Algorithm of model development and validation. by Bethan Cracknell Daniels (20469396)

Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

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

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.…”

Evaluation of model aggregation algorithms. by JiaMing Wang (334865)

“…To address these challenges, this paper proposes a federated learning-based intrusion detection algorithm (NIDS-FGPA) that utilizes gradient similarity model aggregation. …”

Parameters of the machine learning algorithms used in the study. by Xiaoyan Yang (259084)

Subjects:

Development of the CO₂ Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms by Hossein Mashhadimoslem (11456548)

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. …”

Data on the prediction performances of ML algorithms. by Yasemin Ayaz Atalan (21989402)

Comparison of homomorphic encryption algorithms. by JiaMing Wang (334865)

“…To address these challenges, this paper proposes a federated learning-based intrusion detection algorithm (NIDS-FGPA) that utilizes gradient similarity model aggregation. …”

Federated averaging algorithm. by Yan Qin (118276)

Hyperparameters of self-supervised learning algorithms used in Experiments 1–5. by Lalit Pandey (13195488)

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. …”

Performance of models by different algorithms. by Yong Zhang (5893)

Subjects:

Data Sheet 1_Development and validation of an endoscopic diagnostic model for sessile serrated lesions based on machine learning algorithms.docx by Xinying Yu (16400811)

“…Background and aims<p>Sessile serrated lesions (SSLs) are morphologically subtle and often misclassified as hyperplastic polyps (HPs), increasing colorectal cancer risks. We developed a machine learning (ML) model to improve endoscopic SSL diagnosis.…”

Evaluation metrics of the models constructed by each algorithm. by Xiaoyan Yang (259084)

Subjects:

Data_Sheet_1_Development of prognostic models for advanced multiple hepatocellular carcinoma based on Cox regression, deep learning and machine learning algorithms.CSV by Jie Shen (31533)

“…</p>Methods<p>Eligible patients with HCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and then prognostic models were built using Cox regression, machine learning (ML), and deep learning (DL) algorithms. The model’s performance was evaluated using C-index, receiver operating characteristic curve, Brier score and decision curve analysis, respectively, and the best model was interpreted using SHapley additive explanations (SHAP) interpretability technique.…”

Data_Sheet_2_Development of prognostic models for advanced multiple hepatocellular carcinoma based on Cox regression, deep learning and machine learning algorithms.docx by Jie Shen (31533)

“…</p>Methods<p>Eligible patients with HCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and then prognostic models were built using Cox regression, machine learning (ML), and deep learning (DL) algorithms. The model’s performance was evaluated using C-index, receiver operating characteristic curve, Brier score and decision curve analysis, respectively, and the best model was interpreted using SHapley additive explanations (SHAP) interpretability technique.…”

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

Machine learning prediction algorithm flow chart. by Ahmad M. Al-Omari (20460931)