An Extensive Analytical Approach on Human Resources using Random Forest Algorithm

Papineni, Swarajya Lakshmi V.; Reddy, A. Mallikarjuna; yarlagadda, Sudeepti; Yarlagadda, Snigdha; Akkinen, Haritha

doi:10.14445/22315381/ijett-v69i5p217

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…Kangzhuoling is also a safe and efficient biological fungicide. Some scholars have used organic acid mold inhibitor KMC-LF2 to conduct mold proof test on corn and found that it has good mold proof effect [4]. In recent years, some researchers have also studied the control of microwave technology.…”

Section: Literature Reviewmentioning

confidence: 99%

Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

Jiang

Zhang

Huang

et al. 2022

Scientific Programming

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In order to further improve the identification efficiency of tobacco mildew, a rapid identification model of tobacco mildew based on random forest algorithm was proposed in this study. In order to ensure the feasibility and pertinence of the model study, this study takes redried leaf tobacco as the research object, selects high-temperature and high-humidity environment as the experimental conditions, and obtains the sample data of the degree of tobacco mildew under different experimental conditions. At the same time, this paper constructs a rapid identification model of tobacco mildew with the help of random forest algorithm. Through the model experimental results, it is found that the accuracy of the model for the rapid identification of training samples can reach 93.82%, while the accuracy of independent testing is 94.84%. The experimental results fully reflect the availability and efficiency of the random forest algorithm model in the rapid identification of tobacco mildew.

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Section: Literature Reviewmentioning

confidence: 99%

Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

Jiang

Zhang

Huang

et al. 2022

Scientific Programming

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“…On the basis of indepth understanding of forecasting mining technology, the multiple linear regression method is determined [11]. Through modeling, statistical index analysis, and significance test, the regression equation is obtained, the demand forecast for the number of enterprise personnel is carried out according to this, and the feasibility of the multiple regression model is verified [12,13].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Human Resource Management System Based on Data Mining Technology and Random Forest Algorithm

Tang

2022

Wireless Communications and Mobile Computing

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Talent is the first resource, and the role of talents in an enterprise is self-evident. Retaining key talents is crucial to the development of an enterprise. However, traditional data statistics methods cannot meet the needs of the government or enterprises in talent introduction, training, and reserve. For this reason, data mining technology and random forest algorithm are used to discover the hidden laws in the human resource management system and provide the government or enterprises with information. Decision support has become an urgent concern of the government and enterprises. This paper mainly studies human resources analysis and forecasting methods based on data mining technology and random forest. First, the data mining technology, naive Bayesian algorithm, decision tree algorithm, and random forest algorithm used in the research are introduced, and finally, the paper concludes this research.

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“…The process from a single tree to a forest is solved. The problem of overfitting of a single decision tree increases the noise tolerance of the model [5]. There is also a big feature in the random forest algorithm -the Bagging method.…”

Section: Random Forest Algorithmmentioning

confidence: 99%

A Platform for Intelligent Funding Information Management System for Colleges and Universities Based on Random Forest Algorithm

Wang¹

2023

Atlantis Highlights in Intelligent Systems

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As for the financial aid information management for students with financial difficulties, due to the wide range of financial aid and subsidy objects, various subsidy types, inconsistent time and complicated subsidy review and approval processes, the work of subsidy information management staff are very headache, and it is difficult to improve work efficiency. With the increasing complexity and difficulty of poverty aid work in colleges and universities (CAU), the advantages of informatization are used to integrate poverty aid work into the management of CAU. The random forest algorithm screens and organizes data, realizes intelligent information management, and enhances precise funding for poor college students. This paper uses the B/S structure to build the platform architecture of the funding information management system, and sets up multiple functional modules for the system. After the system design is completed, the CPU, memory and disk usage of the system are tested. It is found that with the increase of concurrent users, its the share has also increased. In the system, the informatization of financial aid application and poverty identification management has been realized, the identification work can be completed more concisely and efficiently, and the update of student poverty information is also very convenient.

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An Extensive Analytical Approach on Human Resources using Random Forest Algorithm

Cited by 13 publications

References 13 publications

Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

Optimization of Human Resource Management System Based on Data Mining Technology and Random Forest Algorithm

A Platform for Intelligent Funding Information Management System for Colleges and Universities Based on Random Forest Algorithm

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