Multi-label classification of pH levels using support vector machines

Multi-label classification of pH levels using support vector machines

This paper developed an intelligent system application for the multi-label classification of pH levels. The pH is a measure of how acidic or how basic a substance is. The use of supervised learning methods may serve as a cheaper and more reliable alternative for pH level measurement. In this study,...

Full description

Saved in:

Bibliographic Details
Main Authors:	Luta, Raphael Benedict G., Baldovino, Renann G., Bugtai, Nilo T.
Format:	text
Published:	Animo Repository 2019
Subjects:	Hydrogen-ion concentration—Measurement Support vector machines Chemistry Manufacturing
Online Access:	https://animorepository.dlsu.edu.ph/faculty_research/3454 https://animorepository.dlsu.edu.ph/context/faculty_research/article/4456/type/native/viewcontent/HNICEM.2018.8666299
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	De La Salle University

Similar Items

Classification and determination of pH value: A decision tree learning approach
by: Chioson, Francheska B., et al.
Published: (2019)

A noncontact pH level sensing indicator using computer vision and knowledge-based systems
by: Luta, Raphael Benedict G., et al.
Published: (2017)

Hydrogen interaction on TM-decorated silicene
by: Narido, Shaira Jehsarine C.
Published: (2017)

An extended Lagrangian support vector machine for classifications
by: Yang, X., et al.
Published: (2014)

Saliency analysis of support vector machines for gene selection in tissue classification
by: Cao, L., et al.
Published: (2016)

Question Classification using Support Vector Machines
by: Zhang, D., et al.
Published: (2013)

Prediction of transporter family from protein sequence by support vector machine approach
by: Lin, H.H., et al.
Published: (2014)

Support vector machine classification of physical and biological datasets
by: Cai, C.-Z., et al.
Published: (2014)

Neural network controller for pH neutralization process
by: Lacar, Honorina L., et al.
Published: (2003)

Classification of landcover from combined LiDAR and orthophotos using support vector machine
by: Pula, Rolando A., et al.
Published: (2019)

Prediction of antifungal activity with support vector machine
by: Li, Z.-R., et al.
Published: (2014)

Support vector machines model for classification of thermal error in machine tools
by: Ramesh, R., et al.
Published: (2014)

Convergence of the extended Lagrangian support vector machine
by: Yang, X.W., et al.
Published: (2014)

Enzyme Family Classification by Support Vector Machines
by: Cai, C.Z., et al.
Published: (2014)

Learning with support vector machines for query-by-multiple-examples
by: Dell, Z., et al.
Published: (2013)

Classification models for HERG potassium channel inhibitors based on the support vector machine approach
by: Li, P., et al.
Published: (2014)

Incorporating prior-knowledge in support vector machines by kernel adaptation
by: Veillard, A., et al.
Published: (2013)

Extraction of brain tumor from MR images using one-class support vector machine
by: Zhou, J., et al.
Published: (2014)

Extraction of brain tumor from MR images using one-class support vector machine
by: Zhou, J., et al.
Published: (2016)

A pH regulation system for perfusion machine using computer vision and fuzzy logic
by: Limchesing, Tristan Joseph C., et al.
Published: (2019)

Stability regions for constrained nonlinear systems and their functional characterization via support-vector-machine learning
by: Ong, C.J., et al.
Published: (2014)

Image preprocessing using quick color averaging approach for color machine vision (CMV) systems
by: Luta, Raphael Benedict G., et al.
Published: (2017)

Kernel methods for the incorporation of prior-knowledge into support vector machines
by: VEILLARD ANTOINE PAUL MITSUMASA
Published: (2013)

Supervised and unsupervised machine learning for side-channel based Trojan detection
by: Jap, Dirmanto, et al.
Published: (2017)

Multiclassification of tool wear with support vector machine by manufacturing loss consideration
by: Sun, J., et al.
Published: (2014)

Flood stage forecasting with support vector machines
by: Liong, S.-Y., et al.
Published: (2014)

Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine
by: Chanin Nantasenamat, et al.
Published: (2011)

Investigation into the use of support vector machine for -omics applications
by: GUO YANGFAN
Published: (2012)

Thermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network - Support vector machine model
by: Ramesh, R., et al.
Published: (2014)

Bayesian approach to support vector machines
by: CHU WEI
Published: (2010)

A fast iterative nearest point algorithm for support vector machine classifier design
by: Keerthi, S.S., et al.
Published: (2014)

Reinterpreting the application of gabor filters as a manipulation of the margin in linear support vector machines
by: Ashraf A.B., et al.
Published: (2018)

Time series forecasting using wavelet and support vector machine
by: FONG KENG MUN
Published: (2010)

An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels
by: Lee, M.M.S., et al.
Published: (2014)

PROGNOSTICS AND HEALTH MANAGEMENT WITH SUPPORT VECTOR MACHINES CONSIDERING IMBALANCED DATA
by: JOSEY MATHEW
Published: (2019)

Saliency analysis of support vector machines for feature selection
by: Tay, F.E.H., et al.
Published: (2014)

Hidden Logistic Linear Regression for Support Vector Machine based phone verification
by: Li, B., et al.
Published: (2013)

Determination of global minima of some common validation functions in support vector machine
by: Yang, J.-B., et al.
Published: (2014)

Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression
by: Sean Andrei, P. Co, et al.
Published: (2023)

Splice Site Prediction Using Support Vector Machines with a Bayes Kernel
by: ZHANG, Ya, et al.
Published: (2006)