{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:40:45Z","timestamp":1776811245403,"version":"3.51.2"},"reference-count":25,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"4","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p>Traditional human resource management systems face the problems of low efficiency and insufficient accuracy in data processing and decision support, while intelligent human resource management systems based on machine learning algorithms can significantly improve management efficiency and decision-making accuracy through advanced data analysis and automated decision-making. By analyzing the training duration and response efficiency of various algorithms, it was observed that the machine learning algorithm\u2019s training time is approximately 10.3 times longer than that of the non-optimized machine learning algorithm and 3.6 times longer than that of the intelligent management system, indicating strong time performance. Additionally, the response time of the classification model after applying feature selection is slightly improved compared to the original machine learning algorithm, measuring 225% of the non-optimized version and 98% of the response time of the intelligent HR management system. The key technologies involved in system development include neural network models and classification analysis techniques, and the implementation of these algorithms in the intelligent HR management system provides notable benefits.<\/jats:p>","DOI":"10.1177\/14727978251318814","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T14:19:41Z","timestamp":1741097981000},"page":"3684-3696","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Construction and application of intelligent human resource management system based on machine learning algorithm"],"prefix":"10.66113","volume":"25","author":[{"given":"Ping","family":"Yang","sequence":"first","affiliation":[{"name":"School of Economics Sichuan University Science &amp; Engineering, Zigong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Foreign Languages Sichuan University Science &amp; Engineering, Zigong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1108\/IJPPM-08-2020-0427"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21113791"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100514"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122538"},{"key":"e_1_3_2_6_2","first-page":"172","article-title":"Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review","volume":"33","author":"Vrontis D","year":"2023","unstructured":"Vrontis D, Christofi M, Pereira V, et al. Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. Int J Artif Intell 2023; 33: 172\u2013201.","journal-title":"Int J Artif Intell"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121794"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0266373"},{"issue":"1","key":"e_1_3_2_9_2","first-page":"4681959","article-title":"Human resources balanced allocation method based on deep learning algorithm","volume":"2021","author":"Shi W","year":"2021","unstructured":"Shi W, Li Q. Human resources balanced allocation method based on deep learning algorithm. Sci Program 2021; 2021(1): 4681959.","journal-title":"Sci Program"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2023.100568"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11030383"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3571728"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3296140"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1080\/08839514.2023.2198897"},{"key":"e_1_3_2_15_2","first-page":"17","article-title":"Machine learning through the lens of e-commerce initiatives: an up-to-date systematic literature review","volume":"41","author":"Policarpo LM","year":"2021","unstructured":"Policarpo LM, da Silveira DE, Righi RD, et al. Machine learning through the lens of e-commerce initiatives: an up-to-date systematic literature review. Computer Science Review. 2021; 41: 17.","journal-title":"Computer Science Review"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121947"},{"key":"e_1_3_2_17_2","first-page":"74","article-title":"Deep reinforcement learning-based scheduling in distributed systems: a critical review","author":"Abadi ZJK","year":"2024","unstructured":"Abadi ZJK, Mansouri N, Javidi MM. Deep reinforcement learning-based scheduling in distributed systems: a critical review. Knowl Inf Syst 2024: 74.","journal-title":"Knowl Inf Syst"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1177\/0008125619867910"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13638-020-01677-6"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-023-09903-2"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3179047"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.3390\/s23115281"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13636-024-00329-7"},{"issue":"1","key":"e_1_3_2_24_2","first-page":"1418020","article-title":"Deep neural network model construction for digital human resource management with human\u2010job matching","volume":"2022","author":"Ni Q","year":"2022","unstructured":"Ni Q. Deep neural network model construction for digital human resource management with human\u2010job matching. Comput Intell Neurosci 2022; 2022(1): 1418020.","journal-title":"Comput Intell Neurosci"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2023.103348"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107126"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978251318814","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14727978251318814","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978251318814","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:05:53Z","timestamp":1776809153000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14727978251318814"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,4]]},"references-count":25,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["10.1177\/14727978251318814"],"URL":"https:\/\/doi.org\/10.1177\/14727978251318814","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,4]]}}}