Representation of the feature selection algorithm. حسب Betty Wutzl (6945248)

Binary leaf image rotated anti-clock wise from the origin degree to 40^<i>o</i>, 80^<i>o</i>, 120^<i>o</i>, and 160^<i>o</i> from left to ri... حسب Ali Ahmed (2567584)

Top-performing model configuration and performance by algorithm type. حسب Uğur Ejder (22683228)

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The statistical description of the original data set of the patients (<i>n</i> = 162). حسب Uğur Ejder (22683228)

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The list of parameters of the modified data set for machine learning (<i>n</i> = 162). حسب Uğur Ejder (22683228)

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Determination of the Solute Content and Volumetric Properties of Binary Ionic Liquid Mixtures Using a Global Regularity of Molar Volume Expansion حسب Javad Hekayati (1383249)

"…This regularity is presented for the first time as a robust alternative for the representation of experimental <i>P</i>–ν–<i>T</i>–<i>x</i> data of binary IL+ solute systems, with a much simpler formulation compared to different equations of state and without the inclusion of any temperature-dependent adjusted parameters. …"

ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction. حسب Uğur Ejder (22683228)

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The accuracy score change matrix following model development. حسب Uğur Ejder (22683228)

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The comparison of the accuracy score of the benchmark and the proposed models. حسب Uğur Ejder (22683228)

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The prediction accuracy of simple versus hybrid models. حسب Uğur Ejder (22683228)

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Flow diagram of the proposed model. حسب Uğur Ejder (22683228)

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Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%). حسب Uğur Ejder (22683228)

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The most effective active substances for subfertility treatment. حسب Uğur Ejder (22683228)

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Confusion matrix of the ABC–LR–RF hybrid model for IVF outcome prediction. حسب Uğur Ejder (22683228)

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Flow diagram of the conversion of drugs into active substances. حسب Uğur Ejder (22683228)

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Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction. حسب Uğur Ejder (22683228)

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Fig 3 - حسب Ali Ahmed (2567584)

AttentiveSkin: To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods حسب Zejun Huang (45753)

"…Then, a series of binary classifiers were developed with five machine learning (ML) algorithms and six molecular representations. …"

Frequency-comparison plots for the eleven categories of linguistic features. حسب Olga Kellert (12254765)

Flow chart of the algorithm. حسب Mukesh Karunanethy (18422857)

"…<p>Flow chart showing the algorithm pipeline, including time series normalization, filtering, feature extraction, feature reduction, and data splitting into training and testing. …"