Number of features selected for IRBMO vs. other binary algorithms. حسب Chenyi Zhu (9383370)

Sensitivity values of IRBMO vs. other binary algorithms. حسب Chenyi Zhu (9383370)

Specificity values of IRBMO vs. other binary algorithms. حسب Chenyi Zhu (9383370)

Fitness values of IRBMO vs. other binary algorithms. حسب Chenyi Zhu (9383370)

Classification accuracy values of IRBMO vs. other binary algorithms. حسب Chenyi Zhu (9383370)

F-score values of IRBMO vs. other binary algorithms. حسب Chenyi Zhu (9383370)

Flowchart depicting the structure of the BDMO algorithm. حسب Olatunji A. Akinola (13919696)

IRBMO vs. feature selection algorithm boxplot. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

The parameter settings of the RF algorithm after parameter optimization. حسب Xini Fang (20861990)

The Pseudo-Code of the IRBMO Algorithm. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

IRBMO vs. meta-heuristic algorithms boxplot. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

Parameter optimization results of the RF model improved by FCM clustering. حسب Xini Fang (20861990)

Evaluation metrics of the models’ performance. حسب Guangwei Liu (181992)

Pseudocode of artificial dragonfly algorithm. حسب Ghassan Ahmed Ali (17041488)

الموضوعات:

The Hopfield artificial neural network algorithm. حسب Ghassan Ahmed Ali (17041488)

الموضوعات:

Features selected by optimization algorithms. حسب Afnan M. Alhassan (18349378)

"…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …"

The workflow diagram for the gene expression biomarker study which involve using principal component analysis (PCA) to reduce the number of features (principal components), thus re... حسب Aninda Astuti (21736334)

Zimbabwe model outputs on actual and optimized 2016 spending and impact by intervention. حسب Nicole Fraser-Hurt (2636833)

Optimized model prediction performance. حسب Guang Tu (22054865)

Training error and accuracy for all HNN models. حسب Ghassan Ahmed Ali (17041488)

الموضوعات: