Classification performance results using 5-fold cross-validation for the training datasets after applying the data imbalance tool, SMOTE and NQE for data embedding. The values with... حسب Aninda Astuti (21736334)

Binary contingency table. حسب Lu Xiao (314030)

The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC12. حسب Aninda Astuti (21736334)

The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC11. حسب Aninda Astuti (21736334)

The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC10. حسب Aninda Astuti (21736334)

Comparison of performance metrics, SVC vs QSVC after using NQE for data embedding. Bold-faced fonts denote that the classifier achieves better performance. حسب Aninda Astuti (21736334)

Comparison of NQE combined with SVC, NN, and QCNN classifiers. The table shows the number of test cases, comparison with classical classifiers, and data imbalance issues in this an... حسب Aninda Astuti (21736334)

Histograms of various water quality parameters, including pH, Hardness, Solids, Chloramines, Sulfate, Organic Carbon, Trihalomethanes, Turbidity, and a binary portability check. حسب Amal H. Alharbi (21755906)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results حسب Se-Hee Jo (20554623)

"…A Python-based algorithm was developed for estimating the nonrandom two-liquid (NRTL) model parameters of aqueous binary systems in a straightforward manner from simplified molecular-input line-entry specification (SMILES) strings of substances in a system. …"

Homomorphic binary tree. حسب Jianhua He (341366)

Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. حسب Jiaqing Luo (10975030)

"…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …"

Flowchart for classification tasks in automated extinction risk assessment method, using the XGBoost algorithm [29]. حسب Gabriel Henrique de Oliveira Caetano (12647413)

The workflow diagram for the gene expression biomarker study which involve using principal component analysis (PCA) to reduce the number of features (principal components), thus re... حسب Aninda Astuti (21736334)

Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke حسب Chulho Kim (622686)

"…<div><p>Background and purpose</p><p>This project assessed performance of natural language processing (NLP) and machine learning (ML) algorithms for classification of brain MRI radiology reports into acute ischemic stroke (AIS) and non-AIS phenotypes.…"

Algorithm for generating hyperparameter. حسب Balraj Preet Kaur (20370832)

"…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"

Characteristics of the data sets used to evaluate Machine Learning and Deep Learning models for arboviral diseases classification. حسب Sebastião Rogério da Silva Neto (11944944)

Results of machine learning algorithm. حسب Balraj Preet Kaur (20370832)

"…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"

Attributes found in the data sets. حسب Sebastião Rogério da Silva Neto (11944944)

Summary of all demographic, epidemiological and clinical data presented in data set used by the primary studies. حسب Sebastião Rogério da Silva Neto (11944944)

ROC comparison of machine learning algorithm. حسب Balraj Preet Kaur (20370832)

"…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"