{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T21:04:39Z","timestamp":1765487079503,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031604867"},{"type":"electronic","value":"9783031604874"}],"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\/978-3-031-60487-4_8","type":"book-chapter","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:02:20Z","timestamp":1717203740000},"page":"94-105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction and Analysis of Mobile Phone Export Volume Based on SVR Model"],"prefix":"10.1007","author":[{"given":"Ruizhi","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibo","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,1]]},"reference":[{"issue":"16","key":"8_CR1","doi-asserted-by":"publisher","first-page":"3263","DOI":"10.3390\/rs13163263","volume":"13","author":"ZJ Liu","year":"2021","unstructured":"Liu, Z.J., et al.: Gradient boosting estimation of the leaf area index of apple orchards in UAV remote sensing. Remote Sens. 13(16), 3263 (2021)","journal-title":"Remote Sens."},{"issue":"6","key":"8_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.3390\/su15065437","volume":"15","author":"XD Zhu","year":"2023","unstructured":"Zhu, X.D., Liu, X.: Research on the evolution of global electronics trade network structure since the 21st century from the Chinese perspective. Sustainability 15(6), 19 (2023)","journal-title":"Sustainability"},{"key":"8_CR3","first-page":"2413","volume":"12","author":"PF Li","year":"2022","unstructured":"Li, P.F., Xu, J.G., Ai-Hamami, M.: Application of machine learning in stock selection. Appl. Math. Nonlinear Sci. 12, 2413\u20132424 (2022)","journal-title":"Appl. Math. Nonlinear Sci."},{"issue":"16","key":"8_CR4","doi-asserted-by":"publisher","first-page":"8109","DOI":"10.3390\/app12168109","volume":"12","author":"LX Shangguan","year":"2022","unstructured":"Shangguan, L.X., Yin, Y.F., Zhang, Q.T., Liu, Q., Xie, W., Dong, Z.J.: Icing time prediction model of pavement based on an improved SVR model with response surface approach. Appl. Sci. 12(16), 8109 (2022)","journal-title":"Appl. Sci."},{"issue":"4","key":"8_CR5","doi-asserted-by":"publisher","first-page":"2162","DOI":"10.1016\/j.eswa.2014.10.031","volume":"42","author":"J Patel","year":"2015","unstructured":"Patel, J., Shah, S., Thakkar, P., Kotecha, K.: Predicting stock market index using fusion of machine learning techniques. Expert Syst. Appl. 42(4), 2162\u20132172 (2015)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"8_CR6","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s10614-020-10032-2","volume":"57","author":"T Syriopoulos","year":"2021","unstructured":"Syriopoulos, T., Tsatsaronis, M., Karamanos, I.: Support vector machine algorithms: an application to ship price forecasting. Comput. Econ. 57(1), 55\u201387 (2021)","journal-title":"Comput. Econ."},{"key":"8_CR7","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1016\/j.physa.2019.01.026","volume":"521","author":"S Fu","year":"2019","unstructured":"Fu, S., Li, Y., Sun, S., Li, H.: Evolutionary support vector machine for RMB exchange rate forecasting. Physica A-Stat. Mech. Appl. 521, 692\u2013704 (2019)","journal-title":"Physica A-Stat. Mech. Appl."},{"issue":"A8","key":"8_CR8","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1205\/cherd.04246","volume":"83","author":"AA Levis","year":"2005","unstructured":"Levis, A.A., Papageorgiou, L.G.: Customer demand forecasting via support vector regression analysis. Chem. Eng. Res. Des. 83(A8), 1009\u20131018 (2005)","journal-title":"Chem. Eng. Res. Des."},{"issue":"12","key":"8_CR9","first-page":"8775","volume":"35","author":"C Dai","year":"2023","unstructured":"Dai, C.: A method of forecasting trade export volume based on back-propagation neural network. Neural Comput. Appl. 35(12), 8775\u20138784 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"4","key":"8_CR10","first-page":"762","volume":"30","author":"AN Gerasimov","year":"2022","unstructured":"Gerasimov, A.N., Gromov, E.L., Skripnichenko, Y.S., Grigoryeva, O.P., Skripnichenko, V.Y.: Models and forecasts of the export potential of the regional economic system. Regionologiya-Regionology Russian J. Reg. Stud. 30(4), 762\u2013782 (2022)","journal-title":"Regionologiya-Regionology Russian J. Reg. Stud."},{"issue":"2","key":"8_CR11","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1016\/j.ejor.2020.09.046","volume":"291","author":"F Eckert","year":"2021","unstructured":"Eckert, F., Hyndman, R.J., Panagiotelis, A.: Forecasting Swiss exports using Bayesian forecast reconciliation. Eur. J. Oper. Res. 291(2), 693\u2013710 (2021)","journal-title":"Eur. J. Oper. Res."