The Knowledge System of Composite Talents Based on the Development of Virtual Digital People

Abstract

The present research analyzes how digital technology might enhance the talent process, including talent identification, selection, and retention. Talent development is the systematic process of cultivating people's talents and abilities in order to maximize their potential. It entails offering specialized training, tools, and opportunities for advancement to help individuals flourish in their professions and develop their careers. In this study, The Knowledge System of Composite Talents Based on the Development of Virtual Digital People (KS-CT-DVDP-QSANN) is proposed. Initially, the data gathered from development talent upwork dataset. Collected data are pre-processed to include filtering, normalization and improve the successive stages of talent data using Regularized bias-aware ensemble Kalman filter (RBAEKF).In general, Quantum Self-Attention Neural Networks predict composite talents. Hence, proposed utilize Red panda optimization algorithm (RPOA)enhance Quantum Self-Attention Neural Networks (QSANN)accurately predict the composite talents. Then, the KS-CT-DVDP-QSANN is implemented to Python and the performance metrics such as, Recall, mean square error, Accuracy, precision, F1-score, and error rate. Finally, the performance of KS-CT-DVDP-QSANN method provides 19.87%, 24.57% and 34.15% high accuracy, 23.17%, 25.42% and 29.28% higher Precision and 23.63%, 28.37% and 27.23% higher recall while compared with existing Intelligent talent: How to identify, select, and retain talents from around the world using artificial intelligence (IR-IS-RTF-AI), An innovative talent training mechanism for maker education in colleges and universities based on the IPSO-BP-enabled technique (ITTE-IPSO-BPNN) and  Adaptive talent journey: Optimization of talents' growth path within a company via Deep Q-Learning (TGPC-DQL) respectively.