Pre-processing of raw images for pose estimation using physical model-trained convolutional neural network model. by Aniket Ravan (3174171)

Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)

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

“…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. …”

Data_Sheet_1_Performance of TB-LAMP in the Diagnosis of Tuberculous Empyema Using Samples Obtained From Pleural Decortication.docx by Chang Liu (35901)

Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP by Xiaoyuan Wang (492534)

Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx by Chenglin Wang (430151)

“…The R-LBP algorithm, an extension of the LBP algorithm, enhances the texture features of citrus fruits at distinct ripeness stages by calculating the coefficient of variation in grayscale values of pixels within a certain range in different directions around the target pixel. …”

Data Sheet 1_Artificial intelligence-assisted capsule endoscopy for detecting lesions in Crohn’s disease: a systematic review and meta-analysis.docx by Yuling Bin (20970842)

“…Meta-regression analysis further revealed that AI algorithm type, study population size, and study design might be key sources of heterogeneity.…”

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. …”

PGAE-ICA_A simplified digital system for intellectual measurement-assessment in children and adolescents using cognitive testing and machine learning techniques by Runzhou Wang (5894849)

“…A genetic algorithm-optimized extreme learning machine model was then constructed and trained to predict intellectual status of children and adolescents. …”

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). …”