{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T17:19:18Z","timestamp":1743095958811,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442155"},{"type":"electronic","value":"9783031442162"}],"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\/978-3-031-44216-2_2","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T07:02:58Z","timestamp":1695279778000},"page":"13-25","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Echo State Network-Based Method for\u00a0Identity Recognition with\u00a0Continuous Blood Pressure Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7208-9003","authenticated-orcid":false,"given":"Ziqiang","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8114-7837","authenticated-orcid":false,"given":"Kantaro","family":"Fujiwara","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6223-4406","authenticated-orcid":false,"given":"Gouhei","family":"Tanaka","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"2_CR1","unstructured":"Bianchi, F.M., Scardapane, S., L\u00f8kse, S., Jenssen, R.: Bidirectional deep-readout echo state networks. arXiv preprint arXiv:1711.06509 (2017)"},{"key":"2_CR2","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":"2_CR3","volume-title":"Algorithms to Live By: The Computer Science of Human Decisions","author":"B Christian","year":"2016","unstructured":"Christian, B., Griffiths, T.: Algorithms to Live By: The Computer Science of Human Decisions. Macmillan, New York (2016)"},{"issue":"10","key":"2_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0076585","volume":"8","author":"M Elgendi","year":"2013","unstructured":"Elgendi, M., Norton, I., Brearley, M., Abbott, D., Schuurmans, D.: Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions. PLoS ONE 8(10), e76585 (2013)","journal-title":"PLoS ONE"},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Gallicchio, C., Micheli, A., Pedrelli, L.: Design of deep echo state networks. Neural Netw. 108, 33\u201347 (2018). https:\/\/doi.org\/10.1016\/j.neunet.2018.08.002. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0893608018302223","DOI":"10.1016\/j.neunet.2018.08.002"},{"key":"2_CR6","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-3-030-43883-8_3","volume-title":"Recent Trends in Learning From Data","author":"C Gallicchio","year":"2020","unstructured":"Gallicchio, C., Scardapane, S.: Deep randomized neural networks. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds.) Recent Trends in Learning From Data. SCI, vol. 896, pp. 43\u201368. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-43883-8_3"},{"key":"2_CR7","unstructured":"Jaeger, H.: Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the \u201cecho state network\u201d approach (2002)"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. (2021)","DOI":"10.1109\/TNNLS.2021.3084827"},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neucom.2021.08.122","volume":"467","author":"Z Li","year":"2022","unstructured":"Li, Z., Tanaka, G.: Multi-reservoir echo state networks with sequence resampling for nonlinear time-series prediction. Neurocomputing 467, 115\u2013129 (2022)","journal-title":"Neurocomputing"},{"issue":"3","key":"2_CR10","doi-asserted-by":"publisher","first-page":"308","DOI":"10.3390\/electronics10030308","volume":"10","author":"S Mekruksavanich","year":"2021","unstructured":"Mekruksavanich, S., Jitpattanakul, A.: Biometric user identification based on human activity recognition using wearable sensors: an experiment using deep learning models. Electronics 10(3), 308 (2021)","journal-title":"Electronics"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Norman, T.L.: Foundational security and access control concepts, Chapter 2. In: Norman, T.L. (ed.) Electronic Access Control, 2nd edn., pp. 21\u201342. Butterworth-Heinemann (2017). https:\/\/doi.org\/10.1016\/B978-0-12-805465-9.00002-6. www.sciencedirect.com\/science\/article\/pii\/B9780128054659000026","DOI":"10.1016\/B978-0-12-805465-9.00002-6"},{"key":"2_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01553-3","volume":"21","author":"JGD Ochoa","year":"2021","unstructured":"Ochoa, J.G.D., Csisz\u00e1r, O., Schimper, T.: Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks. BMC Med. Inform. Decis. Mak. 21, 1\u201315 (2021)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1016\/j.inffus.2022.10.032","volume":"91","author":"Z Qin","year":"2023","unstructured":"Qin, Z., Zhao, P., Zhuang, T., Deng, F., Ding, Y., Chen, D.: A survey of identity recognition via data fusion and feature learning. Inf. Fusion 91, 694\u2013712 (2023)","journal-title":"Inf. Fusion"},{"key":"2_CR14","unstructured":"Salehinejad, H., Sankar, S., Barfett, J., Colak, E., Valaee, S.: Recent advances in recurrent neural networks. arXiv preprint arXiv:1801.01078 (2017)"},{"issue":"1","key":"2_CR15","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1038\/s41597-022-01202-y","volume":"9","author":"A Schumann","year":"2022","unstructured":"Schumann, A., B\u00e4r, K.J.: Autonomic aging\u2013a dataset to quantify changes of cardiovascular autonomic function during healthy aging. Sci. Data 9(1), 95 (2022)","journal-title":"Sci. Data"},{"issue":"6","key":"2_CR16","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1109\/78.678493","volume":"46","author":"IW Selesnick","year":"1998","unstructured":"Selesnick, I.W., Burrus, C.S.: Generalized digital Butterworth filter design. IEEE Trans. Signal Process. 46(6), 1688\u20131694 (1998)","journal-title":"IEEE Trans. Signal Process."},{"key":"2_CR17","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s11334-021-00392-9","volume":"17","author":"M Szymkowski","year":"2021","unstructured":"Szymkowski, M., Jasi\u0144ski, P., Saeed, K.: Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform. Innov. Syst. Softw. Eng. 17, 309\u2013317 (2021)","journal-title":"Innov. Syst. Softw. Eng."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44216-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T07:03:38Z","timestamp":1695279818000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44216-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442155","9783031442162"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44216-2_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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":"The codes of the proposed method are publicly available on the following URL: .","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Availability"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 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":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","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":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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)"}},{"value":"type of other papers accepted  : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}