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Item Metadata only Short-term load forecasting using feature selection techniques and support vector machines(Mahidol University. Mahidol University Library and Knowledge Center, 2011) Ratchanee Jadecharoen; Tanasanee Phienthrakul; Chuttchaval JeraputraPublication Metadata only Analysis of comparisons for forecasting gold price using neural network, radial basis function network and support vector regression(2014-01-01) Khanoksin Suranart; Supaporn Kiattisin; Adisorn Leelasantitham; Mahidol UniversityThis research is done to study and analyze the comparison for forecasting the gold price using Neural Network, Radial Basis Function Network, and Support Vector Regression. Which the neural network radial basis function network and support vectorPublication Metadata only Effectiveness of different spatial interpolators in estimating heavy metal contamination in shallow groundwater: a case study of arsenic contamination in Hanoi, Vietnam(2011-04) Pham Quy Giang; Kanchana Nakhapakorn; กาญจนา นาคะภากร; Achara Ussawarujikulchai; Mahidol University. Faculty of Environment and Resource Studiesin shallow groundwater, (2) generate risk map and compare effectiveness of different spatial interpolation approaches including Kriging, IDW and Radial Basis Function of Geographic Information System (GIS) in estimating arsenic concentrationPublication Metadata only Prediction of soil nitrogen content using E-nose and radial basis function(2019-01-18) Jigme Norbu; Theerapat Pobkrut; Treenet Thepudom; Thinley Namgyel; Teerayut Chaiyasit; Yu Thazin; Teerakiat Kerdcharoen; Mahidol University, e-nose coupled with Radial Basis Function (RBF) is employed to determine the amount of nitrogen (N) which is one of the main nutrients in the soil. The results demonstrate that not only does the e-nose clearly discriminate the odors of soilItem Metadata only Wireless sensor network system for microclimate and soil nutrients monitoring in precision agriculture(Mahidol University Library and Knowledge Center, 2024) Jigme Norbu; Teerakiat Kerdcharoen; Chatchawal Wongchoosukcapable of monitoring real-time microclimatic conditions, but also can adequately predict crop yield based on the acquired weather data. Additionally, the electronic nose coupled with radial basis function was found to efficiently predict the soil nitrogen... concentration with 96.2% accuracy, and that it has huge potential of offering an alternative to conventional soil testing methods. In the future, the capability of electronic nose and radial basis function in determining soil organic matter needs to be explored.Publication Metadata only Monte Carlo simulations of a magnesium ion in liquid ammonia(1992-05-15) Supot Hannongbua; Bernd M. Rode; Mahidol University; University of InnsbruckAn infinitely dilute solution of Mg(II) in ammonia solution is studied by Monte Carlo simulations for two temperatures, using a newly developed Mg(II)-ammonia potential function based on ab initio calculations with a DZP basis set. The structure... of the solution is described by radial and angular distribution functions (RDF). The first shell coordination number of 8 remains constant upon increasing the temperature from 235 to 277 K. A small peak is observed in the Mg(II)-nitrogen RDF between the firstPublication Metadata only Bagging of Duo Output Neural Networks for Single Output Regression Problem(2010-01-01) Somkid Amornsamankul; Pawalai Kraipeerapun; Mahidol University; Ramkhamhaeng Universityneural networks, and the ensemble of support vector machine with linear, polynomial, and radial basis function kernels. © 2010 IEEE.Publication Metadata only Solving regression problem with complementary neural networks and an adjusted averaging technique(2010-12-01) Pawalai Kraipeerapun; Sathit Nakkrasae; Chun Che Fung; Somkid Amornsamankul; Ramkhamhaeng University; Murdoch University; Mahidol University; South Carolina Commission on Higher Educationindustry. We found that our proposed technique provides better performance when compared to the traditional CMTNN, backpropagation neural network, and support vector regression with linear, polynomial, and radial basis function kernels. © 2010 SpringerPublication Metadata only Applying duo output neural networks to solve single output regression problem(2009-12-01) Pawalai Kraipeerapun; Somkid Amornsamankul; Chun Che Fung; Sathit Nakkrasae; Ramkhamhaeng University; Mahidol University; Murdoch Universitywith linear, polynomial, and radial basis function kernels. © 2009 Springer-Verlag Berlin Heidelberg.Publication Metadata only Quantitative structure-property relationship study of spectral properties of green fluorescent protein with support vector machine(2013-01-05) Chanin Nantasenamat; Kakanand Srungboonmee; Saksiri Jamsak; Natta Tansila; Chartchalerm Isarankura-Na-Ayudhya; Virapong Prachayasittikul; Mahidol University; Prince of Songkla University. Such descriptors were mapped onto a higher dimensional space via kernel functions (e.g. linear, polynomial and radial basis function kernels) and learning is then performed using SVM. The predicted spectral properties were well correlated with their experimental