DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx by Yuhong Huang (115702)

“…We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …”

Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf by Muhammad Awais (263096)

“…To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”

DataSheet1_Large-Scale Heterogeneous Computing for 3D Deterministic Particle Transport on Tianhe-2A Supercomputer.zip by Biao Li (36684)

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…Importantly, this strategy locates samples adaptively on the transition between facies which improves the performance of conventional \emph{<i>MPS</i>} algorithms. In conclusion, this work shows that preferential sampling can contribute in \emph{<i>MPS</i>} even at very small sampling regimes and, as a corollary, demonstrates that prior models (obtained form a training image) can be used effectively not only to simulate non-sensed variables of the field, but to decide where to measure next.…”

DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx by Jun Zhang (48506)

“…Logistic regression emerged as the optimal machine learning algorithm for both DLR models. …”

DataSheet1_Towards Computational Modeling of Human Goal Recognition.pdf by Shify Treger (11973125)

DataSheet1_Towards Computational Modeling of Human Goal Recognition.pdf by Shify Treger (11973125)

Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png by Minjin Guo (22751300)

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Experimental Methodology Framework Local Processing Pipeline Architecture Data Flow: Storage I/O → Memory Buffer → CPU/GPU Processing → Cache Coherency → Storage I/O ├── Input Vector: mmap() system call for zero-copy file access ├── Processing Engine: OpenMP parallelization with NUMA-aware thread affinity ├── Memory Management: Custom allocator with hugepage backing └── Output Vector: Direct I/O bypassing kernel page cache Cloud Processing Pipeline Architecture Data Flow: Local Storage → Network Stack → TLS Tunnel → CDN Edge → Origin Server → Processing Grid → Response Pipeline ├── Upload Phase: TCP window scaling with congestion control algorithms ├── Network Layer: Application-layer protocol with adaptive bitrate streaming ├── Server-side Processing: Containerized microservices on Kubernetes orchestration ├── Load Balancing: Consistent hashing with geographic affinity routing └── Download Phase: HTTP/2 multiplexing with server push optimization Dataset Schema and Semantic Structure Primary Data Vectors Field Data Type Semantic Meaning Measurement Unit test_type Categorical Processing paradigm identifier {local_processing, cloud_processing} photo_count Integer Cardinality of input asset vector Count avg_file_size_mb Float64 Mean per-asset storage footprint Mebibytes (2^20 bytes) total_volume_gb Float64 Aggregate data corpus size Gigabytes (10^9 bytes) processing_time_sec Integer Wall-clock execution duration Seconds (SI base unit) cpu_usage_watts Float64 Thermal design power consumption Watts (Joules/second) ram_usage_mb Integer Peak resident set size Mebibytes network_upload_mb Float64 Egress bandwidth utilization Mebibytes energy_consumption_kwh Float64 Cumulative energy expenditure Kilowatt-hours co2_equivalent_g Float64 Carbon footprint estimation Grams CO₂e test_date ISO8601 Temporal execution marker RFC 3339 format hardware_config String Node topology identifier Alphanumeric encoding Statistical Distribution Characteristics The dataset exhibits non-parametric distribution patterns with significant heteroscedasticity across computational load vectors. …”

Search for acetylcholinesterase inhibitors by computerized screening of approved drug compounds by T.A. Materova (22770138)

“…The screening process employed the SOL docking program with MMFF94 force field and genetic algorithms for global optimization, targeting the human AChE structure (PDB ID: 6O4W). …”

Aluminum alloy industrial materials defect by Ying Han (20349093)

“…</p><h2>Description of the data and file structure</h2><p dir="ltr">This is a project based on the YOLOv8 enhanced algorithm for aluminum defect classification and detection tasks.…”

Code by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr">This architecture was implemented using the PyTorch library and trained using cross-entropy loss. The model was optimized to classify RNA sequences, achieving robust performance across multiple test sets.…”

Core data by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr">This architecture was implemented using the PyTorch library and trained using cross-entropy loss. The model was optimized to classify RNA sequences, achieving robust performance across multiple test sets.…”

Table 1_Advances in the application of human-machine collaboration in healthcare: insights from China.docx by Wuzhen Wang (20675405)

“…“Human–machine collaboration” is based on an intelligent algorithmic system that utilizes the complementary strengths of humans and machines for data exchange, task allocation, decision making and collaborative work to provide more decision support. …”

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

“…</li><li>The dataframe of extracted colour features from all leaf images and lab variables (ecophysiological predictors and variables to be predicted)</li><li>Set of scripts used for image pre-processing, features extraction, data analytsis, visualization and Machine learning algorithms training, using ImageJ, R and Python.…”