Dario Liberona

With the integration of information technology into aquaculture, production has become more stable and continues to grow annually. As consumer demand for high-quality aquatic products rises, freshness and appearance integrity are key concerns. In shrimp-based processed foods, freshness declines rapidly post-harvest, and soft-shell shrimp often suffer from head-body separation after cooking or freezing, affecting product appearance and consumer perception. To address these issues, this study leverages deep learning-based image recognition for automated classification of white shrimp immediately after harvest. A convolutional neural network (CNN) model replaces manual sorting, enhancing classification accuracy, efficiency, and consistency. By reducing processing time, this technology helps maintain freshness and ensures that shrimp transportation businesses meet customer demands more effectively.

In recent years, chatbots have increasingly been used in the field of psychological counseling as a new channel for providing personalized services. In order to find a personalized user interface suitable for users in this study, we have put forward two preliminary ideas based on a within-subject design. Investigate the types of chatbot interfaces for psychological counseling among college students, as well as their preferences for different selections of psychological counseling information and reading interfaces. Finally, the research results will be used to develop a personalized user interface for the psychological consultation chatbot, serving as a reference for future chatbot design.