Machine Learning and Virtual Reality

Abstract

The study focuses on data produced by social media users. It applies the El Saddik communication structure between a digital twin and a physical twin to the setting of understanding and personality identification in people. Depending on the requirements, real twins use sensors to mimic their senses of sight, hearing, taste, smell, and touch. Users' posting and liking activities may be tracked in online social networks to get a sense of and an understanding of their personalities. Digital twins should have a deep learning-enhanced controller to act quickly and wisely on behalf of their physical counterparts. The approach combines user content posting and like behavior for personality prediction. Depending on the application, digital twins can be represented virtually as a social humanoid robot or a software element without a physical replica. The status messages uploaded by each user are pooled as a document. The work enhances security by 17.4%.