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process optimization » model optimization (Expand Search)
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ai process » a process (Expand Search), ti process (Expand Search), ii process (Expand Search)
process optimization » model optimization (Expand Search)
design optimization » bayesian optimization (Expand Search)
image design » images designed (Expand Search), simple design (Expand Search), space design (Expand Search)
primary ai » primary aim (Expand Search), primary i (Expand Search), primary pci (Expand Search)
ai process » a process (Expand Search), ti process (Expand Search), ii process (Expand Search)
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MLP vs classification algorithms.
Published 2024“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …”
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Study design.
Published 2024“…At the delivery stage, all patients will receive both a Providence-type brace optimized by the semi-automatic algorithm leveraging a patient-specific FEM (Test) and a conventional Providence-type brace (Control), both designed using CAD/CAM methods. …”
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Sample image for illustration.
Published 2024“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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Quadratic polynomial in 2D image plane.
Published 2024“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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Comparison analysis of computation time.
Published 2024“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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Process flow diagram of CBFD.
Published 2024“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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Precision recall curve.
Published 2024“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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Fortran & C++: design fractal-type optical diffractive element
Published 2022“…</p> <p>(2) calculate diffraction fields for fractal and/or grid-matrix (binary) phase-holograms.</p> <p>(3) optimize the fractal and/or grid-matrix holograms for given target diffraction images, using annealing algorithms. …”
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SPAM-XAI confusion matrix.
Published 2024“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …”
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Illustration of MLP.
Published 2024“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …”
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Dataset detail division.
Published 2024“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …”
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Software defects types.
Published 2024“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …”
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SMOTE representation.
Published 2024“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …”
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Demonstration confusion matrix.
Published 2024“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …”
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Analysis PC2 AU-ROC curve.
Published 2024“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …”
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PROMISE defects prediction attribute aspects.
Published 2024“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …”
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Internal architecture of the SPAM-XAI model.
Published 2024“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …”