Genetic algorithm flowchart. by Wenguang Li (6528113)

“…The results show: (1) Random oversampling, ADASYN, SMOTE, and SMOTEENN were used for data balance processing, among which SMOTEENN showed better efficiency and effect in dealing with data imbalance. (2) The GA-XGBoost model optimized the hyperparameters of the XGBoost model through a genetic algorithm to improve the model’s predictive accuracy. …”

Confusion matrix of the proposed algorithm and state of the art (part 1). by Meshari Alazmi (452881)

Confusion matrix of the proposed algorithm and state of the art (part 2). by Meshari Alazmi (452881)

The algorithm of the applied models. by Elif Tuçe Bal (21367965)

“…It is equally important to select an appropriate valuation methodology. Today, one of the methods is Machine Learning (ML) algorithms stand out in generating better results than traditional methods. …”

Polylines of classification metrics of the Sort Difference Backward Elimination (SDBE) algorithm (Pre-SDBE). by Hu Ai (10689783)

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Comparison of average recognition accuracy of different SEI algorithms under different input features. by Dong Yi (511609)

Features selection using the Boruta algorithm. by Nishat Tasnim Thity (21755858)

“…We identified the important features related to IA using the Boruta algorithm. Predictions were made using different machine learning (ML) (decision tree (DT), random forest (RF), support vector machines (SVMs), and logistic regression (LR)) models. …”

Feature selection using Boruta algorithm. by Shayla Naznin (13014015)

“…Feature selection was performed using the Boruta algorithm and model performance was evaluated by comparing accuracy, precision, recall, F1 score, MCC, Cohen’s Kappa and AUROC.…”

Feature selection using the Boruta algorithm. by Guang Tu (22054865)

“…We extracted baseline characteristics, laboratory parameters, and clinical outcomes. The Boruta algorithm was employed for feature selection to identify variables significantly associated with in-hospital mortality, and 16 machine learning models, including logistic regression, random forest, gradient boosting, and neural networks, were developed and compared using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. …”

FRVT-MORPH performance of the D-MAD algorithm using MagFace embeddings. by Roman Kessler (2559916)

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The algorithm flow chart of BP neural network. by Limei Dong (9617966)

“…This paper considers introducing the granular-ball rough set algorithm for feature variable selection and combining it with the k-nearest neighbor method and back propagation network to analyze hydrological and water quality data, thus promoting overall and fused inspection. …”

The structure and workflow of a RF algorithm. by M. A. Habib (16839161)

“…The ML models are developed using data from the EurOtop (2018) database. Hyperparameter tuning is performed to curtail algorithms to the intrinsic features of the dataset. …”

The structure and workflow of a GBDT algorithm. by M. A. Habib (16839161)

“…The ML models are developed using data from the EurOtop (2018) database. Hyperparameter tuning is performed to curtail algorithms to the intrinsic features of the dataset. …”

By using the seven different machine learning algorithms on the data after missing value imputations, we get that the Support Vector Machine algorithm gives us the best result amon... by Debashis Chatterjee (1790896)

Flowchart of weighting using the AHP method. by Nada Ahmed Ezz-Eldien (17396553)

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Flowchart of weighting using the entropy method. by Nada Ahmed Ezz-Eldien (17396553)

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This figure shows a comparison between the feature values extracted from somatic current-clamp recordings from CA1 pyramidal neurons (see S7 Fig for examples) and the features resu... by Máté Mohácsi (20469514)

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Different algorithms’ performance across 8 cores. by Yongan Feng (7345193)

“…This enables SOSK-Means to identify clusters that are farther apart and denser, enhancing clustering accuracy. The selection of the best initial centers is performed using the mean square error criterion. …”

Different algorithms’ performance across 4 cores. by Yongan Feng (7345193)

“…This enables SOSK-Means to identify clusters that are farther apart and denser, enhancing clustering accuracy. The selection of the best initial centers is performed using the mean square error criterion. …”

Five-fold cross-validation results of CNN algorithm at different SNRs. by Jiuxiao Cao (21594403)