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testing algorithm » twisting algorithm (Expand Search), hastings algorithm (Expand Search), boosting algorithm (Expand Search)
method algorithm » network algorithm (Expand Search), means algorithm (Expand Search), mean algorithm (Expand Search)
using algorithm » using algorithms (Expand Search), routing algorithm (Expand Search), fusion algorithm (Expand Search)
based testing » based teaching (Expand Search), care testing (Expand Search), acid testing (Expand Search)
element » elements (Expand Search)
testing algorithm » twisting algorithm (Expand Search), hastings algorithm (Expand Search), boosting algorithm (Expand Search)
method algorithm » network algorithm (Expand Search), means algorithm (Expand Search), mean algorithm (Expand Search)
using algorithm » using algorithms (Expand Search), routing algorithm (Expand Search), fusion algorithm (Expand Search)
based testing » based teaching (Expand Search), care testing (Expand Search), acid testing (Expand Search)
element » elements (Expand Search)
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Versions of the core libraries used in this work.
Published 2025“…To ensure model interpretability, an XAI-based algorithm named Local Interpretable Model-Agnostic Explanations (LIME) was used to explain the predictions of the proposed framework. …”
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Confusion matrix on test image.
Published 2025“…In the current work, we have developed a model using a dataset that consists of a combination of 928 ECG images taken from publicly available Mendeley Data. …”
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Data supporting figures and tables in Attention-Based Framework for Automated Symbol Recognition and Wiring Design in Electrical Diagrams
Published 2025“…The system is tested across <b>proprietary and public datasets,</b> including:</p><ul><li>CGHD (Circuit Graph Hand-drawn Diagrams)</li><li>DCD (Digital Circuit Diagrams)</li></ul><h4><b>Datasets Included</b></h4><p dir="ltr">This data bundle includes the <b>primary data</b> used to generate figures and tables within the manuscript:</p><ul><li>Model performance metrics across different attention modules (Table 1 - Table 3).…”
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DataSheet1_Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm.docx
Published 2024“…Objective<p>The aim of this study was to develop and validate a machine learning-based model to predict the development of impaired fasting glucose (IFG) in middle-aged and older elderly people over a 5-year period using data from a cohort study.…”
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