Undergraduate engineering students employment prediction using hybrid approach in machine learning

Abstract

The knowledge discovery from student’s data can be very useful in predicting the employment under different categories. The machine learning is helping in this regard up to the great extent. In this paper, a hybrid model of machine learning has proposed to predict the jobs categories, students may get in their campus placement. The considered groups of students are from undergraduate courses from engineering stream having the semester’s scheme in their academic. The mapping of jobs has predicted based on their previous seven semesters marks as well as their personality index. The proposed hybrid model consists of three different model based on multilayer feed forward architecture, radial basis function neural network and K-means based clustering method. The proposed model provided the relative chances of available each job category with high accuracy and consistency.

A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory

“…In next stage, radial basis function neural network (RBF NN) model is implemented for classification of the final attribute set. Krishnaiah and Kadegowda [26] introduced a hybrid approach for undergraduate engineering students employment prediction using hybrid approach in machine learning. Panda et al [27] proposed prediction of diabetes disease using machine learning algorithms and concluded K-nearest neighbor (KNN) works well for the dataset includes a large number of datasets that it is easier to minimize processing time.…”