."},{"key":"e_1_3_2_1_5_1","first-page":"2494","volume":"202","author":"Li G.","unstructured":"G. Li , X. M. Hu , and Q. W. Hu , Data analysis and prospects of the national college students\u2019 life science competition. Chin J Biotechnol , 202 0. 36(11): p. 2494 - 2500 . G. Li, X. M. Hu, and Q. W. Hu, Data analysis and prospects of the national college students\u2019 life science competition. Chin J Biotechnol, 2020. 36(11): p. 2494-2500.","journal-title":"Chin J Biotechnol"},{"key":"e_1_3_2_1_6_1","first-page":"113","volume":"202","author":"Yong X.","unstructured":"X. Yong , F. Jin , and C. G. Xiu , Analysis on the influencing factors of winning and losing in 2018-2019cba regular season \u2013 Based on multiple linear regression. Statistics and Management , 202 0. 35(07): p. 113 - 116 . X. Yong, F. Jin, and C. G. Xiu, Analysis on the influencing factors of winning and losing in 2018-2019cba regular season \u2013 Based on multiple linear regression. Statistics and Management, 2020. 35(07): p. 113-116.","journal-title":"Management"},{"key":"e_1_3_2_1_7_1","first-page":"399","volume":"201","author":"Gao X. D.","unstructured":"X. D. Gao and Y. X. Zhang , Prediction model of weld width during high-power disk laser welding of 304 austenitic stainless steel. International Journal of Precision Engineering and Manufacturing , 201 4. 15(3): p. 399 - 405 . X. D. Gao and Y. X. Zhang, Prediction model of weld width during high-power disk laser welding of 304 austenitic stainless steel. International Journal of Precision Engineering and Manufacturing, 2014. 15(3): p. 399-405.","journal-title":"Manufacturing"},{"key":"e_1_3_2_1_8_1","first-page":"102","volume":"201","author":"Yu F.","unstructured":"F. Yu and X. Z. Xu , A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Applied Energy , 201 4. 134: p. 102 \u2013 113 . F. Yu and X. Z. Xu, A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Applied Energy, 2014. 134: p. 102\u2013113.","journal-title":"Applied Energy"},{"key":"e_1_3_2_1_9_1","volume-title":"Journal of Manufacturing Processes","author":"Wang H. Y.","year":"2020","unstructured":"H. Y. Wang , Z. X. Zhang , and L. M. Liu , Prediction and fitting of weld morphology of Al alloy-CFRP welding-rivet hybrid bonding joint based on GA-BP neural network . Journal of Manufacturing Processes , 2020 . H. Y. Wang, Z. X. Zhang, and L. M. Liu, Prediction and fitting of weld morphology of Al alloy-CFRP welding-rivet hybrid bonding joint based on GA-BP neural network. Journal of Manufacturing Processes, 2020."},{"key":"e_1_3_2_1_10_1","first-page":"238","volume-title":"Water Sci Technol, 2018","author":"Khan U. T.","year":"2017","unstructured":"U. T. Khan , J. X. He , and C. Valeo , River flood prediction using fuzzy neural networks: an investigation on automated network architecture . Water Sci Technol, 2018 . 2017 (1): p. 238 - 247 . U. T. Khan, J. X. He, and C. Valeo, River flood prediction using fuzzy neural networks: an investigation on automated network architecture. Water Sci Technol, 2018. 2017(1): p. 238-247."}],"event":{"name":"AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture","acronym":"AIAM2021","location":"Manchester United Kingdom"},"container-title":["2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3495018.3495325","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3495018.3495325","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:45Z","timestamp":1750191525000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3495018.3495325"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,23]]},"references-count":10,"alternative-id":["10.1145\/3495018.3495325","10.1145\/3495018"],"URL":"https:\/\/doi.org\/10.1145\/3495018.3495325","relation":{},"subject":[],"published":{"date-parts":[[2021,10,23]]},"assertion":[{"value":"2022-03-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}