Search Results - (( java implementation bees algorithm ) OR ( parameter reduction factor algorithm ))
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The PID controller parameter tuning based on a modified differential evolutionary optimization algorithm for the intelligent active vibration control of a combined single link robo...
Published 2025“…The Crossover Probability Factor (CPF) as the Certain Ratio (CR) and the Mutation Factor (MF) of the algorithm are gradually altered during algorithm iteration to enhance the method's performance during optimization. …”
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Combined modal parameters-based index for damage identification in a beamlike structure: theoretical development and verification
Published 2011“…The new index called Combined Parameter Index (CPI) compares the factor of reduction in stiffness according to reduction in natural frequencies and also the factor of reduction in stiffness according to change in mode shape. …”
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Enhancing Harmony Search Metaheuristic Algorithm for Coverage Efficiency, Test Suite Reduction, and Running Time in Combinatorial Interaction Testing
Published 2025“…Despite HS’s tendency to get stuck in local optima, we dynamically adjust its parameter values in our proposed eHS. The experimental results demonstrate that eHS outperforms the other algorithms for CIT in terms of coverage efficiency and test suite reduction. …”
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Development of a phantom and metal artifact correction (MAC) algorithm for post-operative spine computed tomography (CT) imaging / Noor Diyana Osman
Published 2014“…The last part is the development of a metal artifact correction (MAC) algorithm and evaluation of the proposed algorithm in artifacts reduction in CT images. …”
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Efficient hybrid reduction for binary based information system in soft set theory
Published 2016“…HRSS consists of two(2) types of parameter reduction and a newly proposed object reduction. …”
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PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah
Published 2017“…Results suggest that the PSO algorithm is viable alternative to other established algorithms for LLS parameter estimation. …”
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Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology
Published 2015“…Additionally,analysis of variance(ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters, genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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Classification of basal stem rot disease in oil palm using dielectric spectroscopy
Published 2018“…An analysis was done to see the effect of implementing different data reduction algorithms in classifying BSR disease. The results showed that the impedance parameter was the best in classifying BSR severity levels compared to the other dielectric properties. …”
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Improved dung beetle optimization algorithm and finite element analysis for spindle optimization
Published 2024“…The obtained optimal parameters guide the construction of a finite element model, considering additional factors like stiffness and maximum stress. …”
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Increment of insecure RSA private exponent bound through perfect square RSA diophantine parameters cryptanalysis
Published 2022“…The public parameters of the RSA cryptosystem are represented by the pair of integers N and e. …”
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Optimization of super twisting sliding mode control gains using Taguchi method
Published 2018“…Optimized algorithm achieved 9.3% of reduction in root mean square of tracking error and 38.4% of reduction in chattering experimentally.…”
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Optimization of fast fourier transform based on twiddle factor using genetic algorithm on raspberry pi
Published 2019“…Over the years the Genetic Algorithms (GA) proved to be one of the best methods for optimization. …”
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Design of intelligent Qira’at identification algorithm
Published 2017“…The process of the SPAP Algorithm is to extend parameters of the Affine Projection Block with two different selections of windowing length that affect the final accuracy on pattern classification. …”
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Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization
Published 2022“…The ANN model is further improved using GA and PSO. Each algorithm has its own parameters and is further optimized using RSM. …”
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Study on sparseness effects over NMF applied for automatic text summarization
Published 2012“…While, storage capability relates to the extent of data reduction process achieved by NMF. The parametric values that serve as input to the NMF process include initialization method, rank of factorization, sparseness measure and maximum iteration. …”
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Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters
Published 2016“…Additionally,analysis of variance (ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters,genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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Transformer T-joint optimization using particle swarm optimization and hemisphere-shape design of the core
Published 2017“…The losses in transformers can be significantly reduced, especially in the core by improving the performance of the joint design. Several factors and parameters contribute to core losses such as shape of joint, gaps in between the joint parts, thickness of laminations, overlapping, orientation and number of laminations per stack. …”
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Techniques and strategies to minimize radiation exposure in pediatric computed tomography (CT) abdominal examinations: a review
Published 2024“…An in-depth review of 12 selected articles demonstrated the radiation dose reduction techniques and strategies, which include prefiltering and post-processing algorithms, careful adjustment of exposure parameters such as tube voltage (kVp) and current (mAs), and the establishment of diagnostic reference levels (DRL). …”
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Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…Also, self-adaptive scaling factor and crossover probability control parameters are introduced to diminish time of finding an optimal parameter to produce the best population. …”
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