Proposed methodology. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Loss vs. Epoch. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Sample images from the BreakHis dataset. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Accuracy vs. Epoch. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

S1 Dataset - by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

CSCO’s flowchart. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Image_1_Evaluation of the corneal topography based on deep learning.pdf by Shuai Xu (147659)

DataSheet1_High Resolution, Annual Maps of Field Boundaries for Smallholder-Dominated Croplands at National Scales.pdf by Lyndon D. Estes (12151059)

HMM of spike data and the problem of rapid state switching. by Tianshu Li (1527088)

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>…”

Fig 1 - by Matthew F. Wipperman (2095960)

“…The microcontroller unit (MCU) enables data acquisition, signal processing, and on-device data processing. …”

Validation of fitness tracker for sleep measures in women with asthma by Jessica Castner (3619997)

“…We linked data between devices for comparison both automatically by 24-hour period and manually by sleep segment. …”

Using artificial intelligence in the development of diagnostic models of coronary artery disease based on ECG features: A scoping review by Rong Yang (21661040)

“…</p><p dir="ltr">Extracted Fields:</p><table><tr><td><p dir="ltr">​​Category​​</p></td><td><p dir="ltr">​​Variables​​</p></td></tr><tr><td><p dir="ltr">Study Metadata​​</p></td><td><p dir="ltr">Authors, year, country, design, objectives</p></td></tr><tr><td><p dir="ltr">Population & Data Source​​</p></td><td><p dir="ltr">Cohort characteristics (age, sex), database (e.g., PTB-XL), sample size</p></td></tr><tr><td><p dir="ltr">AI Model Details​​</p></td><td><p dir="ltr">Algorithm (e.g., 1D-CNN, SVM), ECG features (e.g., QTc, spectral entropy)</p></td></tr><tr><td><p dir="ltr">Performance & Validation​​</p></td><td><p dir="ltr">Metrics (Acc, Sens, Spec), validation method (internal/external), generalizability</p></td></tr><tr><td><p dir="ltr">Critical Appraisal​​</p></td><td><p dir="ltr">Clinical relevance, limitations, implementation barriers</p></td></tr></table><p dir="ltr">7. …”

CDK6 271123 6Plex TMT.csv by Kathryn Fleming (21585680)

“…</p><p dir="ltr">The raw data files were processed and quantified using Proteome Discoverer software v2.4 (Thermo Scientific) and searched against the UniProt Mouse database (downloaded July 2021: 35859 entries) using the SEQUEST HT algorithm. …”