Several loop patterns found in binary code. by Alba Pedro-Zapater (8600382)

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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Proposed Algorithm. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”

Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

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

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IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

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

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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)

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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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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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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