Proposed IDS architecture. حسب Saad Hammood Mohammed (20623506)

Table_1_Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units.XLSX حسب Qin-Yu Zhao (10014626)

Table_2_Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units.DOCX حسب Qin-Yu Zhao (10014626)

Table_1_Construction of a risk screening and visualization system for pulmonary nodule in physical examination population based on feature self-recognition machine learning model.X... حسب Fang Tian (360682)

All online review text data. حسب Yuandi Jiang (16540833)

"…Kernel density and standard deviational ellipse methods revealed the spatio-temporal evolution of museum space preferences (2016–2024). TF-IDF and LDA algorithms identified key image perception themes. Visitor satisfaction was then evaluated with SnowNLP sentiment analysis to examine the dynamic correlation between the perception themes and satisfaction. …"

Table 1_Explainable machine learning-based prediction of early and mid-term postoperative complications in adolescent tibial fractures.docx حسب Yufeng Wang (274657)

"…</p>Results<p>Baseline characteristics were balanced between training and test sets (P > 0.05). The IHSO-optimized algorithm outperformed controls in 91.67% of CEC2022 benchmark functions. …"

DataSheet_1_Stronger wind, smaller tree: Testing tree growth plasticity through a modeling approach.docx حسب Haoyu Wang (429641)

"…The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to maximize the multi-objective function (stem biomass and tree height) by determining the key parameter value controlling the biomass allocation to the secondary growth. …"

Algorithms and evaluation metrics integrated in the COPS framework. حسب Teemu J. Rintala (19333003)

Table_1_Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.docx حسب Liyin Zhang (6371999)

Minimal Dateset. حسب Hongwei Yue (574068)

"…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …"

Loss Function Comparison. حسب Hongwei Yue (574068)

"…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …"

Comparative Results of Different Models. حسب Hongwei Yue (574068)

"…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …"

Loss Function Comparison. حسب Hongwei Yue (574068)

"…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …"

Overall Framework of the PSO-KM Model. حسب Hongwei Yue (574068)

"…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …"

Overall Framework of the PSO-KM Model. حسب Hongwei Yue (574068)

"…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …"

DATASET AI حسب Elena Stamate (18836305)

"…</li></ul><p dir="ltr">The dataset includes model-ready variables suitable for classification tasks and has been used to train and evaluate algorithms such as Extra Trees, Support Vector Machines (SVM), Random Forest, and Quadratic Discriminant Analysis (QDA). …"

CSPP instance حسب peixiang wang (19499344)

"…</b></p><p dir="ltr">Its primary function is to create structured datasets that simulate container terminal operations, which can then be used for developing, testing, and benchmarking optimization algorithms (e.g., for yard stacking strategies, vessel stowage planning).…"

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles حسب Soham Savarkar (21811825)

"…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…"

Supplementary file 1_A study on a real-world data-based VTE risk prediction model for lymphoma patients.docx حسب Changli He (22424818)

"…Model development incorporated three imputation methods, three sampling strategies, three feature selection approaches, and nine machine learning algorithms. …"

Table_4_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX حسب Hui Tang (226667)

"…Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for sample clustering. …"