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Your search keyword '"Slimane, Jihane Ben"' showing total 2 results
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2 results on '"Slimane, Jihane Ben"'

1. Adaptive Method for Feature Selection in the Machine Learning Context.

Author

El Touati, Yamen, Slimane, Jihane Ben, and Saidani, Taoufik

Subjects
FEATURE selection, RANDOM forest algorithms, ARTIFICIAL intelligence, REGRESSION analysis, ALGORITHMS
Abstract

Feature selection is a fundamental aspect of machine learning that is crucial for improving the accuracy and efficiency of models. It carefully analyzes the abundance of data to identify the most significant characteristics, hence improving the accuracy of predictions and minimizing the likelihood of model overfitting. This technique not only optimizes model training by reducing computational requirements, but also enhances the model's interpretability, resulting in more transparent and reliable predictions. The deliberate omission of unnecessary variables is a process of improving the model and also constitutes a crucial measure toward achieving more flexible and comprehensible results in machine learning. An analysis to assess the effectiveness of feature selection on regression models was conducted. The impact was measured using Mean Squared Error (MSE) metrics. A variety of regression algorithms were evaluated, and then feature selection techniques, including statistical and algorithmic methods, such as SelectKBest, PCA, and RFE with Linear Regression and Random Forest, were applied. After selecting the features, linear models demonstrated improvements in mean squared error (MSE), highlighting the value of removing unnecessary data. This study emphasizes the subtle impact of feature selection on model performance, calling for a tailored strategy to maximize prediction accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks.

Author

Assadi, Khaoula, Slimane, Jihane Ben, Chalandi, Hanene, and Salhi, Salah

Subjects
ELECTRIC lines, ELECTRIC power, RECURRENT neural networks, STANDARD deviations, ARTIFICIAL neural networks, CEREBROSPINAL fluid shunts
Abstract

Purpose: This study aims to focus on an adaptive method for fault detection and classification of fault types that trigger in three-phase transmission lines using artificial neural networks (ANNs). The proposed scheme can detect and classify several types of faults, including line-to-ground, line-to-line, double-line-to-ground, triple-line and triple-line-to-ground faults. Design/methodology/approach: The fundamental components of three-phase current and voltage were used as inputs in the ANNs. An analysis of the impact of variations in the fault resistance, fault type and fault inception time was conducted to evaluate the ANNs performance. The survey compares the performance of the multi-layer perceptron neural network (MLPNN) and Elman recurrent neural network trained with the backpropagation learning technique to improve each of the three phases of the fault detection and classification process. A detailed analysis validates the choice of the ANNs architecture based on the variation in the number of hidden neurons in each step. Findings: The mean square error, root mean square error, mean absolute error and linear regression are measured to improve the efficiency of the ANN models for both fault detection and classification. The results indicate that the MLPNN can detect and classify faults with a satisfactory performance. Originality/value: The smart adaptive scheme is fast and accurate for fault detection and classification in a single circuit transmission line when faced with different conditions and can be useful for transmission line protection schemes. [ABSTRACT FROM AUTHOR]

Published
2023
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