Search Results - (( java application swarm algorithm ) OR ( learning solution mining algorithm ))
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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Accelerated mine blast algorithm for ANFIS training for solving classification problems
Published 2016“…Mine Blast Algorithm (MBA) is newly developed metaheuristic technique. …”
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An artificial immune system model as talent performance predictor / Siti ‘Aisyah Sa’dan, Hamidah Jantan and Mohd Hanapi Abdul Latif
Published 2016“…Immune based algorithm is part of bio-inspired algorithms elicits theories which can act as an inspiration for computer-based solutions. …”
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Diabetes Diagnosis And Level Of Care Fuzzy Rule-Based Model Utilizing Supervised Machine Learning For Classification And Prediction
Published 2024“…Therefore, a fuzzy model based on machine learning and data mining is a vital solution. In this study, ten supervised machine learning algorithms namely the J48, Logistic, NaiveBayes Updateable, RandomTree, BayesNet, AdaBoostM1, Random Forest, Multilayer Perceptron, Bagging and Stacking are applied for a simulated diabetes fuzzy dataset, verified by medical experts. …”
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…The experimental results demonstrate that the proposed algorithm is competitive compared to the state-of-the-art semi-supervised learning algorithms in terms of accuracy. …”
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A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works
Published 2023Conference Paper -
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Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem
Published 2023Conference Paper -
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An enhanced version of black hole algorithm via levy flight for optimization and data lustering problems
Published 2019“…Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems, in which it is a population-based metaheuristic that emulates the phenomenon of the black holes in the universe. …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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An enhanced version of black hole algorithm via levy flight for optimization and data clustering problems
Published 2019“…Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems, in which it is a population-based metaheuristic that emulates the phenomenon of the black holes in the universe. …”
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Improving intrusion detection using genetic algorithm
Published 2013“…To overcome this problem, a genetic algorithm approach is proposed. Genetic Algorithm (GA) is most frequently employed as a robust technology based on machine learning for designing IDS. …”
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Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO
Published 2014“…The proposed method is applied to 14 real world dataset from the machine learning repository. The algorithm’s performance is illustrated by the corresponding table of the classification rate. …”
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Synergizing intelligence and knowledge discovery: Hybrid black hole algorithm for optimizing discrete Hopfield neural network with negative based systematic satisfiability
Published 2024“…Additionally, a Hybrid Black Hole Algorithm was proposed in the retrieval phase to optimize the final neuron states. …”
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Towards a better feature subset selection approach
Published 2010“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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