A Design for Talent Cultivation of Students Through Integrated Deep Learning with Hybrid Optimal Attention Model in Edge-Cloud-IoT Environment

Abstract

Student talent enhancement is highly concentrated in today’s educational system. Students are getting distracted by several activities, and their concentration is out of education. Therefore an effective model is needed to enhance to students’ talent by performance several activities on daily basis to them. To address this issue, a new model created the integrated DL model with Hybrid Optimal Attention in Edge-Cloud network (IDHOEC) framework units several cutting-edge technologies to detect the student talent. To achieve these, Edge-Cloud-IoT systems equipped with Raspberry Pi and Arduino sensors are used for managing data in real-time. The cloud setup is in charge of image storage, handling databases, and displaying information through LXD containerized services. Student monitoring is done through YOLO-based object detection. A bio-inspired approach not only increases the effectiveness of the model but also makes its decisions more understandable. The use of a Bayesian-optimized LSTM (BO-LSTM) model leads to better student performance prediction. Capsule Network (CapsNet) and dynamic routing help preserve spatial relationships when features are being represented. Deep Neural Network (DNN) are capable of learning nonlinear mappings and after that, they are optimized using gradient descent. A Modified Aquila Optimization (MAO)-optimized attention-based CapsNet enhances feature extraction and accuracy.