More than half of the universities have recently added advanced technical courses for their students. The specialised courses would be the backbone to keep the students’ knowledge updated. In that case, artificial intelligence (AI) and virtual reality (VR) have been lightened up recently. This paper introduces the basic concepts of AI and VR and the changes that can be made when they are implemented in real-time applications. Students will be more focused on the courses utilising virtual reality. This VR makes the visualisation of any challenging course easier. Many students have been working on virtual reality in their colleges, and their involvement shows the actual output in the upcoming years. The research was carried on the Virtual Reality Driving Simulator Dataset to implement the virtual reality method. The results are compared to the real world and show that the proposed model is 98.67% more accurate than the real world.

Visual communication concepts enable linguistics or semiotics to the teaching of visual communication designs, creating graphic designs into an innovative and scientific discipline. The use of storyline techniques in visual communication not only inspires the imagination of designer but also arouses the visual memory of the audience. Besides, improving cultural heritage such as historical images is important to protect cultural diversity. Recently, the developments of deep learning (DL) and computer vision (CV) approaches make it possible for the automatic colorization of grayscale images into color images. Also, the usage of visual communication design in APP interface design has increased. With this motivation, this work introduces the enhanced deep learning-based automated historical image colorization (EDL-AHIC) technique for wireless network-enabled visual communication. The proposed EDL-AHIC technique intends to effectually convert the grayscale images into color images. The presented EDL-AHIC technique extracts the local as well as global features. For global feature extraction, the enhanced capsule network (ECN) model is applied. Finally, the fusion layer and decoding unit are employed to determine the output, i.e., chrominance component of the input image. A comprehensive experimental validation process is performed to ensure the betterment of the EDL-AHIC technique. The comparison study reported the supremacy of the EDL-AHIC technique over the other recent methods.

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