Feature selection convergence time comparison of various binary metaheuristic algorithms. by Amal H. Alharbi (21755906)

Characteristics of the data sets used to evaluate Machine Learning and Deep Learning models for arboviral diseases classification. by Sebastião Rogério da Silva Neto (11944944)

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Histograms of various water quality parameters, including pH, Hardness, Solids, Chloramines, Sulfate, Organic Carbon, Trihalomethanes, Turbidity, and a binary portability check. by Amal H. Alharbi (21755906)

Classification performance after optimization. by Amal H. Alharbi (21755906)

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

Summary of all non-clinical data (laboratory and others) presented in the data set used by the primary studies. by Sebastião Rogério da Silva Neto (11944944)

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Models used in the works divided by the main problems. by Sebastião Rogério da Silva Neto (11944944)

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Study selection. by Sebastião Rogério da Silva Neto (11944944)

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Summary of all demographic, epidemiological and clinical data presented in data set used by the primary studies. by Sebastião Rogério da Silva Neto (11944944)

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Distribution of samples per classes. by Sebastião Rogério da Silva Neto (11944944)

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Overview of the primary studies. by Sebastião Rogério da Silva Neto (11944944)

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Metrics used to evaluate the models proposed in the literature. by Sebastião Rogério da Silva Neto (11944944)

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Attributes found in the data sets. by Sebastião Rogério da Silva Neto (11944944)

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Classification performance of ML and DL models. by Amal H. Alharbi (21755906)

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

Example depiction of the autoML process. by Cynthia Lokker (3813832)

Wilcoxon test results for feature selection. by Amal H. Alharbi (21755906)

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

Feature selection metrics and their definitions. by Amal H. Alharbi (21755906)

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

Statistical summary of all models. by Amal H. Alharbi (21755906)

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

ANOVA test for optimization results. by Amal H. Alharbi (21755906)

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

Feature selection results. by Amal H. Alharbi (21755906)

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

ANOVA test for feature selection. by Amal H. Alharbi (21755906)

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”