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1
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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2
Validation of combine white noise using simulated data
Published 2016“…Recent studies reveal that the data that exhibits heteroscedasticity are modelled by Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH).Nevertheless, EGARCH model estimation is not efficient when the heteroscedasticity data have leverage effect.In this study, an algorithm is developed which is called Combine White Noise (CWN).The standardized residuals of EGARCH errors (heteroscedastic variance) are decomposed into equal variances (white noise series). …”
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3
Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning
Published 2025“…Choosing a suitable optimization algorithm in deep learning is essential for effective model development as it significantly influences convergence speed, model performance, and the success of the train- ing process. …”
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Proceeding Paper
