{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:58:50Z","timestamp":1743051530256,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":16,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819756650"},{"type":"electronic","value":"9789819756667"}],"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\/978-981-97-5666-7_10","type":"book-chapter","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T20:37:45Z","timestamp":1722544665000},"page":"113-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CPMA: Spatio-Temporal Network Prediction Model Based on Convolutional Parallel Multi-head Self-attention"],"prefix":"10.1007","author":[{"given":"Tiantian","family":"Liu","sequence":"first","affiliation":[]},{"given":"Xin","family":"You","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.ijrefrig.2023.06.010","volume":"153","author":"X Shichao","year":"2023","unstructured":"Shichao, X., Min, S., Lianqing, Y.: Energy saving analysis of refrigeration room group control based on kernel ridge regression algorithm. Int. J. Refrig. 153, 345\u2013355 (2023)","journal-title":"Int. J. Refrig."},{"key":"10_CR2","unstructured":"Enzo, Leiva-Aravena, Eduardo, et al.: Neural architecture search with reinforcement learning. Science of the Total Environment (2019)"},{"key":"10_CR3","unstructured":"Chen, Z., Yang, R., Cao, B., et al.: SmarNet: teaching machines to read and comprehend like human (2017)"},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"55129","DOI":"10.1007\/s11356-021-14687-8","volume":"28","author":"Y Yurong","year":"2021","unstructured":"Yurong, Y., Qingyu, X., Chao, W., et al.: A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism. Environ. Sci. Pollut. Res. Int. 28, 55129\u201355139 (2021)","journal-title":"Environ. Sci. Pollut. Res. Int."},{"key":"10_CR5","doi-asserted-by":"publisher","first-page":"4007","DOI":"10.3390\/su11154007","volume":"11","author":"H Ali","year":"2019","unstructured":"Ali, H., Choi, J.-H.: A review of underground pipeline leakage and sinkhole monitoring methods based on wireless sensor networking. Sustainability 11, 4007 (2019)","journal-title":"Sustainability"},{"issue":"11","key":"10_CR6","doi-asserted-by":"publisher","first-page":"2279","DOI":"10.3390\/w11112279","volume":"11","author":"D Manzi","year":"2019","unstructured":"Manzi, D., et al.: Pattern recognition and clustering of transient pressure signals for burst location. Water 11(11), 2279 (2019)","journal-title":"Water"},{"key":"10_CR7","doi-asserted-by":"publisher","first-page":"110094","DOI":"10.1016\/j.measurement.2021.110094","volume":"186","author":"W Wenming","year":"2021","unstructured":"Wenming, W., Haibo, S., Jianqiang, G., et al.: Experimental study on water pipeline leak using In-Pipe acoustic signal analysis and artificial neural network prediction. Measurement 186, 110094 (2021)","journal-title":"Measurement"},{"issue":"22","key":"10_CR8","doi-asserted-by":"publisher","first-page":"7661","DOI":"10.3390\/en16227661","volume":"16","author":"NZ Tian","year":"2023","unstructured":"Tian, N.Z., Gao, X.X., Xia, T., et al.: Evaluation of landweber coupled least square support vector regression algorithm for electrical capacitance tomography for LN 2 \u2013VN 2Flow. Energies 16(22), 7661 (2023)","journal-title":"Energies"},{"key":"10_CR9","doi-asserted-by":"publisher","first-page":"138226","DOI":"10.1016\/j.foodchem.2023.138226","volume":"440","author":"Q Xu","year":"2024","unstructured":"Xu, Q., Mengqi, L., Yanfeng, S., et al.: Decoding the aroma characteristics of ice wine by partial least-squares regression, aroma reconstitution, and omission studies. Food Chem. 440, 138226 (2024)","journal-title":"Food Chem."},{"key":"10_CR10","doi-asserted-by":"publisher","first-page":"109922","DOI":"10.1016\/j.ress.2023.109922","volume":"244","author":"Y Shui","year":"2024","unstructured":"Shui, Y., Yuyao, R., Xiao, W., et al.: Dynamic pruning-based Bayesian support vector regression for reliability analysis. Reliab. Eng. Syst. Safety 244, 109922 (2024)","journal-title":"Reliab. Eng. Syst. Safety"},{"key":"10_CR11","doi-asserted-by":"publisher","first-page":"115945","DOI":"10.1016\/j.marpolbul.2023.115945","volume":"199","author":"M Bijoy","year":"2024","unstructured":"Bijoy, M., Prakash, S.T., Sakib, M.U., et al.: Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: a case study in Bay of Bengal. Mar. Pollut. Bull. 199, 115945 (2024)","journal-title":"Mar. Pollut. Bull."},{"key":"10_CR12","doi-asserted-by":"publisher","first-page":"5885","DOI":"10.1007\/s12652-022-03818-9","volume":"13","author":"EB Priyanka","year":"2022","unstructured":"Priyanka, E.B., Thangavel, S.: Multi-type feature extraction and classification of leakage in oil pipeline network using digital twin technology. J. Ambient Intell. Hum. Comput. 13, 5885\u20135901 (2022)","journal-title":"J. Ambient Intell. Hum. Comput."},{"issue":"13","key":"10_CR13","doi-asserted-by":"publisher","first-page":"6467","DOI":"10.3390\/app12136467","volume":"12","author":"SS Lee","year":"2022","unstructured":"Lee, S.S., Lee, H.-H., Lee, Y.-J.: Prediction of minimum night flow for enhancing leakage detection capabilities in water distribution networks. Appl. Sci. 12(13), 6467 (2022)","journal-title":"Appl. Sci."},{"key":"10_CR14","doi-asserted-by":"publisher","first-page":"105964","DOI":"10.1016\/j.engappai.2023.105964","volume":"120","author":"J Kim","year":"2023","unstructured":"Kim, J., Kang, H., Kang, P.: Time-series anomaly detection with stacked Transformer representations and 1D convolutional network. Eng. Appl. Artif. Intell. 120, 105964 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10_CR15","first-page":"3258","volume":"38","author":"B SangSoo","year":"2020","unstructured":"SangSoo, B., Jongcheol, P., Ahn, J.C.: Prediction of water level and water quality using a CNN-LSTM combined deep learning approach. Water 38, 3258\u20133260 (2020)","journal-title":"Water"},{"key":"10_CR16","doi-asserted-by":"publisher","first-page":"75664","DOI":"10.1007\/s11356-022-21115-y","volume":"29","author":"Z Qiang","year":"2022","unstructured":"Qiang, Z., Ruiqi, W., Ying, Q., et al.: A watershed water quality prediction model based on attention mechanism and Bi-LSTM. Environ. Sci. Pollut. Res. Int. 29, 75664\u201375680 (2022)","journal-title":"Environ. Sci. Pollut. Res. Int."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5666-7_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T20:41:02Z","timestamp":1722544862000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5666-7_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819756650","9789819756667"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5666-7_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}