Search Results - (( parameter evaluation method algorithm ) OR ( variables selection method algorithm ))
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Model selection approaches of water quality index data
Published 2016“…Automatic model selection by using algorithm can avoid huge variability in model specification process compared to manual selection.With the employment of algorithm, the right model selected is then also used for forecasting purposes. …”
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Deterministic Image Enhancement Based on Fuzzy Method
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Proceedings -
3
Information Theoretic-based Feature Selection for Machine Learning
Published 2018“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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Thesis -
4
Extremal region detection and selection with fuzzy encoding for food recognition
Published 2019“…The second algorithm reduces the quantity of interest regions by using the Extremal Region Selection (ERS) algorithm. …”
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5
Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…The performance of ANNs depend on many factors, including the network structure, the selection of activation function, the learning rate of the training algorithm, and initial synaptic weight values, the number of input variables, and the number of units in the hidden layer. …”
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6
Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…Furthermore, a systematic feature selection approach was proposed in which search algorithms (Ant-Search, Best First-Search and Particle Swamp Optimization (PSO) - Search) performance were evaluated to select the most significant features. …”
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7
Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification
Published 2020“…The results indicated that good classification performance depends on these factors. All algorithms showed more stability and accuracy when training size applied is more than 6% by the Equal Sample Rate (ESR) method with six variables. …”
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8
Evaluation of lightning return stroke current using measured electromagnetic fields
Published 2012“…On the other hand, indirect methods can evaluate the return stroke current using measured fields and they can cover a greater number of lightning occurrences compared to direct measurement. …”
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9
Variable Neighborhood Descent and Whale Optimization Algorithm for Examination Timetabling Problems at Universiti Malaysia Sarawak
Published 2025“…A constructive algorithm was developed to generate an initial feasible solution, which was subsequently refined using two primary approaches to evaluate their efficiency: Iterative Threshold Pipe Variable Neighborhood Descent (IT-PVND), and a modified Whale Optimization Algorithm (WOA). …”
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10
Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh
Published 2019“…This technique is selected for the treatment such a non-linear function relationship among the variables. …”
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11
Perturbation stochastic model updating of a bolted structure / Mohamad Azam Shah Aziz Shah
Published 2022“…The SMU method based on the improved model was used to predict the variability of the dynamic behaviour of the structure. …”
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12
Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani
Published 2018“…To evaluate the performance of the Weibull parameters’ estimator methods, two sets of data are considered, one based on simulated data with different random variable size and the other based on actual data collected from a wind farm in Iran. …”
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13
Optimization of Lipase Catalysed Synthesis of Sugar Alcohol Esters Using Taguchi Method and Neural Network Analysis
Published 2011“…In this system,similar insolvent system, three methods including one variable at a time, Taguchi method and ANN were used for optimization and prediction of percentage of conversion. …”
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14
Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms
Published 2023Article -
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The use of heuristic ordering and particle swarm optimization for nurse scheduling problem
Published 2017“…The comparison of the result of HOPSO, harmony search algorithm (HSA) and heuristic variable neighborhood search (HVNS) is presented. …”
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16
Model Prediction Of Pm2.5 And Pm10 Using Machine Learning Approach
Published 2021“…The model was developed as a generic use where data pre-processing using two separate methods of calculating a correlation coefficient and variable importance in projection (VIP) scores managed to select significant input toward output for model development. …”
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Monograph -
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Neural network based adaptive pid controller for shell-and-tube heat exchanger
Published 2019“…Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. Single hidden layer feed forward neural networks with 20 neurons in hidden layer was selected. …”
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Student Project -
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Neural network based adaptive pid controller for shell-and-tube heat exchanger: article
Published 2019“…Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. Single hidden layer feed forward neural networks with 20 neurons in hidden layer was selected. …”
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Article -
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Impacts of Butterworth post-filtering on absolute quantification of 99mTc SPECT/CT using nema phantom
Published 2024“…Using phantom measurements, this multi-vendor and multi-center study evaluated the quantitative accuracy and inter-system variability of different SPECT/CT systems, as well as the impact of patient size, processing software and reconstruction algorithms on recovery coefficients (RC). …”
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Monograph -
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Shear strength test on unsaturated soil using natural microscopic surface tension force / Basharudin Abdul Hadi
Published 2017“…There were two sets of algorithms in developing the UPLRB ANN model and every algorithm set consisted of model inputs data preparation, neural network 29 30 base of the specimen until the flow ceased..…”
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