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One challenging task for the blind is to input Braille while by no way could they sense the location information on touch screens. The existing Braille input methods are suffering from problems including inaccurate positioning and lack of interactive prompts. In this paper, touch gestures are recognized by trained RBF network while combined gestures are modelled. By doing so, the Braille input concerning multi-touch gesture recognition is then implemented. The experimental results show that the method is effective and blind people can friendly input Braille with almost real-time interaction.

Nowadays, predicting students' performance is one of the most specific topics for learning environments, such as universities and schools, since it leads to the development of effective mechanisms that can enhance academic outcomes and avoid destruction. In education 4.0, Artificial Intelligence (AI) can play a key role in identifying new factors in the performance of students and implementing personalized learning, answering routine student questions, using learning analytics, and predictive modeling. It is a new challenge to redefine education 4.0 to recognize the creative and innovative intelligent students, and it is difficult to determine students' outcomes. Hence, in this paper, Hybridized Deep Neural Network (HDNN) to predict student performance in Education 4.0. The proposed HDNN method is utilized to determine the dynamics that likely influence the student's performance. The deep neural network monitor, predicts, and evaluates the student's performance in an education 4.0 environment. The findings show that the proposed HDNN method achieved better prediction accuracy when compared to other popular methods.