بدائل البحث:
feature optimization » resource optimization (توسيع البحث), feature elimination (توسيع البحث), structure optimization (توسيع البحث)
based optimization » whale optimization (توسيع البحث)
binary cases » binary values (توسيع البحث), binary labels (توسيع البحث), binary mask (توسيع البحث)
main feature » main features (توسيع البحث), each feature (توسيع البحث), a feature (توسيع البحث)
cases based » case based (توسيع البحث), scores based (توسيع البحث), values based (توسيع البحث)
binary main » binary mask (توسيع البحث), binary image (توسيع البحث), binary pairs (توسيع البحث)
feature optimization » resource optimization (توسيع البحث), feature elimination (توسيع البحث), structure optimization (توسيع البحث)
based optimization » whale optimization (توسيع البحث)
binary cases » binary values (توسيع البحث), binary labels (توسيع البحث), binary mask (توسيع البحث)
main feature » main features (توسيع البحث), each feature (توسيع البحث), a feature (توسيع البحث)
cases based » case based (توسيع البحث), scores based (توسيع البحث), values based (توسيع البحث)
binary main » binary mask (توسيع البحث), binary image (توسيع البحث), binary pairs (توسيع البحث)
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21
Parameter setting for LSTM.
منشور في 2023"…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …"
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22
LITNET-2020 data splitting approach.
منشور في 2023"…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …"
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23
Transformation of symbolic features in NSL-KDD.
منشور في 2023"…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …"
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24
Analysis and design of algorithms for the manufacturing process of integrated circuits
منشور في 2023"…From this, we propose: (i) a new ILP model, and (ii) a new solution representation, which, unlike the reference work, guarantees that feasible solutions are obtained throughout the generation of new individuals. Based on this new representation, we proposed and evaluated other approximate methods, including a greedy algorithm and a genetic algorithm that improve the state-of-the-art results for test cases usually used in the literature. …"
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25
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26
Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf
منشور في 2020"…And we propose two new multilabel uses of the Common Spatial Pattern (CSP) algorithm to optimize the signal-to-noise ratio, namely MC2CMI and MC2SMI approaches. …"
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27
Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield...
منشور في 2022"…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …"
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28
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29
DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx
منشور في 2021"…We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …"
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30
Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat.
منشور في 2025"…The single predictor variable was the mushroom habitat, a categorical feature that was preprocessed using the One-Hot Encoding technique, resulting in seven distinct binary variables. …"