Random forest with hyperparameter optimization. حسب Balraj Preet Kaur (20370832)

Best optimizer results for random forest. حسب Balraj Preet Kaur (20370832)

Best optimizer results of Random Forest. حسب Balraj Preet Kaur (20370832)

Results of Random Forest. حسب Balraj Preet Kaur (20370832)

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

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Flowchart scheme of the ML-based model. حسب Noshaba Qasmi (20405009)

"…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …"

Flow diagram of the proposed model. 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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The accuracy score change matrix following model development. 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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Confusion matrix of the ABC–LR–RF hybrid model for IVF outcome prediction. 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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Hyperparameters of the LSTM Model. حسب Ahmed M. Elshewey (21463867)

"…Out of all the models, LSTM produced the best results. The AD-PSO-Guided WOA algorithm was used to adjust the hyperparameters for the LSTM model. …"

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

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Prediction results of individual models. حسب Ahmed M. Elshewey (21463867)

"…Out of all the models, LSTM produced the best results. The AD-PSO-Guided WOA algorithm was used to adjust the hyperparameters for the LSTM model. …"

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data حسب Changhun Kim (682542)

"…An assay matrix based on CI and DS was prepared for 335 assays with biologically intended target information, and 28 CI assays and 3 DS assays were selected. Thirty models established by combining five molecular fingerprints (i.e., Morgan, MACCS, RDKit, Pattern, and Layered) and six algorithms [i.e., gradient boosting tree, random forest (RF), multi-layered perceptron, <i>k</i>-nearest neighbor, logistic regression, and naive Bayes] were trained using the selected assay data set. …"

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

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The most effective active substances for subfertility treatment. 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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