Intelligent Platform for International Talent Cultivation using Multiverse Optimization Algorithm based Support Vector Machine

The fats growth of Artificial Intelligence (AI) advancement demands greater necessities for the engagement and talent training and talent cultivation. The combination of education and enterprises is a significant approach for addressing the mismatch among the industrial demand as well as talent supply. An aim of this article is to identify the new necessities for developing the AI-based approach for solving the talent management problems. Thus, this research proposes the Multiverse Optimization with Support Vector Machine (MVO-SVM) approach to the intelligent platform for international talent cultivation. The combination of MVO with SVM supports in fine-tuning hyperparameters, enhanced the model precision for talent selection and cultivation. The exploration and exploitation capabilities of the MVO approach allowed the SVM to maintain the large dataset based on the talent cultivation, learning procedure as well as career development, resulted in effective model performance. The experimental outcomes signifies that the introduced MVO-SVM approach attains the better accuracy of 91.63%, precision of 90.24%, recall of 89.33% and F1-score of 89.78% correspondingly as compared to the existing methods like conventional SVM.