Weights of sample data. حسب Yixuan Lu (9987980)

Sample data of K-means clustering. حسب Yixuan Lu (9987980)

Data_Sheet_1_Determination of phage load and administration time in simulated occurrences of antibacterial treatments.docx حسب Steffen Plunder (14022498)

ECE6379_PSOM.zip حسب 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_Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma.PDF حسب Chaoyue Chen (7298480)

A portfolio selection model based on the knapsack problem under uncertainty حسب Fereshteh Vaezi (6655028)

"…The resulted model is converted into a parametric linear programming model in which the decision maker is able to determine the optimism threshold. …"

Table 1_Optimization of diagnosis-related groups for patients with acute appendicitis using a machine learning model.docx حسب Xinlong Gu (22163251)

Data_Sheet_1_Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury.doc حسب Jijun Yang (5119103)

Data_Sheet_1_Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling.PDF حسب Nguyen Thanh Nhu (16400859)

Data_Sheet_1_Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app.PDF حسب Jevitha Lobo (14368401)

Data_Sheet_1_Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app.PDF حسب Jevitha Lobo (14368401)

Big Data Model Building Using Dimension Reduction and Sample Selection حسب Lih-Yuan Deng (17081779)

"…To achieve this goal, the selection of training subdata becomes pivotal in retaining essential characteristics of the full data. Recently, several procedures have been proposed to select “optimal design points” as training subdata under pre-specified models, such as linear regression and logistic regression. …"

Table_1_Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling.DOCX حسب Nguyen Thanh Nhu (16400859)

Data Sheet 1_Triglyceride-glucose index and mortality in congestive heart failure with diabetes: a machine learning predictive model.doc حسب Lin Yu (221619)

"…</p>Methods<p>We obtained clinical data for patients with CHF and DM from the MIMIC-IV (3.1) database. …"

Data Sheet 1_Association between admission Braden Skin Score and delirium in surgical intensive care patients: an analysis of the MIMIC-IV database.docx حسب Meiling Shang (21086624)

"…The primary outcome was incidence of delirium. Feature importance of BSS was initially assessed using a machine learning algorithm, while restricted cubic spline (RCS) models and multivariable logistic analysis evaluated the relationship between BSS and delirium. …"

DataSheet_1_A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn... حسب Gaosen Zhang (539619)

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows حسب 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. …"