A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation  

作　　者：Amira M.Idrees Abdul Lateef Marzouq Al-Solami 

机构地区：[1]Faculty of Computers and Information Technology,Future University in Egypt,Cairo,11835,Egypt [2]Alkamil College of Science and Arts,University of Jeddah,Jeddah,21959,Saudi Arabia

出　　处：《Computers, Materials & Continua》2024年第1期1115-1133,共19页计算机、材料和连续体（英文）

摘　　要：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 real-time 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 amodular 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.

关 键 词：Social networks text analytics emoji prediction features extraction information retrieval 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]