Home / Journals Mathematical Modelling and Applications / Machine Learning and Its Applications in Engineering
Machine Learning and Its Applications in Engineering
Submission Deadline: Sep. 30, 2019

This special issue currently is open for paper submission and guest editor application.

Join as Guest Editor Submit to Special Issue
Lead Guest Editor
YOUNESS EL HAMZAOUI
Universidad Autónoma del Carmen, Carmen, Mexico
Guest Editors
  • JUAN ANTONIO ALVAREZ ARELLANO
    UNIVERSIDAD AUTONOMA DEL CARMEN, Carmen, Mexico
  • AGUSTIN PEREZ RAMIREZ
    UNIVERSIDAD AUTONOMA DEL CARMEN, Carmen, Mexico
  • MANUEL MAY ALARCON
    UNIVERSIDAD AUTONOMA DEL CARMEN, Carmen, Mexico
  • UNIVERSIDAD AUTONOMA DEL CARMEN, Carmen, Mexico
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
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.
Published Papers
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.

Special Issue Flyer (PDF)

Please download to know all details of the
Special Issue

Introduction
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:

  1. Deep learning technologies and its application
  2. Artificial Intelligence Techniques on Software Engineering
  3. Intelligent decision support systems
  4. Big data software and applications in smart energy, smart business and smart cities
  5. Smart grid using advanced machine learning
  6. Data analytics
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186