Key points are not available for this paper at this time.
In contemporary society, conventional notice boards encounter challenges in terms of accessibility and engagement, especially within educational institutions where effective information dissemination is vital. By implementing a Smart Notice Board that is powered by cutting-edge technologies like artificial intelligence (AI), machine learning (ML), image processing, and effective frontend-backend communication, this research offers a game-changing solution. The system surpasses traditional analog methods by integrating digital capabilities, allowing for real-time updates and efficient communication within a global network. AI-based facial recognition is used by the Smart Notice Board to improve user experience and deliver tailored information to individuals or particular groups. When a person appears in front of the camera, the system recognizes them and pulls up relevant notifications based on their profile, which reduces the amount of work teachers, staff, and students have to do to stay informed. The system includes advanced image processing techniques to identify individuals, so notice delivery accuracy is guaranteed. In addition, the integration of socket technology guarantees smooth communication between the backend server and frontend user interface, facilitating effective data storage and transfer. By eliminating the time constraints linked to conventional notice boards, this method enables administrators to remotely oversee and distribute content. Additionally, the server stores individual images for facial recognition and customized notice delivery. By combining AI for facial recognition, ML and image processing for visual data analysis, and an advanced frontend-backend technology for system architecture, this smart notice board project not only streamlines the distribution of information but also greatly improves communication in educational setting as well as lessens burdens and produce a flexible platform that can adapt to changing user requirements.
Islam et al. (Thu,) studied this question.