Pseudocode for the missForestPredict algorithm. by Elena Albu (15181070)

“…The algorithm iteratively imputes variables using random forests until a convergence criterion, unified for continuous and categorical variables, is met. …”

Schematic diagram of random forest algorithm. by Imee V. Necesito (12749885)

The accuracy of all employed learning algorithms. by Quan Shao (20148209)

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

“…</p><p>Methods</p><p>Multiple machine learning (ML) algorithms were applied to data from the 2022 Bangladesh Demographic Health Survey, including Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, XGBoost, LightGBM and Neural Networks. …”

Types of machine learning algorithms. by Md. Merajul Islam (12646837)

“…<div><p>Background and objectives</p><p>Child undernutrition is a leading global health concern, especially in low and middle-income developing countries, including Bangladesh. Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.…”

Metrics of the complex network algorithm. by Xumei Pan (21464351)

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Classification accuracy of feature set 2 and feature set 3 in each algorithm. by Quan Shao (20148209)

Ranking of ML algorithms. by Yasemin Ayaz Atalan (21989402)

“…For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. …”

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. …”

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…The spectral coefficients are based on an orthogonal system of Legendre type smooth polynomials. We developed the mathematical theory to calculate spectral moment in Legendre polynomails space and use these moments to train traditional classifier like SVM and random forest for a classification task. …”

Pseudocode of the BBAC_I Algorithm. by Zezhi Lin (22521663)

Execution Steps of the EOF Algorithm. by Zezhi Lin (22521663)

The pseudocode of the RFE algorithm. by Zuzhen Ji (17593348)

Analysis of single features of vehicle movement. by Yaqin He (4627711)

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The overview of the ML algorithms’ flowchart. by Yasemin Ayaz Atalan (21989402)

“…For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. …”

Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

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Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

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Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

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Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

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Performance comparison for different algorithms reported in terms of bootstrapped canonical correlation of each component. by Shashwat Kumar (19120858)