Min-Max Machine Learning Estimation Model with Big Data Analytics in Industry-Education Fusion
Keywords:
Industry-Education Fusion, Machine Learning, Big Data Analytics, Probabilistic Classifier, FeaturesAbstract
An industry-education fusion model is a strategic framework that seeks to create a symbiotic relationship between educational institutions and industries to better prepare students for the workforce and drive economic growth through innovation and collaboration.Big data analytics plays a significant role in the industry-education fusion model by facilitating the alignment of educational programs with industry needs, improving student outcomes, and fostering innovation. This paper concentrated on the evaluation of industry-education fusion with the use of machine learning-based big data analytics. To examine the contribution with the use of min-max computation in industry-education fusion strategy. The effective performance is achieved with the proposed min-max probabilistic Classifier (Min-Max_PC). With the proposed Min-Max_PC the features associated with the student performance are computed through min-max estimation. Based on the min-max estimation the features are evaluated and the probabilistic model is computed with big data analytics. The constructed Min-Max_PC is estimated with the fusion strategy for the evaluation of the student performance with industry performance and contribution. The simulation analysis expressed that the proposed Min-Max_PC model achieves a higher classification accuracy of 0.989. The results concluded that industry-education fusion exhibits improved performance of students.
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