Ecological evolution path of smart education platform based on deep learning and image detection

Abstract

Abstract Smart environments are becoming a reality in our society, and the number of smart devices integrated into these spaces is overgrowing. End users are being provided a simplified way to handle complex smart features, as the combination of smart elements opens up a wide range of new opportunities to facilitate. This article explores the significant challenges to be overcome in designing an intelligent educational environment for the main characteristics and the personalized support of ecology. To integrate intelligent learning environments into learning ecology and educational environments, innovative applications, and new teaching methods should be implemented to coordinate formal and informal learning. However, despite the increased use of smart learning environments in higher education, at the same time, there is an excellent network that does not define a set of demand models for the development and evaluation of smart learning environment education that considers teaching, evaluation, and design. Deep learning is one of the modern methods that can be used to automate the process of effective intellectual education based on image detection. The deep learning process is based on image discovery. It provides an overview of ecological evaluation based smart education level analysis used image detection. The system that has been proposed here is an intelligent education system that has been customized to provide the resources of the evolution of the ecosystem to the learner to suit their perceptions and education center to start the platform.