Employee Culling based on of Online Work Assessment through Machine Learning Algorithm

Employee Culling based on of Online Work Assessment through Machine Learning Algorithm

Khaled Redwan

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Yeasin Ahammed

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Masum Akram Hridoy

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Fernaz Narin Nur

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A. H. M. Saiful Islam

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Notre Dame University Bangladesh

Employee Culling based on of Online Work Assessment through Machine Learning Algorithm

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Abstract

Job analysis, planning employee needs, recruiting the appropriate people, wages and salary management are the important theme of human resource management. Human resource management also includes evaluating performance, resolving problems, and create communication with all employees at all levels. On the other hand, Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans. So through these two sectors such as computation and business administration, in this paper on employee culling based on work assessment by which machine learning algorithm such as KNN, SVM, The Decision tree can give the best result (perfect employee). We also focus on the accuracy that algorithm is performing. We marked an employee through their experience, language skills, skills, graduation, etc. we create e model by which we can get input through the companies and give them a perfect result through their requirement assessment and machine learning algorithm.

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The Implementation of K-Nearest Neighbor Algorithm in Case-Based ReasoningModel for Forming Automatic Answer Identity and Searching Answer Similarity ofAlgorithm Case Yana Aditia Gerhana1, Aldy Rialdy Atmadja2.
Arnold Kalalo,Rosmini Rosmini,Anto Anto (2024). Klasifikasi Penyakit Karies Gigi Menggunakan Algoritma Modified K-Nearest Neighbor.
W Lei,W Xu,R Qi,R Huang (2020). FACILE SYNTHESIS AND GROWTH MECHANISM OF TRIGONAL SELENIUM NANOWRIES.
Zhuang Wang,Wei-Dong Hu A Quick Evidential Classification Algorithm Based On K-Nearest Neighbor Rule.
Amin Zollanvari (2023). k-Nearest Neighbors.
Jeffrey Dastin (2022). Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women *.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Khaled Redwan. 2019. \u201cEmployee Culling based on of Online Work Assessment through Machine Learning Algorithm\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 19 (GJCST Volume 19 Issue D4).

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GJCST Volume 19 Issue D4

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-D Classification F.2.1

Submission ReceivedDecember 15, 2018
Peer Review Double Blind
Handling Editor
Accepted January 4, 2019
Published January 15, 2019

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v1.2

Issue date

November 14, 2019

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