Dynamic voltage stability prediction of power systems by a new feature selection technique and probabilistic neural network

2010 ◽  
Vol 21 (1) ◽  
pp. 312-328 ◽  
Author(s):  
Nima Amjady ◽  
Mohammad Hossein Velayati
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Power Systems ◽  
Probabilistic Neural Network ◽  
Stability Prediction ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Dynamic Voltage ◽  
Dynamic Voltage Stability ◽  
New Feature

scholarly journals Thai Water Buffalo Disease Analysis with the Application of Feature Selection Technique and Multi-Layer Perceptron Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 6907-6911
Author(s):  
S. Nuanmeesri ◽  
W. Sriurai
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Water Buffalo ◽  
Information Gain ◽  
Sampling Technique ◽  
Multi Layer Perceptron ◽  
Analysis Model ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Correlation Based Feature Selection

This research aims to develop the analysis model for diseases in water buffalo towards the application of the feature selection technique along with the Multi-Layer Perceptron neural network. The data used for analysis was collected from books and documents related to diseases in water buffalo and the official website of the Department of Livestock Development. The data consists of the characteristics of six diseases in water buffalo, including Anthrax disease, Hemorrhagic Septicemia, Brucellosis, Foot and Mouth disease, Parasitic disease, and Mastitis. Since the amount of the collected data was limited, the Synthetic Minority Over-sampling Technique was also employed to adjust the imbalance dataset. Afterward, the adjusted dataset was used to select the disease characteristics towards the application of two feature selection techniques, including Correlation-based Feature Selection and Information Gain. Subsequently, the selected features were then used for developing the analysis model for diseases in water buffalo towards the use of Multi-Layer Perceptron neural network. The evaluation results of the model’s effectiveness, given by the 10-fold cross-validation, showed that the analysis model for diseases in water buffalo developed by Correlation-based Feature Selection and Multi-Layer Perceptron neural network provided the highest level of effectiveness with the accuracy of 99.71%, the precision of 99.70%, and the recall of 99.72%. This implies that the analysis model is effectively applicable.

scholarly journals A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change

2021 ◽  
Vol 14 (1) ◽  
pp. 40
Author(s):  
Hamed Naseri ◽  
E. Owen D. Waygood ◽  
Bobin Wang ◽  
Zachary Patterson ◽  
Ricardo A. Daziano
Keyword(s):  
Climate Change ◽  
Feature Selection ◽  
Environmental Concern ◽  
Stage Of Change ◽  
Principal Component ◽  
Selection Methods ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Testing Data ◽  
New Feature

Indications of people’s environmental concern are linked to transport decisions and can provide great support for policymaking on climate change. This study aims to better predict individual climate change stage of change (CC-SoC) based on different features of transport-related behavior, General Ecological Behavior, New Environmental Paradigm, and socio-demographic characteristics. Together these sources result in over 100 possible features that indicate someone’s level of environmental concern. Such a large number of features may create several analytical problems, such as overfitting, accuracy reduction, and high computational costs. To this end, a new feature selection technique, named the Coyote Optimization Algorithm-Quadratic Discriminant Analysis (COA-QDA), is first proposed to find the optimal features to predict CC-SoC with the highest accuracy. Different conventional feature selection methods (Lasso, Elastic Net, Random Forest Feature Selection, Extra Trees, and Principal Component Analysis Feature Selection) are employed to compare with the COA-QDA. Afterward, eight classification techniques are applied to solve the prediction problem. Finally, a sensitivity analysis is performed to determine the most important features affecting the prediction of CC-SoC. The results indicate that COA-QDA outperforms conventional feature selection methods by increasing average testing data accuracy from 0.7 to 5.6%. Logistic Regression surpasses other classifiers with the highest prediction accuracy.

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