{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:29:52Z","timestamp":1765268992958,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":12,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819759330"},{"type":"electronic","value":"9789819759347"}],"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\/978-981-97-5934-7_12","type":"book-chapter","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T08:02:36Z","timestamp":1723449756000},"page":"133-146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LSTM Based Time Series Forecasting of Noisy Signals"],"prefix":"10.1007","author":[{"given":"Beza Negash","family":"Getu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.procir.2021.03.088","volume":"99","author":"B Lindemann","year":"2021","unstructured":"Lindemann, B., M\u00fcller, T., Vietz, H., Jazdi, N., Weyrich, M.: A survey on long short-term memory networks for time series prediction. Procedia CIRP 99, 650\u2013655 (2021). https:\/\/doi.org\/10.1016\/j.procir.2021.03.088","journal-title":"Procedia CIRP"},{"issue":"1","key":"12_CR2","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.csl.2014.09.005","volume":"30","author":"W De Mulder","year":"2015","unstructured":"De Mulder, W., Bethard, S.: Marie-Francine Moens; A survey on the application of recurrent neural networks to statistical language modeling. Comput. Speech Lang. 30(1), 61\u201398 (2015). https:\/\/doi.org\/10.1016\/j.csl.2014.09.005","journal-title":"Comput. Speech Lang."},{"key":"12_CR3","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.jmsy.2018.05.011","volume":"48","author":"J Zhanga","year":"2018","unstructured":"Zhanga, J., Wanga, P., Yanb, R., Gaoa, R.X.: Long short-term memory for machine remaining life prediction. J. Manuf. Syst. 48, 78\u201386 (2018). https:\/\/doi.org\/10.1016\/j.jmsy.2018.05.011","journal-title":"J. Manuf. Syst."},{"key":"12_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.iswa.2021.200049","volume":"10","author":"SRB Shah","year":"2021","unstructured":"Shah, S.R.B., Chadha, G.S., Schwung, A., Ding, S.X.: A sequence-to-sequence approach for remaining useful lifetime estimation using attention-augmented bidirectional LSTM. Intell. Syst. Appl. 10, 1\u201318 (2021). https:\/\/doi.org\/10.1016\/j.iswa.2021.200049","journal-title":"Intell. Syst. Appl."},{"key":"12_CR5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.promfg.2020.06.015","volume":"49","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Zhao, Y., Addepalli, S.: Remaining useful life prediction using deep learning approaches: a review. Procedia Manufact. 49, 81\u201388 (2020). https:\/\/doi.org\/10.1016\/j.promfg.2020.06.015","journal-title":"Procedia Manufact."},{"key":"12_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1088\/1742-6596\/1863\/1\/012016","volume":"1863","author":"RAA Viadinugroho","year":"2021","unstructured":"Viadinugroho, R.A.A., Rosadi, D.: Long short-term memory neural network model for time series forecasting: case study of forecasting IHSG during covid-19 outbreak. J. Phys. Conf. Ser. 1863, 1\u201311 (2021). https:\/\/doi.org\/10.1088\/1742-6596\/1863\/1\/012016","journal-title":"J. Phys. Conf. Ser."},{"issue":"03","key":"12_CR7","first-page":"3342","volume":"05","author":"R Nandakumar","year":"2018","unstructured":"Nandakumar, R., Uttamraj, K.R., Vishal, R., Lokeswari, Y.V.: Stock price prediction using long short term memory. Int. Res. J. Eng. Technol. (IRJET) 05(03), 3342\u20133348 (2018)","journal-title":"Int. Res. J. Eng. Technol. (IRJET)"},{"key":"12_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1088\/1755-1315\/299\/1\/012037","volume":"299","author":"Y Sudriani","year":"2019","unstructured":"Sudriani, Y., Ridwansyah, I., Rustini, H.A.: Long short term memory (LSTM) recurrent neural network (RNN) for discharge level prediction and forecast in Cimandiri river, Indonesia. IOP Conf. Ser. Earth Environ. Sci. 299, 1\u20138 (2019). https:\/\/doi.org\/10.1088\/1755-1315\/299\/1\/012037","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"12_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs10030452","volume":"10","author":"Y-L Kong","year":"2018","unstructured":"Kong, Y.-L., Huang, Q., Wang, C., Chen, J., Chen, J., He, D.: Long short-term memory neural networks for online disturbance detection in satellite image time series. Remote Sens. 10, 1\u201313 (2018). https:\/\/doi.org\/10.3390\/rs10030452","journal-title":"Remote Sens."},{"issue":"3","key":"12_CR10","doi-asserted-by":"publisher","first-page":"163","DOI":"10.3934\/DSFE.2022008","volume":"2","author":"K Zhang","year":"2022","unstructured":"Zhang, K., Hong, M.: Forecasting crude oil price using LSTM neural networks. Data Sci. Finan. Econ. 2(3), 163\u2013180 (2022). https:\/\/doi.org\/10.3934\/DSFE.2022008","journal-title":"Data Sci. Finan. Econ."},{"key":"12_CR11","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neucom.2022.06.111","volume":"503","author":"SR Dubey","year":"2022","unstructured":"Dubey, S.R., Singh, S.K., Chaudhuri, B.B.: Activation functions in deep learning: a comprehensive survey and benchmark. Neurocomputing 503, 92\u2013108 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2022.06.111","journal-title":"Neurocomputing"},{"key":"12_CR12","doi-asserted-by":"publisher","unstructured":"K\u0131l\u0131\u00e7arslan, S., Adem, K., \u00c7elik, M.: An overview of the activation functions used in deep learning algorithms. J. New Results Sci. 10(3), 75\u201388 (2021). https:\/\/doi.org\/10.54187\/jnrs.1011739","DOI":"10.54187\/jnrs.1011739"}],"container-title":["Communications in Computer and Information Science","Recent Challenges in Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5934-7_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T06:04:35Z","timestamp":1733119475000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5934-7_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819759330","9789819759347"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5934-7_12","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"13 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ras Al Khaimah","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Arab Emirates","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":"15 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 April 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2024\/index.php#about","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}