{"title":"通过推动业务模式的创新和增长,实现企业资源管理与技术的协同效应","authors":"Mengtong Xie, Huaqi Chai","doi":"10.3233/jifs-235379","DOIUrl":null,"url":null,"abstract":"A human resources management plan is presently recognised as one of the most important components of a corporate technique. This is due to the fact that its major purpose is to interact with people, who are the most precious asset that an organisation has. It is impossible for an organisation to achieve its objectives without the participation of individuals. An organisation may effectively plan as well as manage individual processes to support the organization’s objectives and adapt nimbly to any change if it has well-prepared HR techniques and an action plan for its execution. This investigation puts up a fresh way for the board of directors of a private firm to increase their assets and advance their growth by using cloud programming that is characterised by networks. The small company resource has been improved by strengthening human resource management techniques, and the cloud SDN network is used for job scheduling using Q-convolutional reinforcement recurrent learning. The proposed technique attained Quadratic normalized square error of 60%, existing SDN attained 55%, HRM attained 58% for Synthetic dataset; for Human resources dataset propsed technique attained Quadratic normalized square error of 62%, existing SDN attained 56%, HRM attained 59% ; proposed technique attained Quadratic normalized square error of 64%, existing SDN attained 58%, HRM attained 59% for SyriaTel dataset.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Synergizing enterprise resource management with technology through driving innovation and growth in business models\",\"authors\":\"Mengtong Xie, Huaqi Chai\",\"doi\":\"10.3233/jifs-235379\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A human resources management plan is presently recognised as one of the most important components of a corporate technique. This is due to the fact that its major purpose is to interact with people, who are the most precious asset that an organisation has. It is impossible for an organisation to achieve its objectives without the participation of individuals. An organisation may effectively plan as well as manage individual processes to support the organization’s objectives and adapt nimbly to any change if it has well-prepared HR techniques and an action plan for its execution. This investigation puts up a fresh way for the board of directors of a private firm to increase their assets and advance their growth by using cloud programming that is characterised by networks. The small company resource has been improved by strengthening human resource management techniques, and the cloud SDN network is used for job scheduling using Q-convolutional reinforcement recurrent learning. The proposed technique attained Quadratic normalized square error of 60%, existing SDN attained 55%, HRM attained 58% for Synthetic dataset; for Human resources dataset propsed technique attained Quadratic normalized square error of 62%, existing SDN attained 56%, HRM attained 59% ; proposed technique attained Quadratic normalized square error of 64%, existing SDN attained 58%, HRM attained 59% for SyriaTel dataset.\",\"PeriodicalId\":509313,\"journal\":{\"name\":\"Journal of Intelligent & Fuzzy Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligent & Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/jifs-235379\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent & Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/jifs-235379","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
人力资源管理计划是目前公认的企业技术最重要的组成部分之一。这是因为人力资源管理计划的主要目的是与人互动,而人是企业最宝贵的资产。没有个人的参与,组织不可能实现其目标。如果组织拥有准备充分的人力资源技术和行动计划,就可以有效地规划和管理个人流程,以支持组织目标的实现,并灵活地适应任何变化。这项调查为私营企业的董事会提出了一种全新的方法,即通过使用以网络为特征的云程序来增加资产和促进发展。通过加强人力资源管理技术改善了小公司的资源,并利用 Q-convolutional 强化循环学习将云 SDN 网络用于工作调度。在合成数据集方面,建议技术的二次归一化平方误差为60%,现有SDN为55%,HRM为58%;在人力资源数据集方面,建议技术的二次归一化平方误差为62%,现有SDN为56%,HRM为59%;在SyriaTel数据集方面,建议技术的二次归一化平方误差为64%,现有SDN为58%,HRM为59%。
Synergizing enterprise resource management with technology through driving innovation and growth in business models
A human resources management plan is presently recognised as one of the most important components of a corporate technique. This is due to the fact that its major purpose is to interact with people, who are the most precious asset that an organisation has. It is impossible for an organisation to achieve its objectives without the participation of individuals. An organisation may effectively plan as well as manage individual processes to support the organization’s objectives and adapt nimbly to any change if it has well-prepared HR techniques and an action plan for its execution. This investigation puts up a fresh way for the board of directors of a private firm to increase their assets and advance their growth by using cloud programming that is characterised by networks. The small company resource has been improved by strengthening human resource management techniques, and the cloud SDN network is used for job scheduling using Q-convolutional reinforcement recurrent learning. The proposed technique attained Quadratic normalized square error of 60%, existing SDN attained 55%, HRM attained 58% for Synthetic dataset; for Human resources dataset propsed technique attained Quadratic normalized square error of 62%, existing SDN attained 56%, HRM attained 59% ; proposed technique attained Quadratic normalized square error of 64%, existing SDN attained 58%, HRM attained 59% for SyriaTel dataset.