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Different mutation and crossover set of genetic programming in an automated machine learning
Published 2020“…The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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Different mutation and crossover set of genetic programming in an automated machine learning
Published 2020“…The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol
Published 2023“…In this research, two machine learning models, Deep Neural Network (DNN) and Support Vector Machine (SVM), are utilized for sound classification. …”
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An enhanced soft set data reduction using decision partition order technique
Published 2017“…Furthermore, the decomposition based on previous algorithms could not achieve better parameter reduction in available domain space. …”
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Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach
Published 2025“…The descriptive analysis examines purchasing behavior through correlation and regression analyses, while the predictive model uses decision trees (J48, Random Tree, REPTree), rule-based algorithms (JRip, OneR, PART), and clustering (K-Means) to identify patterns and predict trends. …”
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Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines
Published 2020“…These parameters are used to develop a standalone intelligently machine learning adaptive distance relay (ML-ADR) modification. …”
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Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine
Published 2019“…The hybridization can improve the convergence speed in PSO in order to find the optimal parameters of SVM. In the feature extraction process, the PCA algorithm is used for that purpose and the resulted features are delivered to the proposed technique in order to classify the face images. …”
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Optimisation of support vector machine hyperparameters using enhanced artificial bee colony variant to diagnose breast cancer
Published 2023“…However, these methods are vulnerable to inaccuracies due to human intervention in diagnosis. Machine learning offers a solution to these challenges, with support vector machines (SVM) being a popular choice for breast cancer diagnosis given its strength in binary classification, which suited well with the dataset used in this thesis. …”
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An enhanced gated recurrent unit with auto-encoder for solving text classification problems
Published 2020“…Classification has become an important task for categorizing documents automatically based on their respective groups. …”
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Intelligent classification algorithms in enhancing the performance of support vector machine
Published 2019“…The difference between the algorithms is the size of the solution archive. The size of the archive in ACOMV is fixed while in IACOMV, the size of solution archive increases as the optimization procedure progress. …”
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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm
Published 2023Subjects:Conference Paper -
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Refinement of Tuned Mass Damper parameters on machine support structure using dynamic Cuckoo Search algorithm
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Estimation of optimal machining control parameters using artificial bee colony
Published 2013“…This research employed ABC algorithm to optimize the machining control parameters that lead to a minimum surface roughness (R a) value for AWJ machining. …”
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Near-infrared technique for oil palm fruit grading system
Published 2013“…The developed system showed high classification results on accuracy of the maturity detection for the three types of oil palm fruits (nigrescens, virescens, and oleifera ) with rates of 95%, 99%, and 98 %, respectively, using the ANN-MLP classifier; rates of 96%, 99%, and 98 %, respectively, using the KNN classifier; and rates of 76%, 96%, and 94%,respectively, using SVM. …”
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Development of cell formation algorithm and model for cellular manufacturing system
Published 2011“…Therefore, for this proposes good benchmarked algorithm, bacteria foraging algorithm is selected and developed to solve multiobjective cell formation model and traced constraints satisfaction handling to produce feasible optimal solution. …”
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Real-time oil palm fruit bunch ripeness grading system using image processing techniques
Published 2013“…These results are optimal based on the thorns model. A new approach was developed using expert rules-based system. …”
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Optimization of cnc turning parameters for minimizing temperature rise in aluminum using a genetic algorithm
Published 2024“…Thus, an optimization process is important to obtain optimized machining outcomes by optimizing machining parameters. …”
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Multi-objectives process optimization in end milling process of aluminium alloy 6061-T6 using genetic algorithm
Published 2024“…For future study, other methods or algorithms can be applied in other machining processes to obtain optimum machining parameters.…”
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