Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method

Prasetyaningrun, Putri Taqwa; Pratama, Irfan; Chandra, Albert Yakobus

doi:10.33005/ijconsist.v2i02.43

Cited by 3 publications

(1 citation statement)

References 17 publications

(15 reference statements)

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“…Each tree in the forest is trained on a random subset of the data with replacement (bootstrap aggregating or bagging), and the final prediction is made by averaging the predictions of each tree. This approach dramatically reduces overfitting and is one of the most potent and versatile ML models available [156].…”

Section: Random Forestmentioning

confidence: 99%

A Review of Artificial Intelligence Methods in Predicting Thermophysical Properties of Nanofluids for Heat Transfer Applications

Basu,

Saha,

Banerjee

et al. 2024

Energies

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This present review explores the application of artificial intelligence (AI) methods in analysing the prediction of thermophysical properties of nanofluids. Nanofluids, colloidal solutions comprising nanoparticles dispersed in various base fluids, have received significant attention for their enhanced thermal properties and broad application in industries ranging from electronics cooling to renewable energy systems. In particular, nanofluids’ complexity and non-linear behaviour necessitate advanced predictive models in heat transfer applications. The AI techniques, which include genetic algorithms (GAs) and machine learning (ML) methods, have emerged as powerful tools to address these challenges and offer novel alternatives to traditional mathematical and physical models. Artificial Neural Networks (ANNs) and other AI algorithms are highlighted for their capacity to process large datasets and identify intricate patterns, thereby proving effective in predicting nanofluid thermophysical properties (e.g., thermal conductivity and specific heat capacity). This review paper presents a comprehensive overview of various published studies devoted to the thermal behaviour of nanofluids, where AI methods (like ANNs, support vector regression (SVR), and genetic algorithms) are employed to enhance the accuracy of predictions of their thermophysical properties. The reviewed works conclusively demonstrate the superiority of AI models over the classical approaches, emphasizing the role of AI in advancing research for nanofluids used in heat transfer applications.

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Section: Random Forestmentioning

confidence: 99%

A Review of Artificial Intelligence Methods in Predicting Thermophysical Properties of Nanofluids for Heat Transfer Applications

Basu,

Saha,

Banerjee

et al. 2024

Energies

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Fake Profiles Identification on Social Networks With Bio Inspired Algorithm

Mahammed

Bennabi

Fahsi

et al. 2022

2022 First International Conference on Big Data, IoT, Web Intelligence and Applications (BIWA)

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Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Aly¹,

Hussein²,

Rahouma³

2022

Computers, Materials &Amp; Continua

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This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees' performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees' performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by "KF" with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.

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Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method

Cited by 3 publications

References 17 publications

A Review of Artificial Intelligence Methods in Predicting Thermophysical Properties of Nanofluids for Heat Transfer Applications

A Review of Artificial Intelligence Methods in Predicting Thermophysical Properties of Nanofluids for Heat Transfer Applications

Fake Profiles Identification on Social Networks With Bio Inspired Algorithm

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

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