Optimized process of the random forest algorithm. by Hongxia Li (493545)

“…Finally, the constructed random forest-based gas explosion early warning model is compared with a classification model based on the support vector machine (SVM) algorithm. …”

Schematic diagram of Latin hypercube sampling. by Guilin Yang (583364)

Image_1_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

Image_5_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

Image_2_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

Image_3_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

Image_4_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

The flowchart of Algorithm 2. by Jing Xu (15337)

“…For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. …”

Parameter values of the compared algorithms. by Huimin Lu (4434682)

“…Finally, we proposed an escape Coati Optimization Algorithm (eCOA) for global optimization to enhance classification performance. …”

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”

Algorithm for generating hyperparameter. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Description of optimal model parameters. by Libo Liu (552605)

Results of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

ROC comparison of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Improved random forest algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

K-means++ clustering algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Sampling points during the 6th respiration. by Feng Gao (3548)

Image2_An evolution strategy of GAN for the generation of high impedance fault samples based on Reptile algorithm.TIF by Hao Bai (555291)

“…In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. …”

Image1_An evolution strategy of GAN for the generation of high impedance fault samples based on Reptile algorithm.TIF by Hao Bai (555291)

“…In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. …”

Image2_An evolution strategy of GAN for the generation of high impedance fault samples based on Reptile algorithm.TIF by Hao Bai (555291)

“…In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. …”