{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:16:18Z","timestamp":1743084978962,"version":"3.40.3"},"publisher-location":"Cham","reference-count":76,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030707125"},{"type":"electronic","value":"9783030707132"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-70713-2_50","type":"book-chapter","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T18:06:11Z","timestamp":1620237971000},"page":"536-553","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Open Data in Prediction Using Machine Learning: A Systematic Review"],"prefix":"10.1007","author":[{"given":"Norismiza","family":"Ismail","sequence":"first","affiliation":[]},{"given":"Umi Kalsom","family":"Yusof","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,6]]},"reference":[{"key":"50_CR1","unstructured":"Open Knowledge Foundation. what is open data? (2014). https:\/\/okfn.org\/opendata\/. Accessed 1 Apr 2019"},{"key":"50_CR2","unstructured":"Open data handbook. What is open data? (2012). https:\/\/opendatahandbook.org\/en\/what-is-open-data\/index.html. Accessed 1 Apr 2019"},{"key":"50_CR3","unstructured":"W3C(e-Gov). egovernment at w3c: improving access to government through better use of the web (2009). https:\/\/www.w3.org\/2007\/eGov\/. Accessed 1 Apr 2019"},{"key":"50_CR4","doi-asserted-by":"crossref","unstructured":"Obama, B.: Transparency and open government.\u00a0Memorandum for the heads of executive departments and agencies\u00a0(2009)","DOI":"10.1037\/e531572010-001"},{"key":"50_CR5","doi-asserted-by":"crossref","unstructured":"Foulonneau, M., Martin, S., Turki, S.: How open data are turned into services? In:\u00a0International Conference on Exploring Services Science, pp. 31\u201339. Springer, Cham (2014)","DOI":"10.1007\/978-3-319-04810-9_3"},{"key":"50_CR6","unstructured":"Office of Management and Budget\u2019s (OMB). Memorandum m-1 0\u201306, open government directive (2013). https:\/\/goo.gl\/LcxbZE. Accessed 1 Apr 2019"},{"key":"50_CR7","unstructured":"Directive 2013\/37\/EU of the European Parliament and of the Council. Amending directive 2003\/98\/ec on the re-use of public sector information known as the \u201cpsi directive\u201d (2013). https:\/\/ec.europa.eu\/justice\/data-protection\/article-29\/documentation\/opinion-recommendation\/files\/2013\/wp207en.pdf. Accessed 1 Apr 2019"},{"key":"50_CR8","unstructured":"Insights; Publications. What executives should know about open data (2014). https:\/\/www.mckinsey.com\/industries\/technology-media-and-telecommunications\/our-insights\/what-executives-should-know-about-open-data. Accessed 1 Apr 2019"},{"key":"50_CR9","unstructured":"MAMPU: Our open data policy (2017). https:\/\/data.gov.my. Accessed 13 Sept 2019"},{"key":"50_CR10","doi-asserted-by":"crossref","unstructured":"Lindman, J., Kinnari, T., Rossi, M.: Industrial open data: case studies of early open data entrepreneurs. In:\u00a02014 47th Hawaii International Conference on System Sciences, pp. 739\u2013748. IEEE (2014)","DOI":"10.1109\/HICSS.2014.99"},{"key":"50_CR11","unstructured":"Song, S.H., Kim, T.D.: A study on the open platform modeling for linked open data ecosystem in public sector. In:\u00a02013 15th International Conference on Advanced Communications Technology (ICACT),\u00a0pp. 730\u2013734. IEEE (2013)"},{"key":"50_CR12","doi-asserted-by":"crossref","unstructured":"Pantano, E., Priporas, C.V., Stylos, N.: \u2018You will like it!\u2019 using open data to predict tourists\u2019 response to a tourist attraction.\u00a0Tourism Manage.\u00a060, 430\u2013438 (2017)","DOI":"10.1016\/j.tourman.2016.12.020"},{"issue":"1","key":"50_CR13","doi-asserted-by":"publisher","first-page":"47","DOI":"10.2501\/IJA-30-1-047-075","volume":"30","author":"SC Chu","year":"2011","unstructured":"Chu, S.C., Kim, Y.: Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. Int. J. Advert. 30(1), 47\u201375 (2011)","journal-title":"Int. J. Advert."},{"key":"50_CR14","unstructured":"Diffley, S., Kearns, J., Bennett, W., Kawalek, P.: Consumer behaviour in social networking sites: implications for marketers.