Search Results - (( parameter optimization method algorithm ) OR ( subset detection method algorithm ))
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Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…The aim of this work is to develop an improved optimization method for IDS that can be efficient and effective in subset feature selection and parameters optimization. …”
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Thesis -
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Rao-SVM machine learning algorithm for intrusion detection system
Published 2020“…Most of the intrusion detection systems are developed based on optimization algorithms as a result of the increase in audit data features; optimization algorithms are also considered for IDS due to the decline in the performance of the human-based methods in terms of their training time and classification accuracy. …”
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Article -
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Improved intrusion detection algorithm based on TLBO and GA algorithms
Published 2021“…The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). …”
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Heart disease prediction using artificial neural network with ADAM optimization and harmony search algorithm
Published 2025“…In summary, this study advances the field by delivering an effective, optimized predictive algorithm for early heart disease detection, thereby offering valuable insights that could enhance healthcare outcomes, support proactive cardiovascular risk management, and pave the way for future innovations in personalized medicine…”
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Optimization of feature selection in Support Vector Machines (SVM) using recursive feature elimination (RFE) and particle swarm optimization (PSO) for heart disease detection
Published 2024“…RFE combines a dimensionality reduction process with a machine learning process where the least important features are iteratively removed until the best feature subset is obtained, while the PSO method is used to optimize the C and gamma parameters of SVM. …”
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Proceeding Paper -
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Robust techniques for linear regression with multicollinearity and outliers
Published 2016“…The ordinary least squares (OLS) method is the most commonly used method in multiple linear regression model due to its optimal properties and ease of computation. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…Experiments demonstrate and prove that the proposed EBPSO method produces better accuracy mining data and selecting subset of relevant features comparing other algorithms. …”
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9
Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System
Published 2020“…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…Experiments demonstrate that ensemble classifier learning method produces better accuracy mining data streams and selecting subset of relevant features comparing other single classifiers. …”
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11
A Naïve-Bayes classifier for damage detection in engineering materials
Published 2007“…The Naïve-Bayes classifier and the proposed feature subset selection algorithm have been shown as efficient techniques for damage detection in engineering materials. …”
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Integrating genetic algorithms and fuzzy c-means for anomaly detection
Published 2005“…In this paper we propose an intrusion detection method that combines Fuzzy Clustering and Genetic Algorithms. …”
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Conference or Workshop Item -
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Algorithm of face recognition by principal component analysis
Published 2002“…The database is divided into subsets, where each subset represents one of 10 different individuals. …”
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Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification
Published 2024“…In addition, the local optima issue is overcome by the population reinitialisation method. The proposed algorithm, named the CFS-Mutable Composite Firefly Algorithm (CFS-MCFA), is evaluated based on two metrics, namely classification accuracy and genes subset size, using a Support Vector Machine (SVM) classifier. …”
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Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
Published 2011“…The results show that proposed method provides an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.…”
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Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
Published 2011“…The results show that proposed method provides an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.…”
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17
Optimization of turning parameters using genetic algorithm method
Published 2008“…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
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Undergraduates Project Papers -
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Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis
Published 2018“…The efficiency of the three algorithms are evaluated and compared with previous results obtained by other optimization methods on similar studies. …”
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Conference or Workshop Item -
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Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization
Published 2014“…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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Optimization of turning parameters using ant colony optimization
Published 2008“…The project objectives are to develop Ant Colony Optimization (ACO) algorithm for CNC turning process and to optimize turning parameters for minimized production cost per unit. …”
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Undergraduates Project Papers
