Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for binary... by Amin Ullah (12015113)

Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for binary... by Amin Ullah (12015113)

Data Sheet 1_Bundled assessment to replace on-road test on driving function in stroke patients: a binary classification model via random forest.docx by Lu Huang (211625)

“…The subject was classified as either Success or Unsuccess group according to whether they had completed the on-road test. A random forest algorithm was then applied to construct a binary classification model based on the data obtained from the two groups.…”

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. …”

The Pseudo-Code of the IRBMO Algorithm. 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. …”

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

Grad-CAM heatmap highlighting affected regions as per the predicted class (left), and its corresponding binary activation mask using Otsu thresholding (right) to extract high-impor... by Sulav Baral (22477755)

IRBMO vs. meta-heuristic algorithms boxplot. 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. …”

IRBMO vs. feature selection algorithm boxplot. 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. …”

ROC curves of the proposed method for multiclass classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning. by Md. Sabbir Hossain (9958939)

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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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