Default and optimized parameters for each predictive model. by Emily S. Nichols (8740029)

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

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Personal algorithmic recommendations of potassium and phosphorous intake and the corresponding concentration predictions. by Jari Turkia (17912475)

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Nutrient predictors of the model. by Jari Turkia (17912475)

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

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

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

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Comparison of model structures for personal nutrient effects. by Jari Turkia (17912475)

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

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Correlation plot of matrix C_<i>b</i> that includes the estimated within-model and cross-model correlations of personal effects. by Jari Turkia (17912475)

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DataSheet1_Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes.PDF by Eva Gonzalez-Flo (5046737)

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”

Correlation plot of matrix C_<i>g</i> that includes the estimated within-model and cross-model correlations in level of dialysis treatment. by Jari Turkia (17912475)

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Posterior predictive check (PPC) of the final nutritional effect model. by Jari Turkia (17912475)

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Structure of the graphical model with general, treatment, and personal level nutrient effects. by Jari Turkia (17912475)

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

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

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Data collection during the patients’ usual treatment routine. by Jari Turkia (17912475)

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

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

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