Presentation_1_Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction.pdf حسب Yongcheng Lin (776525)

"…In this study, we utilized a sea ice concentration dataset obtained from satellite remote sensing and applied the k-nearest-neighbors (Ice-kNN) machine learning model to forecast the summer Arctic sea ice concentration and extent on 122 days prediction. Based on the physical characteristics of summer sea ice, different algorithms are employed to optimize the prediction model. …"

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. حسب Enrico Bertozzi (22461709)

"…Multiple SVM models were trained and evaluated, including configurations with linear and RBF (Radial Basis Function) kernels. </p><p dir="ltr">Additionally, an exhaustive hyperparameter search was performed using GridSearchCV to optimize the C, gamma, and kernel parameters (testing 'linear,' 'rbf,' 'poly,' and 'sigmoid'), aiming to find the highest-performing configuration. …"

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

"…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…"

Data Sheet 1_A novel method for power transformer fault diagnosis considering imbalanced data samples.docx حسب Jun Chen (4238)

"…</p>Methods<p>The methodology begins by introducing a correction factor into the objective function of the NCA algorithm to reduce the impact of sample imbalance on model training. …"

METAHEURISTICS EVALUATION: A PROPOSAL FOR A MULTICRITERIA METHODOLOGY حسب Valdir Agustinho de Melo (12735562)

"…<div><p>ABSTRACT In this work we propose a multicriteria evaluation scheme for heuristic algorithms based on the classic Condorcet ranking technique. …"

Data_Sheet_1_Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster.PDF حسب Gianmarco Tiddia (10824118)

"…NEST GPU is a GPU library written in CUDA-C/C++ for large-scale simulations of spiking neural networks, which was recently extended with a novel algorithm for remote spike communication through MPI on a GPU cluster. …"

Search for acetylcholinesterase inhibitors by computerized screening of approved drug compounds حسب 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). …"

Table_3_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx حسب Qian Wang (32718)

"…Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. …"

Image_1_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.jpeg حسب Qian Wang (32718)

"…Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. …"

Image_2_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.jpeg حسب Qian Wang (32718)

"…Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. …"

DataSheet_1_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.docx حسب Qian Wang (32718)

"…Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. …"

Image_3_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.jpeg حسب Qian Wang (32718)

"…Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. …"

Table_4_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx حسب Qian Wang (32718)

"…Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. …"

Table_2_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx حسب Qian Wang (32718)

"…Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. …"

Table_1_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx حسب Qian Wang (32718)

"…Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. …"

Code حسب Baoqiang Chen (21099509)

"…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

Core data حسب Baoqiang Chen (21099509)

"…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

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

"…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …"

R‑BIND: An Interactive Database for Exploring and Developing RNA-Targeted Chemical Probes حسب Brittany S. Morgan (7554242)

"…Further, we developed several user-friendly tools, including queries based on cheminformatic parameters, experimental details, functional groups, and substructures. …"

R‑BIND: An Interactive Database for Exploring and Developing RNA-Targeted Chemical Probes حسب Brittany S. Morgan (7554242)

"…Further, we developed several user-friendly tools, including queries based on cheminformatic parameters, experimental details, functional groups, and substructures. …"