The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

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Processes in the bagged tree ensemble classification in our experiment. by Rinrada Tanthanathewin (18611171)

The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE... by Song Cui (553627)

Effect of image resolution on the performance of the binary classifier algorithm in predicting occurrence of a bird target(s) on an individual image using the validation imagery se... by Tsung-Wei Ke (18297551)

The results of the ten performance metrics for NQE+SVC vs NQE+QSVC based on classifying miRNA expression profiles. by Aninda Astuti (21736334)

Effect of training regions on the performance of the Mask R-CNN detection algorithm based on the refined annotation bounding boxes. by Tsung-Wei Ke (18297551)

Performance of the baseline SVM and Mask R-CNN detection algorithms based on the original, manually-derived annotated bounding boxes. by Tsung-Wei Ke (18297551)

Prediction AUC of Strokeformer and the competitor methods on the ten large-scale binary classification datasets. by Maocheng Cao (22127378)

Prediction AUC of Strokeformer and the competitor methods on the nine small-scale binary classification datasets. by Maocheng Cao (22127378)

Performance of the Mask R-CNN detection algorithm based on the original, manually-derived, annotation bounding boxes, and the refined annotation bounding boxes. by Tsung-Wei Ke (18297551)

Effect of image resolution on the performance of the Mask R-CNN algorithm in detecting individual bird instances as applied to the validation imagery set. by Tsung-Wei Ke (18297551)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

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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Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …”

The data processing and analysis pipeline. by Xin Zhang (35492)

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

AUC score comparison table for all the deep learning algorithms. by Khandaker Mohammad Mohi Uddin (13124356)

Comparative overview of all DL and ML algorithms parameters in used in this study. by Khandaker Mohammad Mohi Uddin (13124356)

An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. by Olaide N. Oyelade (14047002)

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Small-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

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