Performance of machine learning algorithms. by Wenxue Liu (6877754)

Parameters of ML algorithms. by Md. Maeen Molla (20827041)

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

ML algorithms used in this study. by Toheeb Salahudeen (21368040)

“…Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. …”

A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

“…Future work aims to generalize this algorithm for broader biological applications by training additional Cellpose models and adapting the A* framework.…”

Data Sheet 1_Clinical validation of an artificial intelligence algorithm for classifying tuberculosis and pulmonary findings in chest radiographs.pdf by Thiago Fellipe Ortiz de Camargo (19460818)

“…Artificial Intelligence (AI) algorithms could be of great help, but using real-world data is crucial to ensure their effectiveness in diverse healthcare settings. …”

Comparative analysis of off-policy PPO algorithm and on-policy algorithm on the a) CSMAR and b) MorningStar datasets. by Cai Yuanqing (22513351)

AUC-ROC curves of LNG trade network link prediction via Six Machine Learning Algorithms. by Pei Zhao (487688)

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Proposed hybrid GA- ML algorithms. by Ankur Kumar (1357440)

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Proposed GA-ML algorithms pseudocode. by Ankur Kumar (1357440)

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Structure and the optimization technique of the algorithms adopted in the study. by M. A. Habib (16839161)

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Convergence curves for all algorithms. by Genliang Li (5816264)

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Parameter Settings for competitive algorithms. by Genliang Li (5816264)

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Variables tested in the ML algorithms. by Gilson Yuuji Shimizu (19837946)

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Performance analysis of 500kW solar PV plant based on machine learning algorithms by Karthikeyan Balachandran (20410999)

“…Kernel Ridge Regression (KRR) demonstrated superior performance among the algorithms tested, producing results closely aligned with the actual data. …”

Feature Importance Analysis of the Algorithms expressed by SHAP indices. by M. A. Habib (16839161)

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Statistical analysis of the prediction results from the ML algorithms. by M. A. Habib (16839161)

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Details of multiple GWO algorithm variants. by Genliang Li (5816264)

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Performance metrics (compared with DXA-based measurement) for different prediction algorithms using all predictors in training data. by Nick Birk (15444728)

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Supplementary file 1_The value of unsupervised machine learning algorithms based on CT and MRI for predicting sarcopenia.doc by Huayan Zuo (22308277)

“…Objectives<p>This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold partitioning, in predicting sarcopenia based on computed tomography (CT) and magnetic resonance imaging (MRI) data.…”