Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object repositories (LORs). LORs produce different types of electronic content. In producing e-content, several visualization techniques are employed to attract users and ensure a better understanding of the provided information. Many of these visualization systems match images with corresponding text using methods such as semantic web, ontologies, natural language processing, statistical techniques, neural networks, and deep neural networks. Unlike these methods, in this study, an automatic and intelligent content visualization system is developed using deep learning and popular artificial intelligence techniques. The proposed system includes subsystems that segment images to panoptic image instances and use these image instances to generate new images using a genetic algorithm, an evolution-based technique that is one of the best-known artificial intelligence methods. This large-scale proposed system was used to test different amounts of LOs for various science fields. The results show that the developed system can be efficiently used to create visually enhanced content for digital use.
Keywords: Deep learning; Genetic algorithm; Panoptic segmentation; e-content visualization.
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022.