{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:18:19Z","timestamp":1743031099618,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030693763"},{"type":"electronic","value":"9783030693770"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69377-0_16","type":"book-chapter","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T04:59:40Z","timestamp":1612933180000},"page":"192-203","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Modeling Daily Crime Events Prediction Using Seq2Seq Architecture"],"prefix":"10.1007","author":[{"given":"Jawaher","family":"Alghamdi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-4949","authenticated-orcid":false,"given":"Zi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,10]]},"reference":[{"key":"16_CR1","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., Pentland, A.: Once upon a crime: towards crime prediction from demographics and mobile data. In: Proceedings of the 16th International Conference on Multimodal Interaction, pp. 427\u2013434 (2014)","DOI":"10.1145\/2663204.2663254"},{"key":"16_CR3","unstructured":"Brownlee, J.: Deep learning with time series forecasting. Machine Learning Mastery (2018)"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Cesario, E., Catlett, C., Talia, D.: Forecasting crimes using autoregressive models. In: 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, 14th International Conference on Pervasive Intelligence and Computing, 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech), pp. 795\u2013802. IEEE (2016)","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2016.138"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Chen, P., Yuan, H., Shu, X.: Forecasting crime using the ARIMA model. In: 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, vol. 5, pp. 627\u2013630. IEEE (2008)","DOI":"10.1109\/FSKD.2008.222"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"16_CR7","unstructured":"Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint arXiv:1511.07289 (2015)"},{"key":"16_CR8","unstructured":"El Hihi, S., Bengio, Y.: Hierarchical recurrent neural networks for long-term dependencies. In: Advances in Neural Information Processing Systems, pp. 493\u2013499 (1996)"},{"key":"16_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/978-3-030-00563-4_59","volume-title":"Advances in Brain Inspired Cognitive Systems","author":"M Feng","year":"2018","unstructured":"Feng, M., Zheng, J., Han, Y., Ren, J., Liu, Q.: Big data analytics and mining for crime data analysis, visualization and prediction. In: Ren, J., et al. (eds.) BICS 2018. LNCS (LNAI), vol. 10989, pp. 605\u2013614. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00563-4_59"},{"issue":"8","key":"16_CR10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"16_CR11","unstructured":"Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: International Conference on Machine Learning, pp. 2342\u20132350 (2015)"},{"key":"16_CR12","unstructured":"Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)"},{"key":"16_CR13","unstructured":"Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning, pp. 1310\u20131318 (2013)"},{"issue":"9","key":"16_CR14","first-page":"415","volume":"9","author":"AK Shrivastav","year":"2012","unstructured":"Shrivastav, A.K., Ekata, D.: Applicability of soft computing technique for crime forecasting: a preliminary investigation. Int. J. Comput. Sci. Eng. Technol. 9(9), 415\u2013421 (2012)","journal-title":"Int. J. Comput. Sci. Eng. Technol."},{"key":"16_CR15","unstructured":"Stec, A., Klabjan, D.: Forecasting crime with deep learning. arXiv preprint arXiv:1806.01486 (2018)"},{"key":"16_CR16","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104\u20133112 (2014)"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Yu, C.H., Ward, M.W., Morabito, M., Ding, W.: Crime forecasting using data mining techniques. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp. 779\u2013786. IEEE (2011)","DOI":"10.1109\/ICDMW.2011.56"},{"issue":"2","key":"16_CR18","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.ejor.2003.08.037","volume":"160","author":"GP Zhang","year":"2005","unstructured":"Zhang, G.P., Qi, M.: Neural network forecasting for seasonal and trend time series. Eur. J. Oper. Res. 160(2), 501\u2013514 (2005)","journal-title":"Eur. J. Oper. Res."},{"key":"16_CR19","unstructured":"Zhang, G.: Linear and nonlinear time series forecasting with artificial neural networks. Kent State University (1998)"},{"issue":"2","key":"16_CR20","first-page":"49","volume":"17","author":"L Zhao","year":"2019","unstructured":"Zhao, L., Cheng, B., Chen, J.: A hybrid time series model based on dilated Conv1D and LSTM with applications to PM2. 5 forecasting. Aust. J. Intell. Inf. Process. Syst. 17(2), 49\u201360 (2019)","journal-title":"Aust. J. Intell. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Databases Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69377-0_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T05:06:51Z","timestamp":1612933611000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69377-0_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030693763","9783030693770"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69377-0_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Database Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dunedin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 February 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adc2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adc2021.github.io\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"76% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}