Enabling Intelligence through Deep Learning using IoT in a Classroom Environment based on a multimodal approach

Abstract

Smart Classrooms are becoming very popular nowadays. The boom of recent technologies such as the Internet of Things, thanks to those technologies that are tremendously equipping every corner of a diverse set of fields. Every educational institution has set some benchmark on adopting these technologies in their daily lives. But due to some constraints and setbacks, these IoT technological embodiments in the educational sector is still in the premature stage. The major success of any technological evolution is based on its full-fledged implementation to fit the society in the broader concern. The breakthrough in recent years by Deep Learning principles as it outperforms traditional machine learning models to solve any tasks especially, Computer Vision and Natural language processing problems. A fusion of Computer Vision and Natural Language Processing as a new astonishing field that have shown its existence in the recent years. Using such mixtures with the IoT platforms is a challenging task and and has not reached the eyes of many researchers across the globe. Many researchers of the past have shown interest in designing an intelligent classroom on a different context. Hence to fill this gap, we have proposed an approach or a conceptual model through which Deep Learning architectures fused in the IoT systems results in an Intelligent Classroom via such hybrid systems. Apart from this, we have also discussed the major challenges, limitations as well as opportunities that can arise with Deep Learning-based IoT Solutions. In this paper, we have summarized the available applications of these technologies to suit our solution. Thus, this paper can be taken as a kickstart for our research to have a glimpse of the available papers for the success of our proposed approach.