This project aims to develop seafood dipping sauce and Jaew sauce in solid cube form to address the limitations of liquid sauces, which can be difficult to carry and prone to spillage, as well as powdered sauces, which may lose their texture and authentic flavor. The research and development process focuses on utilizing distinct ingredients and innovative production techniques to enhance the quality and functionality of the product. The primary objective of this project is to introduce an innovative solution that improves the convenience of consumption and transportation while preserving the original taste and quality of traditional dipping sauces. The expected outcome is a novel dipping sauce product in solid cube form that is easy to carry, minimizes the risk of spillage, and holds potential for commercial development in the food industry.
โครงงานนี้มีที่มาจากความต้องการนวัตกรรมที่เพิ่มความสะดวกในการบริโภคและขนส่งน้ำจิ้ม พร้อมทั้งรักษาคุณภาพและรสชาติดั้งเดิมให้ใกล้เคียงต้นตำรับมากที่สุด
คณะวิทยาศาสตร์
In a highly competitive business, understanding customers is crucial for an organization to determine its success. Effective marketing is not just about offering good products, promotions, or services; it also requires strategies to reach and build strong relationships with customer groups. Segmenting customers is one method that helps businesses deeply understand the needs and behaviors of the customers who use their services In this internship, the objective is to understand the behavior of customers purchasing coffee and tea at a large cafe group by analyzing stored customer data. As a result of this process, customer groups purchasing coffee and tea were segmented using Naive Bayes, Random Forest, and Deep Learning techniques to compare the accuracy and suitability of different Machine Learning methods, and the insights gained from this analysis can be for further development in analyzing other data set in the future
คณะเทคโนโลยีสารสนเทศ
This research presents a deep learning method for generating automatic captions from the segmentation of car part damage. It analyzes car images using a Unified Framework to accurately and quickly identify and describe the damage. The development is based on the research "GRiT: A Generative Region-to-text Transformer for Object Understanding," which has been adapted for car image analysis. The improvement aims to make the model generate precise descriptions for different areas of the car, from damaged parts to identifying various components. The researchers focuses on developing deep learning techniques for automatic caption generation and damage segmentation in car damage analysis. The aim is to enable precise identification and description of damages on vehicles, there by increasing speed and reducing the work load of experts in damage assessment. Traditionally, damage assessment relies solely on expert evaluations, which are costly and time-consuming. To address this issue, we propose utilizing data generation for training, automatic caption creation, and damage segmentation using an integrated framework. The researchers created a new dataset from CarDD, which is specifically designed for cardamage detection. This dataset includes labeled damages on vehicles, and the researchers have used it to feed into models for segmenting car parts and accurately labeling each part and damage category. Preliminary results from the model demonstrate its capability in automatic caption generation and damage segmentation for car damage analysis to be satisfactory. With these results, the model serves as an essential foundation for future development. This advancement aims not only to enhance performance in damage segmentation and caption generation but also to improve the model’s adaptability to a diversity of damages occurring on various surfaces and parts of vehicles. This will allow the system to be applied more broadly to different vehicle types and conditions of damage inthe future
คณะวิศวกรรมศาสตร์
This project aims to develop an AI-powered system for detecting and classifying wall cracks using image processing. It identifies different crack types, assesses severity, and ensures accuracy across various image conditions. The goal is to support preventive maintenance by enabling early detection of structural issues, reducing repair costs, and improving safety.