{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:28:38Z","timestamp":1762342118933,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031368073"},{"type":"electronic","value":"9783031368080"}],"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\/978-3-031-36808-0_6","type":"book-chapter","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T23:03:04Z","timestamp":1688079784000},"page":"87-98","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AutoML Framework for Labor Potential Modeling"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0414-906X","authenticated-orcid":false,"given":"Vladislav","family":"Kovalevsky","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2625-452X","authenticated-orcid":false,"given":"Elena","family":"Stankova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5877-4461","authenticated-orcid":false,"given":"Nataly","family":"Zhukova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2101-9750","authenticated-orcid":false,"given":"Oksana","family":"Ogiy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2759-6214","authenticated-orcid":false,"given":"Alexander","family":"Tristanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"6_CR1","unstructured":"Patutina, E.S.: Main approaches to the interpretation of the concepts of \u201clabor potential\u201d and \u201chuman potential\u201d as the basis for the quality of the labor force in the works of domestic and foreign scientists. In: Science Research Practice. Collection of Selected Articles Based on the Materials of the International Scientific Conference, pp. 215\u2013220 (2020)"},{"key":"6_CR2","unstructured":"Human Resources Management: Concepts, Methodologies, Tools, and Applications, p. 1513. IGI Global, Pennsylvania (2012)"},{"issue":"1","key":"6_CR3","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1146\/annurev-soc-073014-112230","volume":"41","author":"SE Page","year":"2015","unstructured":"Page, S.E.: What sociologists should know about complexity. Ann. Rev. Sociol. 41(1), 21\u201341 (2015). https:\/\/doi.org\/10.1146\/annurev-soc-073014-112230","journal-title":"Ann. Rev. Sociol."},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Heravi, G., Eslamdoost, E.: Applying artificial neural networks for measuring and predicting construction-labor productivity. J. Constr. Eng. Manag. 141(10) (2016)","DOI":"10.1061\/(ASCE)CO.1943-7862.0001006"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Ogiy, O.G., Osipov, V.Yu., Tristanov, A.B., Zhukova, N.A.: The process of managing labor potential of the fishery complex as an object of modeling using artificial neural networks. In: AIP Conference Proceedings, vol. 2661, p. 030001 (2022)","DOI":"10.1063\/5.0107815"},{"key":"6_CR6","doi-asserted-by":"publisher","unstructured":"Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013. ACM, New York (2013). https:\/\/doi.org\/10.1145\/2487575.2487629","DOI":"10.1145\/2487575.2487629"},{"key":"6_CR7","doi-asserted-by":"publisher","unstructured":"Guo, X., van Stein, B., B\u00e4ck, T.: A new approach towards the combined algorithm selection and hyper-parameter optimization problem. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2042\u20132049. IEEE, Xiamen (2019). https:\/\/doi.org\/10.1109\/ssci44817.2019.9003174","DOI":"10.1109\/ssci44817.2019.9003174"},{"key":"6_CR8","unstructured":"Auto-WEKA. https:\/\/www.cs.ubc.ca\/labs\/algorithms\/Projects\/autoweka\/. Accessed 07 Apr 2023"},{"key":"6_CR9","unstructured":"Hyperopt-sklearn. https:\/\/hyperopt.github.io\/hyperopt-sklearn\/. Accessed 10 Apr 2023"},{"key":"6_CR10","unstructured":"Feurer, et al.: Auto-Sklearn 2.0: Hands-Free AutoML via Meta-Learning. arXiv (2020)"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Olson, R.S., Bartley, N., Urbanowicz, R.J., Moore, J.H.: Evaluation of a tree-based pipeline optimization tool for automating data science. In: Proceedings of GECCO 2016, pp. 485\u2013492 (2016)","DOI":"10.1145\/2908812.2908918"},{"key":"6_CR12","unstructured":"H2O.AI - The fastest, most accurate AI Cloud Platform. https:\/\/h2o.ai. Accessed 10 Apr 2023"},{"key":"6_CR13","first-page":"1","volume":"6","author":"H Jin","year":"2023","unstructured":"Jin, H., Chollet, F., Song, Q., Hu, X.: AutoKeras: an AutoML library for deep learning. J. Mach. Learn. Res. 6, 1\u20136 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR14","unstructured":"Scikit-Learn. Machine Learning in Python. https:\/\/scikit-learn.org\/stable\/. Accessed 07 Apr 2023"},{"key":"6_CR15","unstructured":"Weka 3: Machine Learning Software in Java. https:\/\/www.cs.waikato.ac.nz\/ml\/weka\/. Accessed 07 Apr 2023"},{"key":"6_CR16","unstructured":"Hyperopt: Distributed Asynchronous Hyper-parameter Optimization. http:\/\/hyperopt.github.io\/hyperopt\/. Accessed 07 Apr 2023"},{"key":"6_CR17","unstructured":"OpenML. A worldwide machine learning lab. https:\/\/www.openml.org. Accessed 07 Apr 2023"},{"key":"6_CR18","unstructured":"TensorFlow. An end-to-end open source machine learning platform for everyone. https:\/\/www.tensorflow.org. Accessed 07 Apr 2023"},{"key":"6_CR19","unstructured":"AutoML Framework for AutoML libraries comparison. https:\/\/github.com\/DarkEol\/AutoML-Framework. Accessed 07 Apr 2023"},{"key":"6_CR20","unstructured":"Kaggle. https:\/\/www.kaggle.com. Accessed 07 Apr 2023"},{"key":"6_CR21","unstructured":"Employee Performance Prediction. https:\/\/www.kaggle.com\/datasets\/gauravduttakiit\/employee-performance-prediction. Accessed 07 Apr 2023"},{"key":"6_CR22","unstructured":"Human Resources Data Set. https:\/\/www.kaggle.com\/datasets\/rhuebner\/human-resources-data-set. Accessed 07 Apr 2023"},{"key":"6_CR23","unstructured":"IBM HR Analytics Employee Attrition & Performance. https:\/\/www.kaggle.com\/datasets\/pavansubhasht\/ibm-hr-analytics-attrition-dataset. Accessed 07 Apr 2023"},{"key":"6_CR24","unstructured":"Employee Performance Analysis INX Future Inc. https:\/\/www.kaggle.com\/datasets\/eshwarganta\/employee-performance-analysis-inx-future-inc\/. Accessed 07 Apr 2023"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36808-0_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T23:09:23Z","timestamp":1688080163000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36808-0_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031368073","9783031368080"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36808-0_6","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":"30 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","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":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.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":"Custom based on Cyberchair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"283","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":"67","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":"13","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":"24% - 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":"2.5","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":"8,5","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)"}},{"value":"PHD Showcase Papers: 6(for main conf) \/ For ICCSA 2023 Workshops 876 subm sent, 350 full papers and 29 short papers accepted, additional PHD Showcase Papers: 2","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)"}}]}}