Urgent versus nonurgent binary classification performance. by Kyung Won Kwon (18150416)

ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

“…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

“…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

Accuracy assessment of 2017 binary classification and multi-classification result (OA(Overall accuracy), PA(Producer accuracy), UA(User accuracy), ML(Maximum likelihood)). by Lei Liang (326409)

Random forest binary classification of the colorized Corona image for the RSK region, Morocco (1972). by Safia Loulad (16913691)

Diagnostic performance for determining malignancy; binary classification. by Seung Seog Han (4782486)

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

“…Questionnaire data and tooth alignment images of the children were collected. The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”

A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets usi... by Olaide N. Oyelade (14047002)

A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional data... by Olaide N. Oyelade (14047002)

A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datas... by Olaide N. Oyelade (14047002)

Explicability results of the crowding classification model. by Kengo Oka (1420585)

Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

Explicability results of the deep bite classification model. by Kengo Oka (1420585)

Explicability results of the maxillary protrusion classification model. by Kengo Oka (1420585)

Objective performance of fully-automated SLAVV software. by Samuel A. Mihelic (11539996)

Random forest algorithm: Method and example results. by Natalie Weed (7345124)

“…White lines: boundaries of cortical areas in Allen Mouse Brain Reference Atlas. (<b>D</b>) Schematic illustration of arrays input into Random Forest algorithm. …”

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

“…Questionnaire data and tooth alignment images of the children were collected. The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”

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

“…Questionnaire data and tooth alignment images of the children were collected. The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”

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

“…Questionnaire data and tooth alignment images of the children were collected. The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”

Testing results for classifying AD, MCI and NC. by Nicodemus Songose Awarayi (18414494)

“…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”