Using Stacking Ensemble Machine Learning to Estimate the Human Half-Life and Apparent Volume of Distribution: Implications for Human Health Risk Assessment حسب Bixuan Wang (19483723)

"…We employed five individual algorithms (Support Vector Regression, Random Forest, Gaussian Process, Artificial Neural Network, and Extreme Gradient Boosting) to construct the base models, and then combined predictions using Multiple Linear Regression to obtain 4 stacking models. …"

Knockdown of suppresses the migration, invasion, and promotes the apoptosis of EC cells. حسب Xuefen Lin (17785367)

Normalized convergence time. حسب Song Qian (5031221)

"…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …"

VGR structure. حسب Song Qian (5031221)

"…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …"

Comparison of normalized throughput and load. حسب Song Qian (5031221)

"…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …"

Principle of transfer learning. حسب Song Qian (5031221)

"…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …"

Body-connected routing scenario. حسب Song Qian (5031221)

"…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …"

Complete blood counts of a single patient حسب Daniel Walke (21680915)

Distributed and explainable GHSOM for anomaly detection in sensor networks حسب Paolo Mignone (9760832)

Machine learning & fairness: an integrated multicriteria approach for the evaluation of supervised classifiers حسب Jean-David Fermanian (12327868)

"…<p>Does <i>Multiple Criteria Decision Aiding</i> (MCDA) improve the process of evaluating <i>Machine Learning</i> (ML) algorithms, when critical criteria of <i>fairness</i> are concurrently considered, beyond predictive power? …"

<b>Multimodal MRI radiomics</b><b> based on </b><b>habitat subregions of the tumor microenvironment</b><b> for predicting risk stratification in glioblastoma</b> حسب Han Wang (21457334)

"…</p><p dir="ltr">A fully automated approach involving label fusion from multiple deep learning algorithms was used to segment distinct tumor subregions histologically. …"

Workflow summary: GRN motif, cell signaling model, and inference framework. حسب Michael Alexander Ramirez Sierra (20209354)

Table 1_Research trends and hotspots evolution of artificial intelligence for cholangiocarcinoma over the past 10 years: a bibliometric analysis.docx حسب Ke-xie Wang (20718248)

"…The disciplinary development pattern in this domain exhibits the characteristic of multiple disciplines intersecting and integrating.</p>Conclusion<p>The current research hotspots primarily revolve around three directions: AI in the diagnosis and classification of CCA, AI in the preoperative assessment of cancer metastasis risk in CCA, and AI in the prediction of postoperative recurrence in CCA. …"

DosePI: A Comprehensive Dataset for Peristaltic Pump Accuracy Enhancement in Pharmaceutical Environments حسب Davide Privitera (20720334)

"…The compensated dataset contains data from multiple compensation strategies. Each compensation sequence includes both uncompensated and compensated phases, with full records of model predictions and resulting dispensed volumes. …"

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) حسب Erola Fenollosa (20977421)

"…The described extracted features were used to predict leaf betalain content (µg per FW) using multiple machine learning regression algorithms (Linear regression, Ridge regression, Gradient boosting, Decision tree, Random forest and Support vector machine) using the <i>Scikit-learn</i> 1.2.1 library in Python (v.3.10.1) (list of hyperparameters used is given in <a href="#sup1" target="_blank">Supplementary Data S5</a>). …"