presentation1_ThermoScan: Semi-automatic Identification of Protein Stability Data From PubMed.pdf حسب Paola Turina (10431428)

"…The results show that ThermoScan returns accurate predictions and outperforms recently developed text-mining algorithms based on the analysis of publication abstracts.…"

PresQT - Services to Improve Re-use and FAIRness of Research Data and Software حسب Sandra Gesing (4501198)

"…PresQT services are easily integratable and target systems can be added via extending JSON files and Python functions. Data is packaged as BagITs for uploads, downloads and transfers. …"

DataSheet1_Global and non-Global slow oscillations differentiate in their depth profiles.docx حسب Sang-Cheol Seok (459143)

"…To test if the two SO types could be differentiated in their cortical-subcortical activity, we trained 30 machine learning classification algorithms to distinguish Global and non-Global SOs within each individual, and repeated this analysis for light (Stage 2, S2) and deep (slow wave sleep, SWS) NREM stages separately. …"

Skeletal_ Muscle_MRI_Registration حسب Lucia Fontana (9435020)

"…</p> <p>wxPython library was employed to develop the GUI, which is composed by two main windows – initial window and registration window – and 5 secondary frames for support functionalities. 3D images are presented with three views – axial, coronal and sagittal – with three sliders to adjust maximum value, minimum value, and gamma correction.…"

A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations on 7T T2-weighted images حسب Shuyu Li (18401358)

"…</p><p dir="ltr">The dataset is freely accessible on IEEE DataPort, a data repository created by IEEE and can be found at the following URL: <a href="https://ieeexplore.ieee.org/document/10218394/algorithms?tabFilter=dataset" target="_blank">https://ieeexplore.ieee.org/document/10218394/algorithms?…"

Data_Sheet_1_EEG phase synchronization during absence seizures.pdf حسب Pawel Glaba (11040603)

"…<p>Absence seizures—generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies. …"

Table 1_Explainable machine learning prediction of internet addiction among Chinese primary and middle school children and adolescents: a longitudinal study based on positive youth... حسب Jiahe Liu (9096353)

"…Our study aimed to examine the risk factors associated with IA among Chinese children and adolescents and leverage explainable machine learning (ML) algorithms to predict IA status at the time of assessment, based on Young’s Internet Addiction Test.…"

Table_1_Brief Sensory Training Narrows the Temporal Binding Window and Enhances Long-Term Multimodal Speech Perception.DOCX حسب Michael Zerr (7482335)

"…There are many complex algorithms our nervous system uses to construct a coherent perception. …"

Expression vs genomics for predicting dependencies حسب Broad DepMap (5514062)

"…If you are interested in trying machine learning, the files Features.hdf5 and Target.hdf5 contain the data munged in a convenient form for standard supervised machine learning algorithms.</p><p dir="ltr"><br></p><p dir="ltr">Some large files are in the binary format hdf5 for efficiency in space and read-in. …"

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). …"

Datasheet1_Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation.zip حسب Guillermo Jimenez-Perez (13239306)

"…Machine learning (ML) techniques based on deep learning algorithms have emerged as promising alternatives, capable of achieving similar performance without handcrafted features or thresholds. …"