{"title":"基于深度学习的企业人力资源管理风险预测","authors":"Min Ding, Hao Wu","doi":"10.3233/hsm-230064","DOIUrl":null,"url":null,"abstract":"BACKGROUND: The efficiency and accuracy of risk prediction in traditional enterprise human resource management (HRM) cannot meet practical needs. In response to this deficiency, this study proposes an enterprise HRM risk intelligent prediction model based on deep learning. METHODS: Two tasks were completed in this study. First, based on the existing research results and the current status of enterprise HRM, the HRM risk assessment system is constructed and streamlined. Second, for the defects of Back Propagation Neural Network (BPNN) model, Seagull Optimization Algorithm (SOA) is used to optimize it. The Whale Optimization Algorithm (WOA) is introduced to promote the SOA for its weak global search capability and its tendency to converge prematurely. RESULTS: By simplifying the HR risk assessment system and optimizing the BPNN using the SOA algorithm, an intelligent HRM risk prediction model based on the ISOA-BPNN was constructed. The results show that the error value of the ISOA-BPNN model is 0.02, the loss value is 0.50, the F1 value is 95.7%, the recall value is 94.9%, the MSE value is 0.31, the MAE value is 8.4, and the accuracy is 99.53%, both of which are superior to the other two models. CONCLUSIONS: In summary, the study of the HRM risk intelligent prediction model constructed based on ISOA-BPNN has high accuracy and efficiency, which can effectively achieve HRM risk intelligent prediction and has positive significance for enterprise development.","PeriodicalId":13113,"journal":{"name":"Human systems management","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Risk prediction of enterprise human resource management based on deep learning\",\"authors\":\"Min Ding, Hao Wu\",\"doi\":\"10.3233/hsm-230064\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BACKGROUND: The efficiency and accuracy of risk prediction in traditional enterprise human resource management (HRM) cannot meet practical needs. In response to this deficiency, this study proposes an enterprise HRM risk intelligent prediction model based on deep learning. METHODS: Two tasks were completed in this study. First, based on the existing research results and the current status of enterprise HRM, the HRM risk assessment system is constructed and streamlined. Second, for the defects of Back Propagation Neural Network (BPNN) model, Seagull Optimization Algorithm (SOA) is used to optimize it. The Whale Optimization Algorithm (WOA) is introduced to promote the SOA for its weak global search capability and its tendency to converge prematurely. RESULTS: By simplifying the HR risk assessment system and optimizing the BPNN using the SOA algorithm, an intelligent HRM risk prediction model based on the ISOA-BPNN was constructed. The results show that the error value of the ISOA-BPNN model is 0.02, the loss value is 0.50, the F1 value is 95.7%, the recall value is 94.9%, the MSE value is 0.31, the MAE value is 8.4, and the accuracy is 99.53%, both of which are superior to the other two models. CONCLUSIONS: In summary, the study of the HRM risk intelligent prediction model constructed based on ISOA-BPNN has high accuracy and efficiency, which can effectively achieve HRM risk intelligent prediction and has positive significance for enterprise development.\",\"PeriodicalId\":13113,\"journal\":{\"name\":\"Human systems management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Human systems management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/hsm-230064\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human systems management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/hsm-230064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
Risk prediction of enterprise human resource management based on deep learning
BACKGROUND: The efficiency and accuracy of risk prediction in traditional enterprise human resource management (HRM) cannot meet practical needs. In response to this deficiency, this study proposes an enterprise HRM risk intelligent prediction model based on deep learning. METHODS: Two tasks were completed in this study. First, based on the existing research results and the current status of enterprise HRM, the HRM risk assessment system is constructed and streamlined. Second, for the defects of Back Propagation Neural Network (BPNN) model, Seagull Optimization Algorithm (SOA) is used to optimize it. The Whale Optimization Algorithm (WOA) is introduced to promote the SOA for its weak global search capability and its tendency to converge prematurely. RESULTS: By simplifying the HR risk assessment system and optimizing the BPNN using the SOA algorithm, an intelligent HRM risk prediction model based on the ISOA-BPNN was constructed. The results show that the error value of the ISOA-BPNN model is 0.02, the loss value is 0.50, the F1 value is 95.7%, the recall value is 94.9%, the MSE value is 0.31, the MAE value is 8.4, and the accuracy is 99.53%, both of which are superior to the other two models. CONCLUSIONS: In summary, the study of the HRM risk intelligent prediction model constructed based on ISOA-BPNN has high accuracy and efficiency, which can effectively achieve HRM risk intelligent prediction and has positive significance for enterprise development.
期刊介绍:
Human Systems Management (HSM) is an interdisciplinary, international, refereed journal, offering applicable, scientific insight into reinventing business, civil-society and government organizations, through the sustainable development of high-technology processes and structures. Adhering to the highest civic, ethical and moral ideals, the journal promotes the emerging anthropocentric-sociocentric paradigm of societal human systems, rather than the pervasively mechanistic and organismic or medieval corporatism views of humankind’s recent past. Intentionality and scope Their management autonomy, capability, culture, mastery, processes, purposefulness, skills, structure and technology often determine which human organizations truly are societal systems, while others are not. HSM seeks to help transform human organizations into true societal systems, free of bureaucratic ills, along two essential, inseparable, yet complementary aspects of modern management: a) the management of societal human systems: the mastery, science and technology of management, including self management, striving for strategic, business and functional effectiveness, efficiency and productivity, through high quality and high technology, i.e., the capabilities and competences that only truly societal human systems create and use, and b) the societal human systems management: the enabling of human beings to form creative teams, communities and societies through autonomy, mastery and purposefulness, on both a personal and a collegial level, while catalyzing people’s creative, inventive and innovative potential, as people participate in corporate-, business- and functional-level decisions. Appreciably large is the gulf between the innovative ideas that world-class societal human systems create and use, and what some conventional business journals offer. The latter often pertain to already refuted practices, while outmoded business-school curricula reinforce this problematic situation.