Fitness function parameters. by Ankita Sharma (3183054)

Flowchart of the clinical decision support system and the interpretable tropical geometry-based fuzzy neural network algorithm. by Yufeng Zhang (195768)

Prediction algorithm. by Muhammad Irfan (255476)

“…Finally, it implements the ELPF using an improved method Residual Network (ResNet-152) and the machine-improved Support Vector Machine (SVM) based forecasting engine to forecast the ELP accurately. …”

Optimization Performance Comparison of Metaheuristic Algorithms. by Xie Qiu (13243770)

Details of 23 basic benchmark functions. by Ying Li (38224)

(a): These systems were simulated for (0,3] and (0,3] without the prior knowledge about different phases, and the probability density function of points in feature space illustrate... by Ali Talebi (7164203)

Details of the comparison algorithm parameters. by Yan Wei (32168)

“…Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. …”

Table 1_Machine learning-based gait adaptation dysfunction identification using CMill-based gait data.docx by Hang Yang (577653)

“…Features were extracted from demographics, gait kinematics, and gait adaptability datasets. ML models based on Support Vector Machine, Decision Tree, Multi-layer Perceptron, K-Nearest Neighbors, and AdaCost algorithm were trained to classify individuals with and without GAD. …”

Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches by Andrés Halabi Diaz (20798460)

“…This study integrates conceptual density functional theory (CDFT) descriptors with explainable no-code machine learning (ML) models to predict NA mutagenicity based on Ames test results. …”

Functional analysis of anoikis-related genes. by Yufeng He (5673119)

Subjects:

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

Subjects:

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

Subjects:

Comparison of classification accuracy across different machine learning models using the objective function metric. by Amal H. Alharbi (21755906)

Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set? by Minyi Su (1425976)

Subjects:

Presentation1_Battery SOH estimation based on decision tree and improved support vector machine regression algorithm.pdf by Lijun Qian (2364427)

“…Five groups of different feature inputs are constructed based on the cumulative feature importance, and the original support vector machine regression (SVR) algorithm is applied to perform SOH estimation simulation experiments on each group. …”

Data Sheet 2_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.zip by Rui Zhang (13940)

“…The model for potential pregnancy loss was also developed using five machine learning algorithms and was based on 7 indicators. …”

Data Sheet 1_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.docx by Rui Zhang (13940)

“…The model for potential pregnancy loss was also developed using five machine learning algorithms and was based on 7 indicators. …”

Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses by Stephan Ellmann (314565)

Subjects:

Library of the 55 different classification and regression machine-learning algorithms used by the ensemble predictor SuperLearner (SL.library) in the CBDA 2.0 implementation. by Simeone Marino (81051)

Subjects:

Table_1_Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms.xlsx by Yiding Yu (701906)

“…We used three machine learning algorithms: LASSO, RF and SVM-RFE to further screen the aging-related genes of heart failure, and fitted and verified them through a variety of machine learning algorithms. …”