Search Results - (( using spatial method algorithm ) OR ( (parameter OR parameters) optimization method algorithm ))
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1
SA-EVPS ALGORITHM FOR DISCRETE SIZE OPTIMIZATION OF THE 582-BAR SPATIAL TRUSS STRUCTURE
Published 2023“…Nowadays, approximate optimization methods are widely used. This study utilized the Self Adaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm as an approximate optimization method, since the EVPS algorithm requires experimental parameters. …”
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2
Kernerlized Correlation Filters Parameters Optimization For Enhanced Visual Tracking
Published 2017“…Until now, there are still no perfect algorithm to track the target flawlessly. In order to improve the performance, the main idea proposed is implementing optimization technique on the selected parameters and obtain a better performance. …”
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3
Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…The accuracy of each algorithm was evaluated using LibSVM, Bayes network, and Adaboost classifier. …”
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4
Impact of low-dose protocols on computed tomography of lung cancer screening on the intrinsic performance metrics: a phantom study
Published 2023“…Conclusions: To sum up, research is being conducted to determine how CT image quality is affected by low dose CT acquisition parameters and IR algorithm. It is essential to adjust IR levels accurately as higher levels can reduce image texture and influence NPS spatial frequency. …”
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5
Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…The second segmentation algorithm combines Delaunay triangulation clustering in the spatial domain and Particle Swarm Optimization (PSO). …”
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6
A joint Bayesian optimization for the classification of fine spatial resolution remotely sensed imagery using object-based convolutional neural networks
Published 2022“…A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. …”
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Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia
Published 2019“…The general objective of the study was the development of optimized hybrid debris flow models using airborne laser scanning data and Machine learning algorithms in Malaysia. …”
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8
Comparison between specifications of linear regression and spatial-temporal autoregressive models in mass appraisal valuation for single storey residential property
Published 2013“…Furthermore, various spatial, temporal and spatio-temporal neighbourhood and weighting schemes, optimization algorithms and lag and error modelling scenarios were created and tested with the data. …”
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9
Task-state EEG signal classification for spatial cognitive evaluation based on multiscale high-density convolutional neural network
Published 2022“…The MHCNN classification method proposed in this research could be used as an effective biological indicator of spatial cognitive training effect and could be extended to other brain function evaluations.…”
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10
Multimodality registration of two-dimensional echocardiography and cardiac CT for mitral valve diagnosis and surgical planning
Published 2024journal::journal article -
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Hospital site suitability assessment using three machine learning approaches: evidence from the Gaza strip in Palestine
Published 2021“…The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. …”
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12
Multimodality registration of two-dimensional echocardiography and cardiac CT for mitral valve diagnosis and surgical planning
Published 2024“…For spatial registration, an intensity-based normalized mutual information method was applied with a pattern search optimization algorithm to produce an interpolated cardiac CT image. …”
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13
Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh
Published 2020“…The training and parameters selection of the machine learning algorithms are conducted using EEG data collected from ten subjects in the laboratory. …”
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14
On-orbit spatial image characterisation and restoration based on stochastic characteristic targets / Wong Soo Mee
Published 2021“…The experimental results demonstrate that the proposed framework is practical and effective, with < 2.3% of relative error at the Nyquist frequency as compared to the well-established edge method. In continuation of the first framework, the proposed MTF measurement algorithms are evaluated experimentally as a blur kernel estimation method for spatially varying and invariant blur removal. …”
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15
Optimization of turning parameters using genetic algorithm method
Published 2008“…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
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Undergraduates Project Papers -
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Optimization of flood inundation simulation using multi-resolution Digital elevation model
Published 2022“…Additionally, the optimization was also validated experimentally using the hydraulic model. …”
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17
Optimization of turning parameters using ant colony optimization
Published 2008“…The project objectives are to develop Ant Colony Optimization (ACO) algorithm for CNC turning process and to optimize turning parameters for minimized production cost per unit. …”
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18
Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization
Published 2014“…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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19
Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis
Published 2018“…The efficiency of the three algorithms are evaluated and compared with previous results obtained by other optimization methods on similar studies. …”
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20
Time series modeling of water level at Sulaiman Station, Klang River, Malaysia
Published 2010“…The estimation of parameters of the model is accomplished using the hybrid learning algorithm consisting of standard neural network backpropagation algorithm and least squares method. …”
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