CrossRef Text and Data Mining
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A Comparative Analysis of the Forecasting Performance of Coal and Iron Ore in Gwangyang Port Using Stepwise Regression and Artificial Neural Network Model
Sang-Ho Cho, Hyung-Sik Nam, Ki-Jin Ryu, Dong-Keun Ryoo
J Navig Port Res. 2020;44(3):187-194.   Published online June 1, 2020
DOI: https://doi.org/10.5394/KINPR.2020.44.3.187

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Improving Flood Forecasting in a Developing Country: A Comparative Study of Stepwise Multiple Linear Regression and Artificial Neural Network
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ENHANCING THE ACCURACY OF MALAYSIAN HOUSE PRICE FORECASTING: A COMPARATIVE ANALYSIS ON THE FORECASTING PERFORMANCE BETWEEN THE HEDONIC PRICE MODEL AND ARTIFICIAL NEURAL NETWORK MODEL
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Prediction of Iron Ore Sintering Characters on the Basis of Regression Analysis and Artificial Neural Network
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Development of Traffic Volume Forecasting Using Multiple Regression Analysis and Artificial Neural Network
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Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network
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Model dissection from earthquake time series: A comparative analysis using modern non-linear forecasting and artificial neural network approaches
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A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions
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Comparison of Artificial Neural Network Models and Multiple Linear Regression Models in Cargo Port Performance Prediction
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Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network
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Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada
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