Search alternatives:
processes optimization » process optimization (Expand Search), process optimisation (Expand Search), property optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
loss processes » poisson processes (Expand Search)
binary b » binary _ (Expand Search)
b model » _ model (Expand Search), a model (Expand Search), 2 model (Expand Search)
processes optimization » process optimization (Expand Search), process optimisation (Expand Search), property optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
loss processes » poisson processes (Expand Search)
binary b » binary _ (Expand Search)
b model » _ model (Expand Search), a model (Expand Search), 2 model (Expand Search)
-
1
-
2
-
3
-
4
-
5
-
6
-
7
<i>hi</i>PRS algorithm process flow.
Published 2023“…<b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”
-
8
-
9
-
10
-
11
-
12
-
13
-
14
Classification baseline performance.
Published 2025“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”
-
15
Feature selection results.
Published 2025“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”
-
16
ANOVA test result.
Published 2025“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”
-
17
Summary of literature review.
Published 2025“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”
-
18
-
19
Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations.
Published 2023“…After <i>K</i> − 2 = 2 steps, the resulting tree is binary and the algorithm stops.</p>…”
-
20
Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish)
Published 2024“…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …”