Optimized model prediction performance. by Guang Tu (22054865)

Details of 23 basic benchmark functions. by Ying Li (38224)

The structure of genetic algorithm (GA). by Ali Akbar Moosavi (17769033)

“…Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

Comparison statistics of different machine learning algorithms including MLP, GRNN, and RBF for modeling and predicting callogenesis rate and callus fresh weight of petunia. by Hamed Rezaei (11085366)

Rapid Prediction of Chemical Ecotoxicity Through Genetic Algorithm Optimized Neural Network Models by Ping Hou (17213)

“…In this study, artificial neural network models are developed to predict chemical ecotoxicity (HC₅₀) based on experimental data to fill data gaps in a widely used database (USEtox). …”

Optimal image settings for object detection of Iba1 immunolabelled cells in DAB. by Danish M. Anwer (15383441)

AUROC scores with 95% CI from Day 1 to Day 4 for different algorithms. by Armin Abdollahi (21440129)

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

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Image1_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.JPEG by Wisnu Ananta Kusuma (9276182)

Image2_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.JPEG by Wisnu Ananta Kusuma (9276182)

Image4_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.jpg by Wisnu Ananta Kusuma (9276182)

Image5_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.jpg by Wisnu Ananta Kusuma (9276182)

Image3_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.JPEG by Wisnu Ananta Kusuma (9276182)

DataSheet1_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.docx by Wisnu Ananta Kusuma (9276182)

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

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Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization by Jungmok Oh (14117464)

“…This paper presents a hybrid framework that combines ML models with a metaheuristic optimization algorithm, to explore improved heterogeneous catalysts for propane dehydrogenation (PDH). …”

Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization by Jungmok Oh (14117464)

“…This paper presents a hybrid framework that combines ML models with a metaheuristic optimization algorithm, to explore improved heterogeneous catalysts for propane dehydrogenation (PDH). …”

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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