\u00a0Irish J. Manage. (2011)"},{"key":"50_CR15","unstructured":"Jai, T.M.C., Burns, L.D.: Attributes of apparel tablet catalogs: value proposition comparisons.\u00a0J. Fashion Mark. Manage. (2014)"},{"key":"50_CR16","doi-asserted-by":"publisher","unstructured":"Turban, E., King, D., Lee, J.K., Liang, T.P., Turban, D.C.: Social commerce: foundations, social marketing, and advertising. In\u00a0Electronic Commerce, pp. 309\u2013364. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-10091-3_7","DOI":"10.1007\/978-3-319-10091-3_7"},{"key":"50_CR17","unstructured":"Kitchenham, B., Charters, S.: Guidelines for performing systematic literature reviews in software engineering (2007)"},{"key":"50_CR18","doi-asserted-by":"crossref","unstructured":"Bizer, C., Heath, T., Berners-Lee, T.: Linked data: the story so far. In:\u00a0Semantic Services, Interoperability and Web Applications: Emerging Concepts, pp. 205\u2013227. IGI Global (2011)","DOI":"10.4018\/978-1-60960-593-3.ch008"},{"key":"50_CR19","doi-asserted-by":"crossref","unstructured":"Davis, A., Dieste, O., Hickey, A., Juristo, N., Moreno, A.M.: Effectiveness of requirements elicitation techniques: empirical results derived from a systematic review. In:\u00a014th IEEE International Requirements Engineering Conference (RE 2006), pp. 179\u2013188. IEEE (2006)","DOI":"10.1109\/RE.2006.17"},{"key":"50_CR20","doi-asserted-by":"crossref","unstructured":"Maglyas, A., Nikula, U., Smolander, K.: What do we know about software product management? -A systematic mapping study. In:\u00a02011 Fifth International Workshop on Software Product Management (IWSPM), pp. 26\u201335. IEEE (2011)","DOI":"10.1109\/IWSPM.2011.6046201"},{"key":"50_CR21","doi-asserted-by":"crossref","unstructured":"Budgen, D., Burn, A.J., Brereton, O.P., Kitchenham, B.A., Pretorius, R.: Empirical evidence about the UML: a systematic literature review.\u00a0Softw. Pract. Experience\u00a041(4), 363\u2013392 (2011)","DOI":"10.1002\/spe.1009"},{"issue":"3","key":"50_CR22","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1177\/1356389013497081","volume":"19","author":"RK Yin","year":"2013","unstructured":"Yin, R.K.: Validity and generalization in future case study evaluations. Evaluation 19(3), 321\u2013332 (2013)","journal-title":"Evaluation"},{"key":"50_CR23","doi-asserted-by":"publisher","first-page":"103383","DOI":"10.1016\/j.jbi.2020.103383","volume":"103","author":"F Sadoughi","year":"2020","unstructured":"Sadoughi, F., Behmanesh, A., Sayfouri, N.: Internet of things in medicine: a systematic mapping study. J. Biomed. Inform. 103, 103383 (2020)","journal-title":"J. Biomed. Inform."},{"issue":"3","key":"50_CR24","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1016\/j.joi.2017.06.005","volume":"11","author":"G Halevi","year":"2017","unstructured":"Halevi, G., Moed, H., Bar-Ilan, J.: Suitability of Google Scholar as a source of scientific information and as a source of data for scientific evaluation\u2014review of the literature. J. Informetrics 11(3), 823\u2013834 (2017)","journal-title":"J. Informetrics"},{"issue":"3\u20134","key":"50_CR25","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1300\/J122v24n03_09","volume":"24","author":"C Madarash-Hill","year":"2004","unstructured":"Madarash-Hill, C., Hill, J.B.: Enhancing access to IEEE conference proceedings: a case study in the application of IEEE Xplore full text and table of contents enhancements. Sci. Technol. Libr. 24(3\u20134), 389\u2013399 (2004)","journal-title":"Sci. Technol. Libr."},{"key":"50_CR26","unstructured":"Zelevinsky, V., Wang, J., Tunkelang, D.: Supporting exploratory search for the ACM digital library. In:\u00a0Workshop on Human-Computer Interaction and Information Retrieval (HCIR 2008), pp. 85\u201388 (2008)"},{"key":"50_CR27","doi-asserted-by":"crossref","unstructured":"Boyle, F., Sherman, D.: Scopus\u2122: The product and its development.