TY  - JOUR
T1  - Effort Estimation using Hybridized Machine Learning Techniques for Evaluating Student’s
Academic Performance
AU - Kumar, Mukesh AU - Singh, A.J. 
JO  - Asian Journal of Information Technology
VL  - 19
IS  - 4
SP  - 82
EP  - 87
PY  - 2020
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2020.82.87
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2020.82.87
KW  - Effort estimation
KW  -random forest
KW  -support vector machine
KW  -Naive-Bayes
KW  -MATLAB
KW  -artificial neural network
AB  - Machine Learning (ML) Technology is an area
of Artificial Intelligence (AI) to facilitate computer
systems with the capability to involuntarily learn and
improve from user experience without being explicitly
programmed from outside. It mainly focuses on the
expansion of computer programs that can access data
from its user to learn for themselves. With this
technology, we can predict the performance of the student
in academic. Here, we are implemented Naive-Bayes
(NB), Support Vector Machine (SVM), Random Forest
(RF) and Artificial Neural Network (ANN) algorithms to
find the total effort required for analysis. We proposed a
Hybridized Support Vector Machine-Neural Network
(SVM-NN) Machine Learning Algorithm which requires
less effort to accurately analysis the student’s academic
performance.
ER  -