NSGA Ⅲ algorithm. حسب Yanlin Zhao (251552)

"…To validate the effectiveness of NSGA III, empirical data from a smart factory in Zhejiang, China, is utilized. …"

HIV testing algorithm KENPHIA 2018. حسب Raphael O. Ondondo (6429656)

Sensitivity Analysis of the “Swift Effect” Across Matching Algorithms. حسب James M. Smoliga (9074225)

الموضوعات:

Model comparison used. حسب Md. Fahim Ul Islam (22470251)

"…In this paper, we propose an Interpretable Network Bandwidth Slicing Identification (INBSI) system that leverages a modified Convolutional Neural Network (CNN) architecture with Nesterov-accelerated Adaptive Moment Estimation (NADAM) optimization. Additionally, we use a Variational Autoencoder (VAE) for preprocessing initial data, along with reconstructed data for data validity assessment. …"

Maple code for algorithm 3. حسب Peter Thompson (165468)

"…., ascertaining whether a model property can be determined from given data, is central to model-based data analysis in biomedicine. …"

Data Sheet 3_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip حسب Cong Peng (160287)

"…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …"

Data Sheet 2_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip حسب Cong Peng (160287)

"…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …"

Data Sheet 4_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip حسب Cong Peng (160287)

"…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …"

Data Sheet 6_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.docx حسب Cong Peng (160287)

"…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …"

Data Sheet 1_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.pdf حسب Cong Peng (160287)

"…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …"

Random Forest and Gradient Boosting algorithms. حسب Yvan J. Garcia-Lopez (20783591)

Latent heat products were generated using the Fully Connected Neural Network Heating (FCNH) algorithm حسب Hongxia Zhu (20296539)

Table 1_Predicting financial distress in TSX-listed firms using machine learning algorithms.docx حسب Mark Eshwar Lokanan (20344707)

"…Given the critical need for reliable financial health indicators, this research evaluates the predictive capabilities of various ML techniques on firm-level financial data.</p>Methods<p>The dataset comprises financial ratios and firm-specific variables from 464 firms listed on the TSX. …"

The workflow of Algorithm 2 with the example in حسب Badhan Das (7434362)

Data_Sheet_1_Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.pdf حسب Mikhail Ya Yadgarov (18543769)

The algorithm is designed to identify patterns in the treatment dataset. حسب Belén Otero-Carrasco (21262360)

"…It then iteratively extends these potential patterns by adding amino acids to previously identified patterns of maximum length. From lines 9 to 21, the algorithm uses the previously found maximum-length patterns to identify those that continue to meet the occurrence criteria and eliminates those that are subpatterns of newly extended patterns (lines 19-21).…"

Accuracy of cross-prediction classification (PR-AUC) results using two classification algorithms for the two T2D datasets by Qin et al. [ حسب Xinyu Han (2237545)

The overview of the ML algorithms’ flowchart. حسب Yasemin Ayaz Atalan (21989402)

"…This study aims to accurately estimate the renewable energy production rate to meet Türkiye’s electricity needs from renewable energy sources. For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. …"

Statistical results of various algorithms. حسب ZeSheng Lin (20501356)

"…Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. …"

TreeMap 2016 Stand Size Code Algorithm (Image Service) حسب U.S. Forest Service (17476914)

"…We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). …"