Neural Network for Vocational Guidance Based on The Applicant's Profile For Admission To A Study Program

Abstract

The vocational orientation tests are currently oriented to established principles and methods, where the applicant identifies his competencies to choose his professional career, the model of university accreditation in its standard 18 reference on the admission to the program of studies and that must be in accordance with the profile of income raised by each program of studies. Artificial intelligence has become necessary through its techniques in the solution of some problems of human behavior simulated through electronic and digital media, so the objective of this research was to develop a neural network based on vocational guidance from the profile of income to ensure that the applicant knows his competencies towards a study program. For this case, it is considered the knowledge, skills and attitudes raised by the program of studies. This neural network considered in 3 incoming axons, 6 hidden layers and one outgoing axon, each nucleus considers sigmoidal activation functions while, for the backpropagation algorithm, functional derivatives of second level have been used, obtaining results with an accuracy of 0.996 correlation with respect to the real data and a constant variance between the tests of 0.01, guaranteeing its use to automate01, guaranteeing its use for automation. Data collection has been done through surveys based on a transversal non-experimental research design and using the applied correlational research level.