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

Subjects:

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

Data Sheet 1_Development of a novel artificial intelligence algorithm for interpreting fetal heart rate and uterine activity data in cardiotocography.docx by Rohit Pardasani (22253656)

“…</p>Conclusion<p>This study demonstrates the successful development of a novel AI algorithm utilizing FHR and UA data to analyze and interpret fetal tracing events and parameters. …”

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)

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

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. …”

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

Federated averaging algorithm pseudocode. by Hadi Mohammadian KhalafAnsar (22783784)

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. …”

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. …”

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

Subjects:

Learning curves under various machine algorithms. by Jinlin Teng (17972595)

“…Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study introduces various machine learning methods and applies the Random Forest algorithm, which performed best, to investigate noise suitability in the central urban area of Nanchang City. …”

Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

Studies preferring ML algorithms for forecasting in renewable energy production. by Yasemin Ayaz Atalan (21989402)

<b>Discrimination of gold deposit types based on big data analysis and machine learning techniques</b> by Lihao Feng (20837036)

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. …”

Metrics obtained for different regression algorithms and target variables. by Silverio García-Cortés (4746477)

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)

“…Background<p>Most patients with multiple hepatocellular carcinoma (MHCC) are at advanced stage once diagnosed, so that clinical treatment and decision-making are quite tricky. The AJCC-TNM system cannot accurately determine prognosis, our study aimed to identify prognostic factors for MHCC and to develop a prognostic model to quantify the risk and survival probability of patients.…”

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)

“…Background<p>Most patients with multiple hepatocellular carcinoma (MHCC) are at advanced stage once diagnosed, so that clinical treatment and decision-making are quite tricky. The AJCC-TNM system cannot accurately determine prognosis, our study aimed to identify prognostic factors for MHCC and to develop a prognostic model to quantify the risk and survival probability of patients.…”