\u00a0Serials Librarian\u00a049(3), 147\u2013153 (2006)","DOI":"10.1300\/J123v49n03_12"},{"key":"50_CR28","doi-asserted-by":"crossref","unstructured":"Lindman, J., Rossi, M., Tuunainen, V.K.: Open data services: Research agenda. In:\u00a02013 46th Hawaii International Conference on System Sciences, pp. 1239\u20131246. IEEE (2013)","DOI":"10.1109\/HICSS.2013.430"},{"key":"50_CR29","doi-asserted-by":"crossref","unstructured":"Derguech, W., Bruke, E., Curry, E.: An autonomic approach to real-time predictive analytics using open data and internet of things. In:\u00a02014 IEEE 11th International Conference on Ubiquitous Intelligence and Computing and 2014 IEEE 11th International Conference on Autonomic and Trusted Computing and 2014 IEEE 14th International Conference on Scalable Computing and Communications and Its Associated Workshops, pp. 204\u2013211. IEEE (2014)","DOI":"10.1109\/UIC-ATC-ScalCom.2014.137"},{"issue":"1","key":"50_CR30","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s41239-020-0177-7","volume":"17","author":"E Alyahyan","year":"2020","unstructured":"Alyahyan, E., D\u00fc\u015fteg\u00f6r, D.: Predicting academic success in higher education: literature review and best practices. Int. J. Educ. Technol. High. Educ. 17(1), 3 (2020)","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"50_CR31","unstructured":"Casta\u00f1\u00f3n, J.: (10). Machine learning methods that every data scientist should know.\u00a0Consultado em Outubro\u00a016 (2019)"},{"key":"50_CR32","doi-asserted-by":"crossref","unstructured":"Kononenko, I., Kukar, M.: Machine learning basics. Mach. Learn. Data Min. 59\u2013105 (2007)","DOI":"10.1533\/9780857099440.59"},{"issue":"1","key":"50_CR33","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s41239-019-0171-0","volume":"16","author":"O Zawacki-Richter","year":"2019","unstructured":"Zawacki-Richter, O., Mar\u00edn, V.I., Bond, M., Gouverneur, F.: Systematic review of research on artificial intelligence applications in higher education\u2013where are the educators? Int. J. Educ. Technol. High. Educ. 16(1), 39 (2019)","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"50_CR34","unstructured":"Schultz, M., Shatter, A.: Directive 2013\/37\/EU of the European Parliament and of the council of 26 June 2013 amending directive 2003\/98\/EC on the re-use of public sector information.\u00a0Official J. Eur. Union Brussels (2013)"},{"key":"50_CR35","unstructured":"Obama, B.: Executive order--making open and machine readable the new default for government information.\u00a0The White House (2013)"},{"key":"50_CR36","doi-asserted-by":"crossref","unstructured":"Weerakkody, V., Sivarajah, U., Mahroof, K., Maruyama, T., Lu, S.: Influencing subjective well-being for business and sustainable development using big data and predictive regression analysis.\u00a0J. Bus. Res. (2020)","DOI":"10.1016\/j.jbusres.2020.07.038"},{"key":"50_CR37","unstructured":"Hunnius, S., Krieger, B., Schuppan, T.: Providing, guarding, shielding: open government data in Spain and Germany. In:\u00a0European Group for Public Administration Annual Conference, Speyer, Germany (2014)"},{"key":"50_CR38","doi-asserted-by":"crossref","unstructured":"Wright, F.: Data Gov. pp. 77\u201382 (2014)","DOI":"10.1080\/08963568.2014.855090"},{"key":"50_CR39","doi-asserted-by":"crossref","unstructured":"Nugroho, R.P., Zuiderwijk, A., Janssen, M., de Jong, M.: A comparison of national open data policies: lessons learned.\u00a0Transforming Government: People, Process and Policy (2015)","DOI":"10.1108\/TG-03-2014-0008"},{"key":"50_CR40","doi-asserted-by":"crossref","unstructured":"Xue, J.: Financial risk prediction and evaluation model of P2P network loan platform. In:\u00a02020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), pp. 1060\u20131064. IEEE (2020)","DOI":"10.1109\/ICMTMA50254.2020.00227"},{"key":"50_CR41","doi-asserted-by":"crossref","unstructured":"Alloghani, M., Aljaaf, A.J., Al-Jumeily, D., Hussain, A., Mallucci, C., Mustafina, J.: Data science to improve patient management system. In:\u00a02018 11th International Conference on Developments in eSystems Engineering (DeSE), pp. 27\u201330. IEEE (2018)","DOI":"10.1109\/DeSE.2018.00012"},{"key":"50_CR42","doi-asserted-by":"crossref","unstructured":"Sarker, F., Tiropanis, T., Davis, H.C.: