{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T10:24:23Z","timestamp":1746095063157,"version":"3.40.3"},"publisher-location":"Cham","reference-count":8,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031417733"},{"type":"electronic","value":"9783031417740"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-41774-0_44","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T03:25:20Z","timestamp":1695266720000},"page":"560-572","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Bacterial Evolutionary Algorithm Based Autoencoder Architecture Search for\u00a0Anomaly Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3022-0665","authenticated-orcid":false,"given":"Hunor Istv\u00e1n","family":"Luk\u00e1cs","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6042-4176","authenticated-orcid":false,"given":"Tam\u00e1s","family":"Fischl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7838-6148","authenticated-orcid":false,"given":"J\u00e1nos","family":"Botzheim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Bertsekas, D.P.: Incremental gradient, subgradient, and proximal methods for convex optimization: a survey. Optimization 2010 (2015)","DOI":"10.7551\/mitpress\/8996.003.0006"},{"key":"44_CR2","doi-asserted-by":"publisher","first-page":"23359","DOI":"10.1109\/ACCESS.2020.2967638","volume":"8","author":"Z Cao","year":"2020","unstructured":"Cao, Z., et al.: Scalable distribution systems state estimation using long short-term memory networks as surrogates. IEEE Access 8, 23359\u201323368 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2967638","journal-title":"IEEE Access"},{"key":"44_CR3","doi-asserted-by":"publisher","first-page":"15:1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41, 15:1-15:58 (2009)","journal-title":"ACM Comput. Surv."},{"key":"44_CR4","unstructured":"Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. CoRR abs\/1412.6980 (2014)"},{"key":"44_CR5","doi-asserted-by":"publisher","unstructured":"Maenaka, K.: MEMS inertial sensors and their applications. In: 2008 5th International Conference on Networked Sensing Systems, pp. 71\u201373 (2008). https:\/\/doi.org\/10.1109\/INSS.2008.4610859","DOI":"10.1109\/INSS.2008.4610859"},{"issue":"5","key":"44_CR6","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1109\/91.797983","volume":"7","author":"NE Nawa","year":"1999","unstructured":"Nawa, N.E., Furuhashi, T.: Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Trans. Fuzzy Syst. 7(5), 608\u2013616 (1999). https:\/\/doi.org\/10.1109\/91.797983","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"44_CR7","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.isatra.2021.01.046","volume":"117","author":"CC Phiri","year":"2021","unstructured":"Phiri, C.C., et al.: Fuzzy rule-based model for outlier detection in a topical negative pressure wound therapy device. ISA Trans. 117, 16\u201327 (2021). https:\/\/doi.org\/10.1016\/j.isatra.2021.01.046","journal-title":"ISA Trans."},{"key":"44_CR8","doi-asserted-by":"publisher","unstructured":"Zhang, T., Yu, B.: Boosting with early stopping: convergence and consistency. Ann. Stat. 33(4) (2005). https:\/\/doi.org\/10.1214\/009053605000000255","DOI":"10.1214\/009053605000000255"}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41774-0_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:35:30Z","timestamp":1695278130000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41774-0_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031417733","9783031417740"],"references-count":8,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41774-0_44","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Budapest","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hungary","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":"27 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2023\/","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":"218","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":"59","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":"27% - 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":"3.01","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":"1.86","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)"}}]}}