Search Results - (( using optimization method algorithm ) OR ( using quality learning algorithm ))
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Clustering ensemble learning method based on incremental genetic algorithms
Published 2012“…Moreover, experiments demonstrate that final clustering solution generated by the proposed incremental genetic-based clustering ensemble algorithm using the pattern ensemble learning method possess comparative or better clustering accuracy than clustering solutions generated by the incremental genetic-based clustering ensemble algorithms using other recombination operators. …”
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Optimization of balanced academic curriculum problem in educational institutions using teaching learning based optimization algorithm
Published 2025“…This study aims to optimize BACP using the Teaching-Learning Based Optimization (TLBO) algorithm, addressing the limitations of existing approaches and providing an efficient framework for curriculum balancing. …”
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Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection
Published 2026“…The experiment results show how these algorithms could be used to improve methods for recognizing human activities using wearables technology, such as feature selection, parameter adjustment, and model optimization.…”
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BAT-BP: A new BAT based back-propagation algorithm for efficient data classification
Published 2016“…Thus, this study investigates the use of Bat algorithm along with back-propagation neural network (BPNN) algorithm in-order to gain optimal weights to solve the local minima problem and also to enhance the convergence rate. …”
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Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
Published 2024“…This finding emphasizes that Stacking with Gradient Boosting provides much better performance in water quality classification compared to other models. This research provides new insights into the application of machine learning algorithms for water quality management as well as guidance for optimal algorithm selection.…”
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Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering
Published 2024“…Particularly, swmcan addresses multi-view data noise using the l1-norm and optimizes the objective function through a novel iterative reweighted method. …”
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Software defect prediction framework based on hybrid metaheuristic optimization methods
Published 2015“…For the purpose of this study, ten classification algorithms have been selected. The selection aims at achieving a balance between established classification algorithms used in software defect prediction. …”
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Optimal power flow based on fuzzy linear programming and modified Jaya algorithms
Published 2017“…Meanwhile, the proposed QOJaya algorithm produces better results than the basic Jaya method in terms of solution optimality and convergence speed. …”
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Hybrid Henry Gas-Harris Hawks comprehensive-opposition algorithm for task scheduling in cloud computing
Published 2025“…This method is based on two elements: comprehensive opposition-based learning (COBL) and Harris Hawks Optimization (HHO). …”
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Optimize and deploy machine learning algorithms on embedded devices for manufacturing applications
Published 2025“…This proposal discusses the techniques of optimizing and deploying machine learning algorithms on embedded devices for manufacturing applications; We investigate problems of printed circuit board (PCB) defects and artificial intelligence in embedded system. …”
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Genetic algorithm based ensemble framework for sentiment analysis
Published 2018“…Extending the concept of ensemble classifiers, this research applies the concept on the feature extraction and feature selection steps too, creating a multilayered ensemble of the three main tasks in machine learning sentiment analysis. Since there are many methods involved in each task of the multilayered ensemble, genetic algorithm is added to optimize the overall framework in order to select the optimal combinations of methods in each layer that can produce satisfactory results. …”
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Defects identification on semiconductor wafer for yield improvement using machine learning / Pedram Tabatabaeemoshiri
Published 2025“…The method uses weighted edges to represent the likelihood of defect propagation between dies, optimized through extensive experimentation, followed by an iterative label propagation process to uncover hidden defects. …”
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Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
Published 2020“…We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. …”
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Modeling, Testing and Experimental Validation of Laser Machining Micro Quality Response by Artificial Neural Network
Published 2009“…Therefore, prediction of laser machining cut quality, namely surface roughness was carried out using machine learning techniques based on Quick Back Propagation Algorithm using ANN. …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing
Published 2024“…While the firefly algorithm solution is superior, it has a higher time complexity compared to other algorithms used when there are more hidden layers and neurons. …”
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An adaptive HMM based approach for improving e-Learning methods
Published 2023“…This adaptive algorithm can thus be applied to any e-learning platform for optimal content delivery to its users in real-time. © 2014 IEEE.…”
Conference Paper -
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…In fact a data clustering method is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on our proposed algorithm; which is Enhanced Binary Particle swarm Optimization (EBPSO), (ii) To mine data using various data chunks (windows) and overcome a failure of single clustering. …”
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Prediction analysis of COVID-19 in Selangor by using Backpropagation Algorithm with Conjugate Gradient Method
Published 2024“…The Fletcher-Reeves approach can improve the efficiency of the backpropagation algorithm by having a faster convergence rate than other methods such as the scaled conjugate gradient method. …”
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Prediction analysis of COVID-19 in Selangor by using backpropagation algorithm with conjugate gradient method
Published 2024“…The Fletcher-Reeves approach can improve the efficiency of the backpropagation algorithm by having a faster convergence rate than other methods such as the scaled conjugate gradient method. …”
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