Shapley value analysis results. by Guomei Cui (20721578)

“…However, the training data of this model comes from the simulation environment, which may deviate from the real game data. …”

Performance comparison of different models. by Guomei Cui (20721578)

“…However, the training data of this model comes from the simulation environment, which may deviate from the real game data. …”

S1 Dataset - by Guomei Cui (20721578)

“…However, the training data of this model comes from the simulation environment, which may deviate from the real game data. …”

Experimental environment. by Guomei Cui (20721578)

“…However, the training data of this model comes from the simulation environment, which may deviate from the real game data. …”

The range of hyperparameters. by Guomei Cui (20721578)

“…However, the training data of this model comes from the simulation environment, which may deviate from the real game data. …”

Dataset. by Jinshan Qi (5180714)

“…Meanwhile, we design two simple and tractable parameter estimation procedures based on cross-validation technique to speed up the model selection processes for DGRL and KDGRL. Finally, we conduct comprehensive experiments on diverse benchmark databases drawn from different areas to evaluate the proposed theories and algorithms. …”

Table 1_Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation.xlsx by Chuangan Zhou (21684530)

“…Machine learning models, including Backpropagation Neural Network (BPNN), Support Vector Machines (SVM), and particle swarm optimization algorithms (PSO-BPNN, PSO-SVR), were applied for regression analysis. …”

Case Distribution in ADNI and ROSMAP Cohorts. by Zilu Li (1477513)

“…We then developed an AD-GCN for both multi-omics and single-omics classification tasks and compared its performance with that of machine learning ensemble methods. …”

Summary of the ADNI participants. by Zilu Li (1477513)

“…We then developed an AD-GCN for both multi-omics and single-omics classification tasks and compared its performance with that of machine learning ensemble methods. …”

The Guardian Reading Dataset by Frans Van der Sluis (18365826)

“…Each participant evaluated 18 articles sampled at three levels of textual complexity (low, medium, high), determined by a readability algorithm (Van der Sluis, 2014). The data captures subjective appraisals of complexity, comprehensibility, and interest, alongside eye-tracking metrics to provide an objective view of readers' processing difficulty and engagement with the text.…”

Data Sheet 1_A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions.docx by Raman Kumar (6918212)

“…To systematically investigate this field, the literature is organized into five thematic categories such as “Heart Disease Detection and Diagnostics,” “Machine Learning Models and Algorithms for Healthcare,” “Feature Engineering and Optimization Techniques,” “Emerging Technologies in Healthcare,” and “Applications of AI Across Diseases and Conditions.” …”

Effect of different randomization constraints on pattern detection. by Zachary P. Neal (9946331)

Chemical Composition-Driven Machine Learning Models for Predicting Ionic Conductivity in Lithium-Containing Oxides (Supporting Information) by Yudai IWAMIZU (20747538)

“…<p>A machine learning model that can predict the ionic conductivity of lithium-containing oxides using chemical composition and ionic conductivity data was previously developed. …”

Data Sheet 1_Machine learning models predict coagulopathy in traumatic brain injury patients in ER.csv by Haoyu Wang (429641)

“…We developed a machine learning model to predict coagulopathy in TBI patients in the emergency room. …”

Data Sheet 1_Identification of signature genes and subtypes for heart failure diagnosis based on machine learning.xlsx by Yanlong Zhang (3159840)

“…</p>Methods<p>HF datasets were acquired from the Gene Expression Omnibus (GEO) database (GSE57338), and through the application of bioinformatics and machine-learning algorithms. We identified four candidate genes (FCN3, MNS1, SMOC2, and FREM1) that may serve as potential diagnostics for HF. …”

Collaborative research: CyberTraining: Implementation: Medium: Training users, developers, and instructors at the chemistry/physics/materials science interface by Francesco Paesani (5128004)

“…We achieve our aims by providing learners with various backgrounds exposure to state-of-the-art techniques and skills, showing them how to overcome the challenges of complexity by combining theories and algorithms or by unbiased learning of patterns. We will foster community-building among learners, developers, and instructors at different stages of their careers in multiple successive events designed to create a cohesive and sustainable environment where research and educational developments can grow beyond the project's duration. …”

Data Sheet 1_Bi-modal contrastive learning for crop classification using Sentinel-2 and Planetscope.pdf by Ankit Patnala (20386422)

“…However, the existing algorithms require a huge amount of annotated data. …”

Data Sheet 1_Deep learning-based beat-to-beat delineation of heart sounds and fiducial points in seismocardiography.pdf by Emil Korsgaard (22779983)

“…Therefore, the aim of this study was to develop an adaptive and data-driven algorithm for automatic delineation of 11 fiducial points in SCG.…”

Supplementary file 1_Personalized machine learning–based prognostic model for ICU-acquired bloodstream infections.docx by Shijun Zhou (11185840)

“…Background<p>Intensive care unit–acquired bloodstream infections (ICU-BSIs) are among the most prevalent healthcare-associated infections and a major cause of mortality among ICU patients. We developed a machine learning (ML)–based model to predict the prognosis of ICU-BSIs.…”

Supplementary file 1_Quantum natural language processing and its applications in bioinformatics: a comprehensive review of methodologies, concepts, and future directions.png by Gundala Pallavi (20741813)

“…However, this study also acknowledges the future of QNLP in bioinformatics in the discussion of the challenges and weaknesses of quantum hardware, data representation, encoding, and the construction and enhancement of the algorithms. …”