The probability density function of points in feature space of variance versus mean of the local field for (0,3] and (0,3] displayed as a contour-plot. by Ali Talebi (7164203)

(a): These systems were simulated for (0,3] and (0,3] without the prior knowledge about different phases, and the probability density function of points in feature space illustrate... by Ali Talebi (7164203)

“…(b): The dense areas are separated by removing the data less than threshold = 0.5 in the probability density function. (c): The centroid of each cluster is determined by the K-means algorithm.…”

Comparison of classification accuracy across different machine learning models using the objective function metric. by Amal H. Alharbi (21755906)

Phase stability at interfaces (dataset) by J Pitfield (21962924)

Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches by Andrés Halabi Diaz (20798460)

“…This study integrates conceptual density functional theory (CDFT) descriptors with explainable no-code machine learning (ML) models to predict NA mutagenicity based on Ames test results. …”

Multidimensional test results of BWEMFO and MFO on IEEE CEC 2017 test functions. by Jingjing Ma (419752)

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Comparative experimental ranking results of BWEMFO and other famous optimization methods on IEEE CEC 2017 test functions. by Jingjing Ma (419752)

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Variations of loss functions versus epoch number. by Xueliang Guo (4797057)

Performance of SVM model for various kernel functions. by Xueliang Guo (4797057)

Variance of local fields versus mean of local fields for (a) , (b) . by Ali Talebi (7164203)

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

Data Sheet 1_Hybrid machine learning algorithms accurately predict marine ecological communities.pdf by Luciana Erika Yaginuma (10477013)

“…In the supervised stage, these associations were modeled as a function of the environmental features by five supervised algorithms (Support Vector Machine, Random Forest, k-Nearest Neighbors, Naive Bayes, and Stochastic Gradient Boosting), using 80% of the samples for training, leaving the remaining for testing. …”

Activation functions: Sigmoid function and ReLU function. by Wan-Hua Zhang (21601139)

Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network by Rajesh Kumar Thangavel (20591164)

“…In this research, Utilizing Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm for Brain Tumor Identification Based on Magnetic resonance imaging (PACDNN-COA-BTI-MRI) is proposed. …”

The phase-field function for three different network configurations considered in this work. by Martina Bukač (20161460)

Experimental results of BWEMFO-KELM with other methods on mammographic dataset. by Jingjing Ma (419752)

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Experimental results of BWEMMO-KELM with other methods on breast cancer dataset. by Jingjing Ma (419752)

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Flowchart of BWEMFO. by Jingjing Ma (419752)

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This section provides medical data tables used in the experiment. by Jingjing Ma (419752)

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Experimental results of BWEMFO-KELM with other methods on mammographic dataset. by Jingjing Ma (419752)

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