The parameter settings of the RF algorithm after parameter optimization. by Xini Fang (20861990)

Parameter optimization results of the RF model improved by FCM clustering. by Xini Fang (20861990)

Zimbabwe model outputs on actual and optimized 2016 spending and impact by intervention. by Nicole Fraser-Hurt (2636833)

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Armenia model outputs on optimized resource allocation and health impact by EUHC care package (2019 vs. higher budget scenario). by Nicole Fraser-Hurt (2636833)

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Principle of the SVM algorithm. by Anfeng Zhu (17817144)

Côte d’Ivoire model outputs on actual and optimized 2016 spending and impact by care delivery platform. by Nicole Fraser-Hurt (2636833)

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Key data on the three country case studies. by Nicole Fraser-Hurt (2636833)

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Overview of the five stages of the HIPtool application. by Nicole Fraser-Hurt (2636833)

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OneDataShare: Data Transfer Optimization as a Service - Talk by Tevfik Kosar (8399697)

OneDataShare: Data Transfer Optimization as a Service - Poster by Tevfik Kosar (8399697)

The robustness test results of the model. by Xini Fang (20861990)

“…Finally, an improved RF model is constructed by optimizing the parameters of the RF algorithm. The data selected is mainly from RESSET/DB, covering the issuance, trading, and rating data of fixed-income products such as bonds, government bonds, and corporate bonds, and provides basic information, net value, position, and performance data of funds. …”

Construction process of RF. by Xini Fang (20861990)

“…Finally, an improved RF model is constructed by optimizing the parameters of the RF algorithm. The data selected is mainly from RESSET/DB, covering the issuance, trading, and rating data of fixed-income products such as bonds, government bonds, and corporate bonds, and provides basic information, net value, position, and performance data of funds. …”

Data_Sheet_1_Prediction of patient choice tendency in medical decision-making based on machine learning algorithm.pdf by Yuwen Lyu (14330781)

“…Objective<p>Machine learning (ML) algorithms, as an early branch of artificial intelligence technology, can effectively simulate human behavior by training on data from the training set. …”

IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems by Xuhui Lin (19505503)

“…</p><p dir="ltr"><b>Quality Assurance</b>: Comprehensive technical validation demonstrates the dataset's integrity, sensitivity to rainfall impacts, and capability to reveal complex traffic-weather interaction patterns.</p><h2>Data Structure</h2><p dir="ltr">The dataset is organized into four primary components:</p><ol><li><b>Road Network Data</b>: Topological representations including spatial geometry, functional classification, and connectivity information</li><li><b>Traffic Sensor Data</b>: Sensor metadata, locations, and measurements at both 5-minute and hourly resolutions</li><li><b>Precipitation Data</b>: Hourly meteorological information with spatial grid cell metadata</li><li><b>Derived Analytical Matrices</b>: Pre-computed structures for advanced spatial-temporal modelling and network analyses</li></ol><h2>File Formats</h2><ul><li><b>Tabular Data</b>: Apache Parquet format for optimal compression and fast query performance</li><li><b>Numerical Matrices</b>: NumPy NPZ format for efficient scientific computing</li><li><b>Total Size</b>: Approximately 2 GB uncompressed</li></ul><h2>Applications</h2><p dir="ltr">The IUTF dataset enables diverse analytical applications including:</p><ul><li><b>Traffic Flow Prediction</b>: Developing weather-aware traffic forecasting models</li><li><b>Infrastructure Planning</b>: Identifying vulnerable network components and prioritizing investments</li><li><b>Resilience Assessment</b>: Quantifying system recovery curves, robustness metrics, and adaptive capacity</li><li><b>Climate Adaptation</b>: Supporting evidence-based transportation planning under changing precipitation patterns</li><li><b>Emergency Management</b>: Improving response strategies for weather-related traffic disruptions</li></ul><h2>Methodology</h2><p dir="ltr">The dataset creation involved three main stages:</p><ol><li><b>Data Collection</b>: Sourcing traffic data from UTD19, road networks from OpenStreetMap, and precipitation data from ERA5 reanalysis</li><li><b>Spatio-Temporal Harmonization</b>: Comprehensive integration using novel algorithms for spatial alignment and temporal synchronization</li><li><b>Quality Assurance</b>: Rigorous validation and technical verification across all cities and data components</li></ol><h2>Code Availability</h2><p dir="ltr">Processing code is available at: https://github.com/viviRG2024/IUTDF_processing</p>…”

Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics by Zhichao Liu (191718)

“…However, extraction of trace-abundance signals from complex data sets (<i>m</i>/<i>z</i> value, separation time, signal abundance) that result from ultrasensitive studies requires improved data processing algorithms. …”

Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics by Zhichao Liu (191718)

“…However, extraction of trace-abundance signals from complex data sets (<i>m</i>/<i>z</i> value, separation time, signal abundance) that result from ultrasensitive studies requires improved data processing algorithms. …”

ECE6379_PSOM.zip by Xingpeng Li (11825663)

Data used to drive the Double Layer Carbon Model in the Qinling Mountains. by Huiwen Li (17705280)

“…It relies on comprehensive input data, including initial SOC stocks, climate data, and vegetation production to drive these simulations.…”

Data_Sheet_3_Development of the Interdisciplinary and Interprofessional Course Concept “Advanced Critical Illness Life Support”.PDF by Mark Michael (11696493)

Data_Sheet_2_Development of the Interdisciplinary and Interprofessional Course Concept “Advanced Critical Illness Life Support”.PDF by Mark Michael (11696493)