Comparison of ROC curves between the baseline and the algorithm proposed in this paper. by Ping Xia (220386)

t-SNE analysis experimental results, including the t-SNE experimental results before training, the experimental results after training of the original baseline training, and the ex... by Ping Xia (220386)

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

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

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

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

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

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. by Tristan Mary-Huard (3864)

“…After <i>K</i> − 2 = 2 steps, the resulting tree is binary and the algorithm stops.</p>…”

Dataset 1: Zip file containing the figures of the presented methods and results in jpeg files by Suchismita Behera (22027316)

“…<p dir="ltr">Figures represented here illustrates the <b>metaheuristic-based band selection framework</b> for hyperspectral image classification using <b>Binary Jaya Algorithm enhanced with a mutation operator</b> to improve population diversity and avoid premature convergence. …”

Pseudo Code of RBMO. by Chenyi Zhu (9383370)

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

P-value on CEC-2017(Dim = 30). by Chenyi Zhu (9383370)

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

Memory storage behavior. by Chenyi Zhu (9383370)

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

Elite search behavior. by Chenyi Zhu (9383370)

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

Description of the datasets. by Chenyi Zhu (9383370)

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

S and V shaped transfer functions. by Chenyi Zhu (9383370)

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

S- and V-Type transfer function diagrams. by Chenyi Zhu (9383370)

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

Collaborative hunting behavior. by Chenyi Zhu (9383370)

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

Friedman average rank sum test results. by Chenyi Zhu (9383370)

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

IRBMO vs. variant comparison adaptation data. by Chenyi Zhu (9383370)

“…Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles by Peter S. Rice (11805875)

“…However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. …”

Related studies on IDS using deep learning. by Arshad Hashmi (13835488)

“…The suggested model’s accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. …”

The architecture of the BI-LSTM model. by Arshad Hashmi (13835488)

“…The suggested model’s accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. …”