Linked data, data mining and external open data for better prediction of at-risk students. In:\u00a02014 International Conference on Control, Decision and Information Technologies (CoDIT), pp. 652\u2013657. IEEE (2014)","DOI":"10.1109\/CoDIT.2014.6996973"},{"key":"50_CR43","doi-asserted-by":"crossref","unstructured":"Capari\u00f1o, E.T., Sison, A.M., Medina, R.P.: Application of the modified imputation method to missing data to increase classification performance. In:\u00a02019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), pp. 134\u2013139. IEEE (2019)","DOI":"10.1109\/CCOMS.2019.8821632"},{"key":"50_CR44","doi-asserted-by":"crossref","unstructured":"Rao, A.R., Clarke, D.: A comparison of models to predict medical procedure costs from open public healthcare data. In:\u00a02018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489257"},{"issue":"2","key":"50_CR45","doi-asserted-by":"publisher","first-page":"102147","DOI":"10.1016\/j.ipm.2019.102147","volume":"57","author":"J Tuke","year":"2020","unstructured":"Tuke, J., Nguyen, A., Nasim, M., Mellor, D., Wickramasinghe, A., Bean, N., Mitchell, L.: Pachinko prediction: a Bayesian method for event prediction from social media data. Inf. Process. Manage. 57(2), 102147 (2020)","journal-title":"Inf. Process. Manage."},{"key":"50_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Siriarya, P., Kawai, Y., Jatowt, A.: Automatic latent street type discovery from web open data.\u00a0Inf. Syst. 101536 (2020)","DOI":"10.1016\/j.is.2020.101536"},{"issue":"4","key":"50_CR47","doi-asserted-by":"publisher","first-page":"956","DOI":"10.3390\/molecules23040956","volume":"23","author":"O Tarasova","year":"2018","unstructured":"Tarasova, O., Poroikov, V.: HIV resistance prediction to reverse transcriptase inhibitors: focus on open data. Molecules 23(4), 956 (2018)","journal-title":"Molecules"},{"key":"50_CR48","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.procs.2017.11.187","volume":"119","author":"J Noymanee","year":"2017","unstructured":"Noymanee, J., Nikitin, N.O., Kalyuzhnaya, A.V.: Urban pluvial flood forecasting using open data with machine learning techniques in pattani basin. Procedia Comput. Sci. 119, 288\u2013297 (2017)","journal-title":"Procedia Comput. Sci."},{"key":"50_CR49","doi-asserted-by":"crossref","unstructured":"Rocca, G.B., Castillo-Cara, M., Levano, R.A., Herrera, J.V., Orozco-Barbosa, L.: Citizen security using machine learning algorithms through open data. In:\u00a02016 8th IEEE Latin-American Conference on Communications (LATINCOM), pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/LATINCOM.2016.7811562"},{"key":"50_CR50","doi-asserted-by":"crossref","unstructured":"Dias, G.M., Bellalta, B., Oechsner, S.: Predicting occupancy trends in Barcelona\u2019s bicycle service stations using open data. In:\u00a02015 SAI Intelligent Systems Conference (IntelliSys), pp. 439\u2013445. IEEE (2015)","DOI":"10.1109\/IntelliSys.2015.7361177"},{"issue":"3","key":"50_CR51","doi-asserted-by":"publisher","first-page":"422","DOI":"10.3390\/molecules22030422","volume":"22","author":"F Montanari","year":"2017","unstructured":"Montanari, F., Zdrazil, B.: How open data shapes in silico transporter modeling. Molecules 22(3), 422 (2017)","journal-title":"Molecules"},{"key":"50_CR52","unstructured":"Chen, Y.Y., Lv, Y., Li, Z., Wang, F.Y.: Long short-term memory model for traffic congestion prediction with online open data. In:\u00a02016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 132\u2013137. IEEE (2016)"},{"key":"50_CR53","doi-asserted-by":"crossref","unstructured":"Asat, A.N., Mahat, A.F., Hassan, R., Ahmed, A.S.: Development of dengue detection and prevention system (Deng-E) based upon open data in Malaysia. In:\u00a02017 6th International Conference on Electrical Engineering and Informatics (ICEEI), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/ICEEI.2017.8312426"},{"key":"50_CR54","doi-asserted-by":"crossref","unstructured":"Nechaev, Y., Corcoglioniti, F., Giuliano, C.: Type prediction combining linked open data and social media. In:\u00a0Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1033\u20131042 (2018)","DOI":"10.1145\/3269206.3271781"},{"key":"50_CR55","doi-asserted-by":"crossref","unstructured":"Li, R., Xiong, H., Zhao, H.: More than address: pre-identify your income with the open data. In\u00a02015 International Conference on Cloud Computing and Big Data (CCBD), pp. 193\u2013200. IEEE (2015)","DOI":"10.1109\/CCBD.2015.51"},{"issue":"10","key":"50_CR56","first-page":"1192","volume":"71","author":"C Qiao","year":"2020","unstructured":"Qiao, C., Hu, X.: A joint neural network model for combining heterogeneous user data sources: an example of at-risk student prediction. J. Am. Soc. Inf. Sci. 71(10), 1192\u20131204 (2020)","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"50_CR57","doi-asserted-by":"crossref","unstructured":"Gutierrez-Osorio, C., Pedraza, C.: Modern data sources and techniques for analysis and forecast of road accidents: a review.\u00a0J. Traffic Transp. Eng. (English edition) (2020)","DOI":"10.1016\/j.jtte.2020.05.002"},{"issue":"3","key":"50_CR58","doi-asserted-by":"publisher","first-page":"145","DOI":"10.3233\/HIS-150212","volume":"12","author":"M Panda","year":"2015","unstructured":"Panda, M.: Learning crisis management information system from open crisis data using hybrid soft computing. Int. J. Hybrid Intell. Syst. 12(3), 145\u2013156 (2015)","journal-title":"Int. J. Hybrid Intell. Syst."},{"key":"50_CR59","doi-asserted-by":"crossref","unstructured":"Chen, S., Wang, Q., Liu, S.: Credit risk prediction in peer-to-peer lending with ensemble learning framework. In:\u00a02019 Chinese Control and Decision Conference (CCDC), pp. 4373\u20134377. IEEE (2019)","DOI":"10.1109\/CCDC.2019.8832412"},{"key":"50_CR60","doi-asserted-by":"crossref","unstructured":"Chen, H., Hu, Q., He, L.: Clairvoyant: an early prediction system for video hits. In:\u00a0Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 2054\u20132056 (2014)","DOI":"10.1145\/2661829.2661847"},{"key":"50_CR61","doi-asserted-by":"crossref","unstructured":"Pohjankukka, J., Riihim\u00e4ki, H., Nevalainen, P., Pahikkala, T., Ala-Ilom\u00e4ki, J., Hyv\u00f6nen, E., Heikkonen, J.: Predictability of boreal forest soil bearing capacity by machine learning.\u00a0J. Terramech. 68, 1\u20138 (2016)","DOI":"10.1016\/j.jterra.2016.09.001"},{"key":"50_CR62","doi-asserted-by":"crossref","unstructured":"Lubis, F.F., Rosmansyah, Y., Supangkat, S.H.: Gradient descent and normal equations on cost function minimization for online predictive using linear regression with multiple variables. In:\u00a02014 International Conference on ICT for Smart Society (ICISS), pp. 202\u2013205. IEEE (2014)","DOI":"10.1109\/ICTSS.2014.7013173"},{"key":"50_CR63","doi-asserted-by":"crossref","unstructured":"Lin, B.H., Tseng, S.F.: A predictive analysis of citizen hotlines 1999 and traffic accidents: a case study of Taoyuan city. In:\u00a02017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 374\u2013376. IEEE (2017)","DOI":"10.1109\/BIGCOMP.2017.7881696"},{"key":"50_CR64","doi-asserted-by":"crossref","unstructured":"Wu, C.H., Kao, S.C., Kan, M.H.: Knowledge discovery in open data of dengue epidemic. In:\u00a0Proceedings of the 4th Multidisciplinary International Social Networks Conference, pp. 1\u20138 (2017)","DOI":"10.1145\/3092090.3092093"},{"key":"50_CR65","doi-asserted-by":"crossref","unstructured":"Grzegorowski, M.: Massively parallel feature extraction framework application in predicting dangerous seismic events. In:\u00a02016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 225\u2013229. IEEE (2016)","DOI":"10.15439\/2016F90"},{"key":"50_CR66","unstructured":"Sarker, F., Tiropanis, T., Davis, H.C.: Students\u2019 performance prediction by using institutional internal and external open data sources (2013)"},{"key":"50_CR67","doi-asserted-by":"crossref","unstructured":"Prabakar, A., Wu, L., Zwanepol, L., Van Velzen, N., Djairam, D.: Applying machine learning to study the relationship between electricity consumption and weather variables using open data. In:\u00a02018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/ISGTEurope.2018.8571430"},{"key":"50_CR68","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.earscirev.2019.04.022","volume":"194","author":"EB Goldstein","year":"2019","unstructured":"Goldstein, E.B., Coco, G., Plant, N.G.: A review of machine learning applications to coastal sediment transport and morphodynamics. Earth Sci. Rev. 194, 97\u2013108 (2019)","journal-title":"Earth Sci. Rev."