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    <responseDate>2026-10-12T14:31:46Z</responseDate>
    <request verb="GetRecord" identifier="oai:data.mendeley.com/ys4t55m57x.1" metadataPrefix="oai_dc">https://data.mendeley.com/oai</request>
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        <record>
            <header>
                <identifier>oai:data.mendeley.com/ys4t55m57x.1</identifier>
                <datestamp>2025-06-10T14:35:04Z</datestamp>
            </header>
            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>Al-Dulaimi, Ahmed</dc:creator>
    <dc:title>Synthetic and Realistic Laser–Tissue Interaction Data for Machine-Learning Applications</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This dataset provides two complementary CSV files containing simulated laser–tissue interaction measurements, designed to support machine-learning research in biomedical optics and computational modeling:

1- **laser_tissue_data.csv**

- Samples: 1,000,000

Features:

- wavelength (nm)
- pulse_duration (ns)
- energy_density (J/cm²)
- absorption_coeff (cm⁻¹)
- thermal_conductivity (W/(m·K))
- fluence (J/cm²/ns) – derived
- beam_profile – simulated optical absorption
-- success (0/1) – binary target indicating whether the laser pulse meets thermal diffusion and energy thresholds

These data were generated via uniform, log-normal, and normal distributions to represent idealized measurements of laser–tissue interactions under controlled conditions.


2- **realistic_laser_tissue_data.csv**

- Samples: 1,000,000

Same feature set as above, with two added realism factors:

- Measurement noise (configurable noise_level = 0.1) applied to each physical variable
- Label noise (flip_prob = 0.05) randomly inverting 5% of the success outcomes

This version simulates sensor variance and occasional mislabeling, making it suitable for testing robustness of classification and regression models.

-------------------------------------------------

Use Cases &amp; Applications

- Train and benchmark classification models (e.g., logistic regression, random forests, neural networks) on binary outcome prediction.
- Explore feature-engineering strategies (e.g., interaction terms, normalization) for optical parameters.
- Evaluate the impact of measurement and label noise on model performance and generalizability.
- Develop synthetic-data augmentation pipelines for scarce or ethically constrained biomedical datasets.</dc:description>
    <dc:subject>Optics</dc:subject>
    <dc:subject>Machine Learning</dc:subject>
    <dc:subject>Laser</dc:subject>
    <dc:subject>Classification System</dc:subject>
    <dc:subject>Regression Model</dc:subject>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/ys4t55m57x.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/ys4t55m57x.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/ys4t55m57x</dc:relation>
    <dc:date>2025-06-10T14:35:04Z</dc:date>
</oai_dc:dc></metadata>
        </record>
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