يعرض 161 - 168 نتائج من 168 نتيجة بحث عن '(( algorithm catenin function ) OR ((( algorithm python function ) OR ( algorithm etc function ))))', وقت الاستعلام: 0.25s تنقيح النتائج
  1. 161

    Landscape17 حسب Vlad Carare (22092515)

    منشور في 2025
    "…</p><p dir="ltr">We utilized TopSearch, an open-source Python package, to perform landscape exploration, at an estimated cost of 10<sup>5 </sup>CPUh. …"
  2. 162

    MCCN Case Study 2 - Spatial projection via modelled data حسب Donald Hobern (21435904)

    منشور في 2025
    "…This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the <a href="https://pypi.org/project/PyKrige/" rel="nofollow" target="_blank">pykrige</a> and <a href="https://pypi.org/project/rioxarray/" rel="nofollow" target="_blank">rioxarray</a> packages.…"
  3. 163

    Data Sheet 1_Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.pdf حسب Guangzong Li (16696443)

    منشور في 2025
    "…Eleven ML algorithms were trained and validated using Python, and external validation and performance evaluations were conducted. …"
  4. 164

    Pressure control techniques in freeze-drying حسب Geoff Smith (6064268)

    منشور في 2025
    "…The most common Pressure control techniques would be listed as follows:</p><ul><li>PID method</li><li>Fuzzy logic</li><li>Max pressure algorithms</li><li>Reinforcement learning</li><li>Adaptive control</li><li>Setpoint profile tracking (Bang-bang control)</li></ul><p dir="ltr">Pressure control systems have to perform a particular task in the target process considering some key functionalities like: system dynamism, control performance, stability, adaptability, accuracy, etc. …"
  5. 165

    a. How various statistical models account for modulation classification performance across the entire dataset. حسب Chris Scholes (3309477)

    منشور في 2025
    "…Parameters are <i>Type:</i> neuron classification (primary-like, sustained chopper, etc.); <i>CV:</i> Coefficient of variation of the interspike intervals in response to a pure tone at CF. …"
  6. 166

    <b>Drug Release Nanoparticle Systems Design:</b><b>Dataset Compilation and Machine Learning Modeling</b> حسب Shan He (14524901)

    منشور في 2024
    "…Herein 11 different AI/ML algorithms were used to develop the predictive AI/ML models. …"
  7. 167

    Data Sheet 1_CDK1 may promote breast cancer progression through AKT activation and immune modulation.docx حسب Huanhong Zeng (22570433)

    منشور في 2025
    "…This study aimed to comprehensively evaluate CDK1 expression, prognostic value, and biological functions in breast cancer through integrated bioinformatics and experimental analyses.…"
  8. 168

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

    منشور في 2025
    "…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). …"