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values decrease » values increased (Expand Search), largest decrease (Expand Search)
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values decrease » values increased (Expand Search), largest decrease (Expand Search)
marked decrease » marked increase (Expand Search)
classifiers » classifier (Expand Search), classified (Expand Search)
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CT outcome of HPDL-MIAIHD technique with other methods.
Published 2025Subjects: “…Biological Sciences not elsewhere classified…”
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CT outcome of HPDL-MIAIHD technique with recent methods.
Published 2025Subjects: “…Biological Sciences not elsewhere classified…”
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Accuracy of multiple classifiers with a deep-learning-based feature set
Published 2025Subjects: “…Biological Sciences not elsewhere classified…”
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The performance (F1) of 11 behavioral classifiers (models) and their corresponding Detection Thresholds.
Published 2024Subjects: “…Biological Sciences not elsewhere classified…”
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Confusion matrix for the proposed ensemble classifier.
Published 2025Subjects: “…Biological Sciences not elsewhere classified…”
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Logistic Classifier Confusion Matrix.
Published 2025“…The validated data is employed to train machine learning classifiers and deep learning algorithms, targeting a 27.25% enhancement in operational efficiency based on F1 score. …”
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Statistic Result of Random Forest Classifier.
Published 2025“…The validated data is employed to train machine learning classifiers and deep learning algorithms, targeting a 27.25% enhancement in operational efficiency based on F1 score. …”
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Statistic Result of Support Vector Classifier.
Published 2025“…The validated data is employed to train machine learning classifiers and deep learning algorithms, targeting a 27.25% enhancement in operational efficiency based on F1 score. …”
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Confusion Matrix for Support Vector Classifier.
Published 2025“…The validated data is employed to train machine learning classifiers and deep learning algorithms, targeting a 27.25% enhancement in operational efficiency based on F1 score. …”
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Naïve Bayes Classifier Confusion Matrix.
Published 2025“…The validated data is employed to train machine learning classifiers and deep learning algorithms, targeting a 27.25% enhancement in operational efficiency based on F1 score. …”