Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

“…This research presents Data-Driven Intrusion Detection System in Internet of Things utilizing Optimized Bayesian Regularization-Back Propagation Neural Network (DIDS-BRBPNN-BBWOA-IoT) to overcome these issues. …”

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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Application of ABC SMC to the fitting of the binary flip with cusp model. by Elena Camacho-Aguilar (10906327)

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

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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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