{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:16:22Z","timestamp":1743063382782,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":14,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981250"},{"type":"electronic","value":"9789819981267"}],"license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"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\/978-981-99-8126-7_21","type":"book-chapter","created":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T08:05:19Z","timestamp":1700813119000},"page":"265-277","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Health Evaluation Algorithm for\u00a0Edge Nodes Based on\u00a0LSTM"],"prefix":"10.1007","author":[{"given":"Qian","family":"Sun","sequence":"first","affiliation":[]},{"given":"Zhengfan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jiarui","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qinglin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Chadza, T., Kyriakopoulos, K.G., Lambotharan, S.: Contemporary sequential network attacks prediction using hidden Markov model. In: 2019 17th International Conference on Privacy, Security and Trust (PST), pp. 1\u20133. IEEE (2019)","DOI":"10.1109\/PST47121.2019.8949035"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Choi, Y., Lim, H., Choi, H., Kim, I.J.: GAN-based anomaly detection and localization of multivariate time series data for power plant. In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 71\u201374. IEEE (2020)","DOI":"10.1109\/BigComp48618.2020.00-97"},{"key":"21_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-3-642-24797-2_2","volume-title":"Supervised Sequence Labelling with Recurrent Neural Networks","author":"A Graves","year":"2012","unstructured":"Graves, A.: Supervised sequence labelling. In: Graves, A. (ed.) Supervised Sequence Labelling with Recurrent Neural Networks, pp. 5\u201313. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-24797-2_2"},{"issue":"8","key":"21_CR4","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., et al.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"8","key":"21_CR5","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651\u2013666 (2010)","journal-title":"Pattern Recogn. Lett."},{"key":"21_CR6","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1109\/ACCESS.2017.2779939","volume":"6","author":"F Karim","year":"2017","unstructured":"Karim, F., Majumdar, S., Darabi, H., Chen, S.: LSTM fully convolutional networks for time series classification. IEEE Access 6, 1662\u20131669 (2017)","journal-title":"IEEE Access"},{"key":"21_CR7","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"21_CR8","unstructured":"Malhotra, P., Vig, L., Shroff, G., Agarwal, P., et al.: Long short term memory networks for anomaly detection in time series. In: ESANN, vol. 2015, p. 89 (2015)"},{"issue":"12","key":"21_CR9","doi-asserted-by":"publisher","first-page":"5094","DOI":"10.3390\/su12125094","volume":"12","author":"L Qiao","year":"2020","unstructured":"Qiao, L., Liu, D., Yuan, X., Wang, Q., Ma, Q.: Generation and prediction of construction and demolition waste using exponential smoothing method: a case study of Shandong Province, China. Sustainability 12(12), 5094 (2020)","journal-title":"Sustainability"},{"key":"21_CR10","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.eswa.2017.10.021","volume":"93","author":"R Saini","year":"2018","unstructured":"Saini, R., Roy, P.P., Dogra, D.P.: A segmental hmm based trajectory classification using genetic algorithm. Expert Syst. Appl. 93, 169\u2013181 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"21_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-021-01235-8","volume":"21","author":"AL Schaffer","year":"2021","unstructured":"Schaffer, A.L., Dobbins, T.A., Pearson, S.A.: Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med. Res. Methodol. 21(1), 1\u201312 (2021)","journal-title":"BMC Med. Res. Methodol."},{"key":"21_CR12","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.jprocont.2018.04.008","volume":"68","author":"L Wang","year":"2018","unstructured":"Wang, L., Yang, C., Sun, Y., Zhang, H., Li, M.: Effective variable selection and moving window hmm-based approach for iron-making process monitoring. J. Process Control 68, 86\u201395 (2018)","journal-title":"J. Process Control"},{"key":"21_CR13","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.measurement.2019.06.038","volume":"146","author":"S Wang","year":"2019","unstructured":"Wang, S., Chen, J., Wang, H., Zhang, D.: Degradation evaluation of slewing bearing using hmm and improved GRU. Measurement 146, 385\u2013395 (2019)","journal-title":"Measurement"},{"key":"21_CR14","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.knosys.2017.12.027","volume":"144","author":"S Wang","year":"2018","unstructured":"Wang, S., Xiang, J., Zhong, Y., Zhou, Y.: Convolutional neural network-based hidden Markov models for rolling element bearing fault identification. Knowl.-Based Syst. 144, 65\u201376 (2018)","journal-title":"Knowl.-Based Syst."}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8126-7_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:34:28Z","timestamp":1709811268000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8126-7_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,13]]},"ISBN":["9789819981250","9789819981267"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8126-7_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,13]]},"assertion":[{"value":"13 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}