Search Results - (( variable estimation using algorithm ) OR ( using composition using algorithm ))
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1
A modified strength capacity for composite slab using reliability approach
Published 2016“…Similarly, a procedural algorithm lead to the development of pro_led composite slab strength determination function for both longitudinal shear estimation methods by considering section slenderness and deck characteristics. …”
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Thesis -
2
Analysis and Optimization of Ultrasound-Assisted Alkaline Palm Oil Transesterification by RSM and ANN-GA
Published 2017“…The obtained results were then predicted by an optimized artificial neural network-genetic algorithm (ANN-GA) algorithm. The estimated results were compared with the experimental results. …”
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3
Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption
Published 2015“…The incremental back propagation algorithm demonstrated the best results and which has been used as learning algorithm for ANN in combination with Genetic Algorithm in the optimization. …”
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4
On the earthquake distribution modeling in Sumatra by Cauchy cluster process : comparing log-linear and log-additive intensity models
Published 2023“…In this study, we consider the Cauchy cluster process with the log-additive intensity model to analyze the distribution of major earthquake occurrences in Sumatra, Indonesia. The estimation procedure follows the standard two-step estimation technique, where the first step adapts the method for the Generalized Additive Models (GAMs) using penalized iteratively reweighted least squares (PIRLS) algorithm, and the second step employs the second-order composite likelihood. …”
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5
Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique
Published 2015“…The fixed block matching uses the same block size throughout the motion estimation process while the variable block matching uses different block size. …”
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Development of Fluid Properties Correlation For Malaysian Crude
Published 2013“…The dataset were established and analyzed using summed contributions from each variable. Based on the result, this model give better estimation by having lower average relative error compare to the previous one. …”
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Final Year Project -
8
Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data
Published 2022“…The reweighted fast, consistent and high breakdown (RFCH) estimator is a multivariate procedure used to estimate the robust location and scatter matrix. …”
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The selection of the Bayesian coincident-index models using model comparison criterion with application in Langat river water quality data
Published 2012“…The S-W index approach is commonly used to estimate parameters using sufficient time series data in composite index. …”
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11
Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
Published 2022“…The suitable independent variables (hL, DBH, and CPA) were vital to estimating the dependent variable (Sc) and producing a carbon stock map for the final result. …”
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12
A comparative study on aviation arrival delay prediction using machine learning methods
Published 2023“…Additionally, the research extends its modelling efforts to assess the viability of constructing a predictive model that can be effectively applied across multiple years and concludes that the 2016 ANN model using four common significant variables from literature review approach and stepwise regression approach can produce F1 scores that exceed 0.81 when used to predict test set data from years 2017 to 2019. …”
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13
Prediction on the mechanical strength of coal ash concrete using artificial neural network
Published 2022“…This study uses a dataset that contains information on the composition of 105 distinct types of materials. …”
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Conference or Workshop Item -
14
A hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation plant
Published 2023“…This paper presents a new approach of gas emission estimation in power generation plant using a hybrid Genetic Algorithm (GA) and Linear Regression (LR) (denoted as GA-LR). …”
Conference paper -
15
Experimental analysis and data-driven machine learning modelling of the minimum ignition temperature (MIT) of aluminium dust
Published 2022“…A machine learning artificial neural network approach with Levenberg-Marquardt algorithm is implemented to obtain the predictive model for MIT of aluminium dust for both the particle size ranges (100â��63 µm, 50â��32 µm). …”
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Experimental analysis and data-driven machine learning modelling of the minimum ignition temperature (MIT) of aluminium dust
Published 2022“…A machine learning artificial neural network approach with Levenberg-Marquardt algorithm is implemented to obtain the predictive model for MIT of aluminium dust for both the particle size ranges (100â��63 µm, 50â��32 µm). …”
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Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search
Published 2014“…These problems resulted in estimation errors and high computational cost. Hybrid genetic algorithm (GA) and Nelder-Mead local search mode 1 has been used to minimize demand estimation errors. …”
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18
Robust Estimation Methods And Outlier Detection In Mediation Models
Published 2010“…The Ordinary Least Squares (OLS) method is often use to estimate the parameters of the mediation model. …”
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19
Aeronautical revenues optimisation model (AROM) for regional airports via airside operations stochastic baseline matrix analysis / Wan Mazlina Wan Mohamed
Published 2016“…Finally, the research embarks on developing a graphical user interface (GUI) tool based on the model to estimate the possible potential aeronautical revenues that could be generated which will be useful for airport managers in their decision making. …”
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20
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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