Performance Comparison of State-of-the-Art WPT-MEC Offloading Methods. by Hend Bayoumi (22693738)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

S1 Code - by Gaosha Li (20570760)

Data_Sheet_2_Computational Neural Modeling of Auditory Cortical Receptive Fields.pdf by Jordan D. Chambers (6732224)

Data_Sheet_1_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

Data_Sheet_3_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

Classification baseline performance. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

Feature selection results. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

ANOVA test result. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

Summary of literature review. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

Table_1_Screening of Long Non-coding RNAs Biomarkers for the Diagnosis of Tuberculosis and Preliminary Construction of a Clinical Diagnosis Model.docx by Juli Chen (12187358)

“…Background<p>Pathogenic testing for tuberculosis (TB) is not yet sufficient for early and differential clinical diagnosis; thus, we investigated the potential of screening long non-coding RNAs (lncRNAs) from human hosts and using machine learning (ML) algorithms combined with electronic health record (EHR) metrics to construct a diagnostic model.…”

Malware detection workflow in compressed archives by Paul A. Gagniuc (1818325)

Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

Input features. by Nicola Lazzarini (3528197)

Baseline characteristics of patients in the study cohort. by Nicola Lazzarini (3528197)

ECE6379_PSOM.zip by Xingpeng Li (11825663)

“…Optimization algorithms that are commonly used to solve these problems will also be covered including linear programming, mixed-integer linear programming, Lagrange relaxation, dynamic programming, branch and bound, and duality theory.…”

Data_Sheet_1_Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.PDF by Michael Moor (10887888)

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>…”

Table_1_Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.pdf by Michael Moor (10887888)

CSPP instance by 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).…”