Search Results - (( based optimization method algorithm ) OR ( pattern operations using 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 multipurpose reservoir operation using evolutionary algorithms / Mohammed Heydari
Published 2017“…An improved particle swarm algorithm (HPSOGA) is used to solve complex problems of water resources optimization. …”
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Performance optimization on axial-flux permanent magnet coreless generator using novel hybrid computational method based on genetic algorithm and pattern search / Lok Choon Long
Published 2016“…Complex real-world problems can be solved by heuristic optimization efficiently. Improved hybrid optimization method using Pattern Search (PS) and Genetic Algorithm (GA) onto Axial-Flux Permanent Magnet (AFPM) Coreless generator is presented in this thesis, and the optimization is based on the popular multi-objective sizing equation. …”
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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“…By using the optimization min-sum belief propagation approach, a low complexity min-sum (MS) based decoding algorithm called Variable Global Optimization Min-Sum (VGOMS) has been developed. …”
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Optimal short term load forecasting using LSSVM and improved BFOA considering Malaysia pandemic disrupted situation
Published 2024“…The IBFOA is proposed by modifying the chemotaxis process in BFOA using a Sine Cosine Algorithm (SCA), which improves the convergence speed and accuracy of the algorithm. …”
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A generalized laser simulator algorithm for optimal path planning in constraints environment
Published 2022“…A statistical test was used to demonstrate the efficiency of the proposed algorithm over the compared 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“…Apart from that, having low values in four of the performance criteria: RMSE, MAE, NSE, and RSR, have further strengthened the credibility of the results. As for the optimization process, the reservoir operation rule was derived using a meta-heuristic algorithm at the monthly interval. …”
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A novel hybrid photovoltaic current prediction model utilizing singular spectrum analysis, adaptive beluga whale optimization, and improved extreme learning machine
Published 2025“…The numerical experimental results demonstrated that the ABWO algorithm beat all other optimization methods and could solve the majority of the benchmark functions. …”
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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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New global maximum power point tracking and modular voltage equalizer topology for partially shaded photovoltaic system / Immad Shams
Published 2022“…In terms of controller-based, in this work, a new global maximum power point tracking (GMPPT) algorithm based on a modified butterfly optimization algorithm (MBOA) has been proposed. …”
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Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer
Published 2025“…Accurate forecasting of chiller power consumption is essential for optimizing building energy management systems and reducing operational costs. Despite advances in deep learning, existing forecasting models often struggle with the complex temporal dependencies and non-linear patterns in chiller operation data. …”
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Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…The second method is called the Modified Binary Tree Growth Algorithm (MBTGA) that applies swap, crossover, and mutation operators. …”
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Image watermarking optimization algorithms in transform domains and feature regions
Published 2012“…A series of training patterns are constructed by employing between two images.Moreover,the work takes accomplishing maximum robustness and transparency into consideration.HPSO method is used to estimate the multiple parameters involved in the model. …”
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Correlation analysis and predictive performance based on KNN and decision tree with augmented reality for nuclear primary cooling process / Ahmad Azhari Mohamad Nor
Published 2024“…These analyses provide nuanced insights into system operational dynamics and efficiency. Subsequently, predictive models employing k-nearest neighbour and decision tree algorithms are constructed and evaluated based on accuracy, precision, and recall metrics. …”
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Adaptive Similarity Component Analysis in Nonparametric Dynamic Environment
Published 2011“…Data arrives from operational field in a stream model and similarity-based classification algorithms must identify them with acceptable performance. …”
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The conceptual framework of knowledge of large scale and incomplete graphs of skyline queries optimization using machine learning
Published 2025“…The implemented algorithm to perform such operation was far more efficient, faster and accurate as compared to conventional methods. …”
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Proceeding Paper -
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Condition monitoring of deep drilling process for cooling channel making in hot press die
Published 2016“…Deep drilling operation is one of the major process that is widely used in the manufacturing industry. …”
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Undergraduates Project Papers -
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Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization
Published 2015“…Traditionally, petroleum and natural gas engineers rely on “rules-of-thumb” in the selection of optimal features with much disregard to the hidden patterns in operational data. …”
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