A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

The developed system for eye and face detection using Convolutional Neural Networks (CNN) models, followed by eye classification and voice-based assistance, has shown promising potential in enhancing accessibility for individuals with visual impairments. The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system. This research significantly contributes to the field of accessibility technology by integrating computer vision, natural language processing, and voice technologies. By leveraging these advancements, the developed system offers a practical and efficient solution for assisting blind individuals. The modular design ensures flexibility, scalability, and ease of integration with existing assistive technologies. However, it is important to acknowledge that further research and improvements are necessary to enhance the system's accuracy and usability. Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities. Additionally, incorporating realtime responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system's ability to provide comprehensive and accurate responses. Overall, this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals. By leveraging cutting-edge technologies and integrating them into a modular framework, this research contributes to creating a more inclusive and accessible society for individuals with visual impairments. Future work can focus on refining the system, addressing its limitations, and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.