},{"key":"50_CR69","doi-asserted-by":"crossref","unstructured":"Lee, J., Park, G.L.: Temporal data stream analysis for EV charging infrastructure in Jeju. In:\u00a0Proceedings of the International Conference on Research in Adaptive and Convergent Systems, pp. 36\u201339 (2017)","DOI":"10.1145\/3129676.3129717"},{"key":"50_CR70","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.rser.2019.04.073","volume":"110","author":"FR Cecconi","year":"2019","unstructured":"Cecconi, F.R., Moretti, N., Tagliabue, L.C.: Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings. Renew. Sustain. Energy Rev. 110, 266\u2013277 (2019)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"50_CR71","doi-asserted-by":"crossref","unstructured":"Petrlik, J., Sekanina, L.: Towards robust and accurate traffic prediction using parallel multiobjective genetic algorithms and support vector regression. In:\u00a02015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 2231\u20132236. IEEE (2015)","DOI":"10.1109\/ITSC.2015.360"},{"key":"50_CR72","doi-asserted-by":"crossref","unstructured":"Shen, S.K., Liu, W., Zhang, T.: Load pattern recognition and prediction based on DTW K-mediods clustering and Markov model. In:\u00a02019 IEEE International Conference on Energy Internet (ICEI), pp. 403\u2013408. IEEE (2019)","DOI":"10.1109\/ICEI.2019.00077"},{"key":"50_CR73","doi-asserted-by":"crossref","unstructured":"Shan, S., Cao, B.: Forecasting the degree of crowding in urban public open space upon multi-source data. In:\u00a02016 9th International Symposium on Computational Intelligence and Design (ISCID), vol. 2, pp. 69\u201374. IEEE (2016)","DOI":"10.1109\/ISCID.2016.2025"},{"key":"50_CR74","doi-asserted-by":"crossref","unstructured":"Violos, J., Pelekis, S., Berdelis, A., Tsanakas, S., Tserpes, K., Varvarigou, T.: Predicting visitor distribution for large events in smart cities. In:\u00a02019 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/BIGCOMP.2019.8679181"},{"key":"50_CR75","unstructured":"Goel, M., Sharma, N., Gurve, M.K.: Analysis of global terrorism dataset using open source data mining tools. In:\u00a02019 International Conference on Computing, Power and Communication Technologies (GUCON), pp. 165\u2013170. IEEE (2019)"},{"key":"50_CR76","doi-asserted-by":"crossref","unstructured":"Pradhan, I., Potika, K., Eirinaki, M., Potikas, P.: Exploratory data analysis and crime prediction for smart cities. In:\u00a0Proceedings of the 23rd International Database Applications and Engineering Symposium, pp. 1\u20139 (2019)","DOI":"10.1145\/3331076.3331114"}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Innovative Systems for Intelligent Health Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-70713-2_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T18:22:03Z","timestamp":1620238923000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-70713-2_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030707125","9783030707132"],"references-count":76,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-70713-2_50","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"type":"print","value":"2367-4512"},{"type":"electronic","value":"2367-4520"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IRICT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of Reliable Information and Communication Technology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Langkawi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaysia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"irict2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/irict.co\/irict2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}