Search Results - (( time evaluation method algorithm ) OR ( pattern optimization method algorithm ))
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Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification
Published 2013“…Traditional training algorithms have some drawbacks such as local minima and its slowness.Therefore, evolutionary algorithms are utilized to train neural networks to overcome these issues.This research tackles the ANN training by adapting Mussels Wandering Optimization (MWO) algorithm.The proposed method tested and verified by training an ANN with well-known benchmarking problems.Two criteria used to evaluate the proposed method were overall training time and classification accuracy.The obtained results indicate that MWO algorithm is on par or better in terms of classification accuracy and convergence training time.…”
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Optimization of multipurpose reservoir operation using evolutionary algorithms / Mohammed Heydari
Published 2017“…One of the main problems of this method is premature convergence and to improve this problem, the compound of the particle swarm algorithm and genetic algorithm were evaluated. …”
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Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics
Published 2018“…Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm intelligence algorithm in solving optimization problems.It has noticeably shown better performance compared to the state-of-art algorithms.This study proposes a novel hybrid ABC algorithm with β-Hill Climbing (βHC) technique (ABC-βHC) in order to enhance the exploitation and exploration process of the ABC in optimizing carbon nanotubes (CNTs) characteristics.CNTs are widely used in electronic and mechanical products due to its fascinating material with extraordinary mechanical,thermal,physical and electrical properties. …”
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Thesis -
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In comparison to different single algorithms for feature selection,experimental results show that the proposed ensemble method is able to reduce dimensionality, the number of irrelevant features and produce reasonable classifier accuracy. …”
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Low Complexity Error Correction in Low Density Parity Check (LDPC) Code Decoder and Encoder for Decode and Forward Cooperative Wireless Communication
Published 2021“…In this model, eight different LU and QR encoding method variants are evaluated in terms of execution time, the number of nonzero, and the pattern of nonzero. …”
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Optimal placement and sizing of distributed generation in radial distribution networks using particle swarm optimization and forward backward sweep method
Published 2012“…The proposed PSO algorithm is used to determine optimal placement and size of DG in radial distribution networks, where Forward Backward Sweep Method (FBSM) of distribution load flow analysis was used, to determine the actual power loss in the system. …”
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Disparity map algorithm using hierarchical of bitwise pixel differences and segment-tree from stereo image
Published 2024“…The algorithm development can be categorized into local, global, and semi-global methods. …”
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Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…The first method is the Binary Tree Growth Algorithm (BTGA), which implements a hyperbolic tangent function to convert the Tree Growth Algorithm into the binary version. …”
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Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures
Published 2020“…For these reasons, to improve the time and accuracy of the coverage in population-based meta-heuristics and their utilization in HPAs, this thesis presents a novel optimization algorithm called the Raccoon Optimization Algorithm (ROA). …”
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Disparity map algorithm for stereo matching process using local based method
Published 2022“…These input images have different viewpoints that corresponds with each other, forming a two-dimensional mapping of matching pixels and is known as disparity map. The SVDM algorithm can be categorized into local, semi global and global methods. …”
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Development of a multi criteria decision support system using convolutional neural network and jaya algorithm for water resources management / Chong Kai Lun
Published 2021“…The developed models were applied to three different case studies to evaluate the performance of the models. The results showed that the proposed model could capture patterns of the monthly and daily interval of the hydrological time series. …”
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Optimize and deploy machine learning algorithms on embedded devices for manufacturing applications
Published 2025“…On the other hand, artificial intelligence had been ways to enhances embedded system by enabling real-time, accurate detection and management of PCB defects through advanced pattern recognition and automated inspection methods. …”
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Final Year Project / Dissertation / Thesis -
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New global maximum power point tracking and modular voltage equalizer topology for partially shaded photovoltaic system / Immad Shams
Published 2022“…The proposed method is hybridized with a constant impedance method to improve the system's response time for fast varying load variations. …”
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Computational dynamic support model for social support assignments around stressed individuals among graduate students
Published 2020“…The algorithm complexity analysis was used to measure the execution time in the worst case. …”
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Photogrammetric low-cost unmanned aerial vehicle for pothole detection mapping / Shahrul Nizan Abd Mukti
Published 2022“…These results confirmed that the UAV is a very useful form of technology in road maintenance applications, at the same time, contributing in alternative method of pothole information extraction. …”
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Adaptive Similarity Component Analysis in Nonparametric Dynamic Environment
Published 2011“…From a dimensionality reduction evaluation aspect, the average misclassification error of the proposed method in low-rank feature space is 9.6% and same error rate for three other well-known feature extraction methods is 21.21%. …”
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