Association between maxillary protrusion and oral habits. by Kengo Oka (1420585)

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Association between crowding and oral habits. by Kengo Oka (1420585)

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Guide frame that appears on the smartphone application. by Kengo Oka (1420585)

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Association between deep bite and oral habits. by Kengo Oka (1420585)

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Performance metrics of the logistic regression models on the test dataset. by Kengo Oka (1420585)

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Representative photographic image. by Kengo Oka (1420585)

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Breakdown of participants by residential area. by Kengo Oka (1420585)

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Flowchart of study participants and design. by Kengo Oka (1420585)

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Breakdown of participants by annual household income. by Kengo Oka (1420585)

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Performance metrics of the machine learning models on the test dataset. by Kengo Oka (1420585)

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Each variable for the dataset. by Kengo Oka (1420585)

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Visual representation of the combination of feature extraction approaches employed and classification algorithms to identify stomata. by Alexandre H. Aono (11603934)

Testing algorithm for modified 2-tiered approaches to Lyme disease diagnosis. by Richard Porwancher (11413931)

Hair removal preprocessing using the DullRazor algorithm before image generation. by Mujung Kim (22367223)

Smaller gene set of DFC was selected by a unique selection criterion. by Takeru Fujii (11731550)

Pseudo Code of RBMO. 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. …”

P-value on CEC-2017(Dim = 30). 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. …”

Memory storage behavior. 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. …”

Elite search behavior. 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. …”

Description of the datasets. 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. …”