The concept for this work came from my curiosity about what would happen if, during interdimensional travel in space, a teleportation system were used. This system involves removing matter from one point and transferring it to another while maintaining its original state. If an error occurs and the matter is recreated or fused together, it could result in an experimental creature merging with the spacecraft. I choose the tardigrade as the first experimental subject for teleportation because the water bear has already been sent into space and survived. Therefore, I thought that if we were to actually test this teleportation system, the tardigrade would likely be one of the creatures chosen for experimentation.
โดยส่วนตัวผมเป็นคนที่ค่อนข้างชอบสื่อเเละนิยาย ที่เกี่ยวกับไซไฟ ผมเลยอยากนำมาต่อยอดเข้ากับการสร้างงานโมเดลของตนเองเพื่อตอบสนองความชอบเเละความเป็นไปได้ต่างๆที่อาจจะเกิดขึ้นได้ในโลกความเป็นจริง
วิทยาลัยเทคโนโลยีและนวัตกรรมวัสดุ
-
คณะวิทยาศาสตร์
In today’s rapidly expanding e-commerce environment, the massive volume of product reviews makes it crucial to summarize user opinions in a way that is both comprehensible and practically applicable. This research presents a system for analyzing product reviews using Aspect-Based Sentiment Analysis (ABSA), a Natural Language Processing (NLP) technique that identifies key aspects of a review (such as shipping, product quality, and packaging) and evaluates the sentiment (positive, negative, or neutral) associated with each aspect, allowing both consumers and merchants to gain more efficient access to in-depth insights. This project focuses on developing AI for Thai-language ABSA by utilizing WangchanBERTa, a model trained on Thai data, and comparing it with various standard approaches such as TF-IDF + Logistic Regression, Word2Vec + BiLSTM, and Multilingual BERT (mBERT/XLM-R) to assess their performance in terms of accuracy, speed, and resource usage. Additionally, a dashboard visualization is provided to help users quickly grasp review trends. The expected outcome is to create an AI tool that can be practically employed in the e-commerce industry, enabling consumers to make easier purchasing decisions and assisting merchants in effectively improving their products and services.
คณะแพทยศาสตร์
This study explores the application of deep convolutional neural networks (CNNs) for accurate pill identification, addressing the limitations of traditional human-based methods. Using a dataset of 1,250 images across 10 household remedy drugs, various CNN architectures, including YOLO models, were tested under different conditions. Results showed that natural lighting was optimal for imprinted pills, while a lightbox improved detection for plain pills. The YOLOv5-tiny model demonstrated the best detection accuracy, and efficientNet_b0 achieved the highest classification performance. While the model showed strong results, its generalization is limited by sample size and drug variability. Nonetheless, this approach holds promise for enhancing medication safety and reducing errors in outpatient care.