Texture-Based Face Recognition Using Grasshopper Optimization Algorithm and Deep Convolutional Neural Network

Veerashetty, Sachinkumar; Patil, Nagaraj B.

doi:10.1007/978-981-33-4909-4_4

Cited by 3 publications

(2 citation statements)

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“…1. The GO is based on hierarchical clustering, and CNN achieved a higher degree of accuracy in prediction as the other introduced methods in [25] which give more practical optimal solutions as shown in "Tab. 2".…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…In this paper, this average linkage clustering method was applied hierarchical clustering, and after that the features of principal component analysis were added to reduce the dimensionality and increase interpretability based on the mathematical formula of it which is introduced in [24,25].…”

Section: Data Preparationmentioning

confidence: 99%