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.…”

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

Subjects:

Top-rated mode of transport based on the SEU algorithm (SEU scores from 0 to 40). by Johannes Weyer (13392175)

Federated averaging algorithm. by Yan Qin (118276)

DMTD algorithm. by Yunhu Huang (21402795)

Subjects:

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

Performance comparison of mainstream algorithms. by Junjie Lu (160350)

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.…”

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.…”

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

Receiver operator characteristic (ROC) curves of the machine learning algorithms. by Yue Lv (549862)

Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

RF Algorithm Principles. by Zhiheng Zhang (9347282)

Subjects:

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. …”

Supplementary Material for: Deep learning-based algorithm for staging secondary caries in bitewings by vanNistelrooij N. (19962855)

“…Therefore, we aimed to develop a Convolutional neural network (CNN)-based algorithm for these purposes using a novel approach for determining lesion severity. …”

Flowchart of the RIR algorithm. by Jiamin Hu (8972420)

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Abbreviations used in the algorithm. by Richa Jain (6550391)

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”