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Prediction of pitting corrosion status of EN 1.4404 stainless steel by using a 2‐stage procedure based on support vector machines
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cem.1401openalex-snapshot
Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks
inferredcites →https://doi.org/10.1002/cem.2591openalex-snapshot
Breakdown potential modelling of austenitic stainless steel
inferredcites →https://doi.org/10.1002/maco.201307252openalex-snapshot
A comprehensive approach based on SVM to model pitting corrosion behaviour of EN 1.4404 stainless steel
inferredcites →https://doi.org/10.1007/978-1-4757-3264-1openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1016/j.commatsci.2007.04.017openalex-snapshot
Computational simulation of multi-pit corrosion process in materials
inferredcites →https://doi.org/10.1016/j.commatsci.2008.05.010openalex-snapshot
Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials
inferredcites →https://doi.org/10.1016/j.corsci.2006.06.023openalex-snapshot
Discovering key meteorological variables in atmospheric corrosion through an artificial neural network model
inferredcites →https://doi.org/10.1016/j.corsci.2008.03.016openalex-snapshot
Effect of temperature on the corrosion resistance and pitting behaviour of Alloy 31 in LiBr solutions
inferredcites →https://doi.org/10.1016/j.corsci.2008.04.005openalex-snapshot
Pitting corrosion behaviour of austenitic stainless steels – combining effects of Mn and Mo additions
inferredcites →https://doi.org/10.1016/j.corsci.2008.10.038openalex-snapshot
Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression
inferredcites →https://doi.org/10.1016/j.corsci.2009.10.024openalex-snapshot
Prediction of corrosion behavior using neural network as a data mining tool
inferredcites →https://doi.org/10.1016/j.corsci.2009.10.033openalex-snapshot
Comparison of influences of NaCl and CaCl2 on the corrosion of 11% and 17% Cr ferritic stainless steels during cyclic corrosion test
inferredcites →https://doi.org/10.1016/j.electacta.2008.02.047openalex-snapshot
Critical pitting and repassivation temperatures for duplex stainless steel in chloride solutions
inferredcites →https://doi.org/10.1016/j.enpol.2007.08.028openalex-snapshot
The energy benefit of stainless steel recycling
inferredcites →https://doi.org/10.1016/j.jcsr.2008.07.001openalex-snapshot
A method to predict pitting corrosion of stainless steels in evaporative conditions
inferredcites →https://doi.org/10.1016/j.jcsr.2008.07.011openalex-snapshot
Stainless steel in construction: A review of research, applications, challenges and opportunities
inferredcites →https://doi.org/10.1016/j.matchemphys.2008.11.016openalex-snapshot
Corrosion behavior of some austenitic stainless steels in chloride environments
inferredcites →https://doi.org/10.1016/j.mser.2009.03.001openalex-snapshot
Recent developments in stainless steels
inferredcites →https://doi.org/10.1016/j.patrec.2005.10.010openalex-snapshot
An introduction to ROC analysis
inferredcites →https://doi.org/10.1016/s0010-938x(02)00154-3openalex-snapshot
Influence of chemical composition on the pitting corrosion resistance of non-standard low-Ni high-Mn–N duplex stainless steels
inferredcites →https://doi.org/10.1016/s0010-938x(79)80096-7openalex-snapshot
The localized corrosion of resistant alloys in chloride solutions
inferredcites →https://doi.org/10.1016/s0010-938x(99)00024-4openalex-snapshot
Phenomenological modelling of atmospheric corrosion using an artificial neural network
inferredcites →https://doi.org/10.1016/s0013-4686(02)00841-1openalex-snapshot
Passive films on stainless steels—chemistry, structure and growth
inferredcites →https://doi.org/10.1016/s0261-3069(99)00026-6openalex-snapshot
Neural network methods for corrosion data reduction
inferredcites →https://doi.org/10.1109/72.788640openalex-snapshot
An overview of statistical learning theory
inferredcites →https://doi.org/10.1109/tsp.2011.6043693openalex-snapshot
The method for material corrosion modelling and feature selection with SVM-RFE
inferredcites →https://doi.org/10.1148/radiology.143.1.7063747openalex-snapshot
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
inferredcites →https://doi.org/10.1179/imr.1982.27.1.321openalex-snapshot
Corrosion resistance of austenitic Fe-Cr-Ni-Mo alloys in marine environments
inferredcites →https://doi.org/10.1198/jasa.2003.s269openalex-snapshot
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
inferredcites →https://doi.org/10.7551/mitpress/1130.001.0001openalex-snapshot
Advances in Kernel Methods
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4815795openalex-extracted
Integrating Double-Labelling Hcr-Fish into a Multidisciplinary Pipeline for Biofouling Assessment on Austenitic Stainless Steel in Brackish Seawater Circuit
inferred← citeshttps://doi.org/10.1002/maco.201810367openalex-snapshot
A support vector machine‐based ensemble algorithm for pitting corrosion modeling of EN 1.4404 stainless steel in sodium chloride solutions
inferred← citeshttps://doi.org/10.1016/j.eti.2024.103782openalex-snapshot
Integrating double-labeling HCR-FISH into a multidisciplinary pipeline for biofouling assessment on austenitic stainless steel in brackish seawater circuit
inferred← citeshttps://doi.org/10.1016/j.jelechem.2019.05.036openalex-snapshot
Determination of SS321 pitting stage in FeCl3 solution based on electrochemical noise measurement data using artificial neural network
inferred← citeshttps://doi.org/10.1016/j.jmst.2020.01.044openalex-snapshot
An improved deep forest model for forecast the outdoor atmospheric corrosion rate of low-alloy steels
inferred← citeshttps://doi.org/10.1016/j.measurement.2022.111592openalex-snapshot
Novel method for prediction of corrosion current density of gas pipeline steel under stray current interference based on hybrid LWQPSO-NN model
inferred← citeshttps://doi.org/10.1177/1478422x261450911openalex-snapshot
Prediction of corrosion rate and characterisation of influencing factors in process pipelines based on data mining
inferred← citeshttps://doi.org/10.3390/ma19101979openalex-snapshot
Machine Learning-Based Pitting Rate Classification and Prediction for 316L Stainless Steel in NaClO3 and NaCl Environment
inferred← citeshttps://doi.org/10.6060/ivkkt.20246703.6944openalex-snapshot
PREDICTION OF ELECTROCHEMICAL PROCESSES OF LOCAL DISSOLUTION OF STAINLESS STEELS. PART 1. SPECTRAL –FREQUENCY ANALYSIS OF POTENTIAL FLUCTUATIONS
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