| Title | An Optimization driven – Deep Belief Neural Network Model for Prediction of Employment Status after Graduation |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Premalatha, N., Sujatha, S. |
| Conference Name | 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) |
| Keywords | belief networks, Chimp Optimization, classifiers, Data models, Deep Belief Neural Network, Employability, Employment, Industries, Measurement, Metrics, Neural networks, Performance Metrics, Predictive models, pubcrawl, Training |
| Abstract | Higher education management has problems producing 100% of graduates capable of responding to the needs of industry while industry also is struggling to find qualified graduates that responded to their needs in part because of the inefficient way of evaluating problems, as well as because of weaknesses in the evaluation of problem-solving capabilities. The objective of this paper is to propose an appropriate classification model to be used for predicting and evaluating the attributes of the data set of the student in order to meet the selection criteria required by the industries in the academic field. The dataset required for this analysis was obtained from a private firm and the execution was carried out using Chimp Optimization Algorithm (COA) based Deep Belief Neural Network (COA-DBNN) and the obtained results are compared with various classifiers such as Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF). The proposed model outperforms other classifiers in terms of various performance metrics. This critical analysis will help the college management to make a better long-term plan for producing graduates who are skilled, knowledgeable and fulfill the industry needs as well. |
| DOI | 10.1109/ICEEICT53079.2022.9768657 |
| Citation Key | premalatha_optimization_2022 |
An Optimization driven – Deep Belief Neural Network Model for Prediction of Employment Status after Graduation