Proposed model tuned hyperparameters. by Subathra Gunasekaran (19492680)

“…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”

The workflow of the proposed model. by Subathra Gunasekaran (19492680)

“…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”

ResNeXt101 training and results. by Subathra Gunasekaran (19492680)

“…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”

Proposed model specificity and DSC outcomes. by Subathra Gunasekaran (19492680)

“…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”

Accuracy comparison of proposed and other models. by Subathra Gunasekaran (19492680)

“…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”

Architecture of ConvNet. by Subathra Gunasekaran (19492680)

“…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”

Comparison of state-of-the-art method. by Subathra Gunasekaran (19492680)

“…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”

Proposed model sensitivity outcome. by Subathra Gunasekaran (19492680)

“…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”

Proposed ResNeXt101 operational flow. by Subathra Gunasekaran (19492680)

“…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”

All online review text data. by Yuandi Jiang (16540833)

“…The findings showed: 1) Museum visitors were highly concentrated in eastern coastal regions, with spatial distribution evolving from single-core to multi-core clusters, gradually expanding into central areas (e.g., Henan, Hubei, Shaanxi). 2) Museum image perception has shifted from object-centered to more human-centered experiences, with significant differences across the various categories. 3) Over 75% of visitors reported positive experiences, with ethnography museums showing the highest satisfaction in 2024 (<i>Pro</i> = 0.922), whereas history museums consistently had the lowest. 4) Satisfaction drivers were dynamic, with 85.26% of perception themes significantly correlated with satisfaction (<i>p</i> < 0.01), with rich collections, distinctive features, immersive experiences, and diverse visitation forms identified as the primary contributors to positive visitor experiences. …”

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

Data_Sheet_1_Global research of artificial intelligence in strabismus: a bibliometric analysis.docx by Ziying Zhou (14027464)

CIAHS-Data.xls by Yingchang Li (22195585)

“…This method identifies inherent natural grouping points within the data through the Jenks optimization algorithm, maximizing between-class differences while minimizing within-class differences37. …”

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. …”

Image 1_Development of machine learning predictive model for type 2 diabetic retinopathy using the triglyceride-glucose index explained by SHAP method.png by Xiaoqin Liu (296429)

“…Introduction<p>This study aimed to develop a diabetic retinopathy (DR) Prediction model using various machine learning algorithms incorporating the novel predictor Triglyceride-glucose index (TyG). …”

Image 2_Development of machine learning predictive model for type 2 diabetic retinopathy using the triglyceride-glucose index explained by SHAP method.png by Xiaoqin Liu (296429)

“…Introduction<p>This study aimed to develop a diabetic retinopathy (DR) Prediction model using various machine learning algorithms incorporating the novel predictor Triglyceride-glucose index (TyG). …”

2000–2020 Monthly Air Quality Index (AQI) Dataset of China by Chaohao Ling (19840471)

“…Four tree-based ensemble algorithms (Random Forest [RF], Gradient Boosting Machine [GBM], CatBoost, XGBoost) were compared, with the RF model selected as optimal (test set: R² = 0.83, Root Mean Square Error [RMSE] = 10.25, Mean Absolute Error [MAE] = 9.03) after validation via 10-fold geographic stratified cross-validation and 100 bootstrap iterations; Recursive Feature Elimination (RFE) further refined 14 core predictors to minimize overfitting. …”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…The dataset also enables parameter space mapping, allowing the generation of 2D/3D response surfaces showing toxicity trends across varying core sizes and dosages.</p><p dir="ltr">This curated dataset addresses several limitations of existing toxicological datasets by enhancing feature diversity, standardization, and data quality control. …”