},{"issue":"2","key":"8_CR12","doi-asserted-by":"publisher","first-page":"143","DOI":"10.35530\/IT.074.02.202265","volume":"74","author":"G Karabay","year":"2023","unstructured":"Karabay, G., Kilic, M.B., Saricoban, K., G\u00fcnaydin, G.K.: Forecasting of Turkey\u2019s apparel exports using artificial neural network autoregressive models. Industria Textila 74(2), 143\u2013153 (2023)","journal-title":"Industria Textila"},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"115199","DOI":"10.1016\/j.eswa.2021.115199","volume":"182","author":"ML Shen","year":"2021","unstructured":"Shen, M.L., Lee, C.F., Liu, H.H., Chang, P.Y., Yang, C.H.: Effective multinational trade forecasting using LSTM recurrent neural network. Expert Syst. Appl. 182, 115199 (2021)","journal-title":"Expert Syst. Appl."},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.techfore.2016.04.027","volume":"112","author":"LW Fan","year":"2016","unstructured":"Fan, L.W., Pan, S.J., Li, Z.M., Li, H.P.: An ICA-based support vector regression scheme for forecasting crude oil prices. Technol. Forecast. Soc. Change 112, 245\u2013253 (2016)","journal-title":"Technol. Forecast. Soc. Change"},{"key":"8_CR15","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.cie.2016.07.012","volume":"99","author":"RJ Kuo","year":"2016","unstructured":"Kuo, R.J., Li, P.S.: Taiwanese export trade forecasting using firefly algorithm based K-means algorithm and SVR with wavelet transform. Comput. Ind. Eng. 99, 153\u2013161 (2016)","journal-title":"Comput. Ind. Eng."},{"unstructured":"Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V.: Presented at the 10th Annual Conference on Neural Information Processing Systems (NIPS), Denver, Co (1996)","key":"8_CR16"},{"issue":"2","key":"8_CR17","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1039\/B918972F","volume":"135","author":"RG Brereton","year":"2010","unstructured":"Brereton, R.G., Lloyd, G.R.: Support Vector Machines for classification and regression. Analyst 135(2), 230\u2013267 (2010)","journal-title":"Analyst"},{"issue":"2","key":"8_CR18","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1080\/19475705.2016.1176604","volume":"8","author":"S Thomas","year":"2017","unstructured":"Thomas, S., Pillai, G.N., Pal, K.: Prediction of peak ground acceleration using \u03b5-SVR, \u03bd-SVR and Ls-SVR algorithm. Geomat. Nat. Hazards Risk 8(2), 177\u2013193 (2017)","journal-title":"Geomat. Nat. Hazards Risk"},{"issue":"1","key":"8_CR19","doi-asserted-by":"publisher","first-page":"256","DOI":"10.14453\/aabfj.v17i1.15","volume":"17","author":"N Latif","year":"2023","unstructured":"Latif, N., Selvam, J.D., Kapse, M., Sharma, V., Mahajan, V.: Comparative performance of LSTM and ARIMA for the short-term prediction of bitcoin prices. Australas. Account. Bus. Financ. J. 17(1), 256\u2013276 (2023)","journal-title":"Australas. Account. Bus. Financ. J."},{"issue":"6","key":"8_CR20","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3390\/sym15061262","volume":"15","author":"M Roozbeh","year":"2023","unstructured":"Roozbeh, M., Rouhi, A., Mohamed, N.A., Jahadi, F.: Generalized support vector regression and symmetry functional regression approaches to model the high-dimensional data. Symmetry 15(6), 21 (2023)","journal-title":"Symmetry"},{"doi-asserted-by":"crossref","unstructured":"Shi, T., Chen, S.G.: Robust twin support vector regression with smooth truncated H\u03b5 loss function. Neural Process. Lett. 45 (2023)","key":"8_CR21","DOI":"10.1007\/s11063-023-11198-0"},{"unstructured":"Hao, D., Kihyung, B., Meng-Ze, Z.: A study on the trade potential of electronic products based on trade gravity expansion model between China and Korea. J. Korea Contents Assoc. 22(7), 216\u2013226 (2022)","key":"8_CR22"}],"container-title":["Lecture Notes in Computer Science","Human-Centered Design, Operation and Evaluation of Mobile Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-60487-4_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:47:30Z","timestamp":1717206450000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-60487-4_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031604867","9783031604874"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-60487-4_8","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 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Washington DC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"29 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}