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Papers should be formatted according to the guidelines for authors (see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=389). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.
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This special issue aims to develop machine learning techniques of supervised analysis with continuous dependent variable and continuous independent variables, developing the regression model including the phases of identification, estimation, diagnosis and prediction. Among the types of regression models incorporated, the following stand out:Multiple linear regression modelStandard Least-Squares FitGeneralized Linear ModelsStepwise RegressionRobust RegressionRidge RegressionLasso and Elastic NetWide Data via Lasso and Parallel ComputingLasso RegularizationLasso and Elastic Net with Cross ValidationLinear Mixed-Effects ModelFit Mixed-Effects Spline Regression, Non linear multiregression and Applications.
Aims and Scope:
Deep learning technologies and its application
Artificial Intelligence Techniques on Software Engineering
Intelligent decision support systems
Big data software and applications in smart energy, smart business and smart cities