**Innovative Walking Stick "Buddy Take Care"** The "Buddy Take Care" walking stick is designed to physically support the elderly or individuals recovering from injuries, enabling convenient mobility, reducing the risk of falls, and enhancing walking safety. It is crafted as a keychain-style walking stick with a one-touch open-close mechanism. Building upon existing market products, the Buddy walking stick incorporates additional functionalities such as a portable flashlight, a medicine compartment, and an AirTag slot to maximize utility. Its design prioritizes ease of use, convenience, and safety, specifically tailored for elderly users
ปัจจุบันประเทศไทยได้ก้าวเข้าสู่สังคมผู้สูงอายุอย่างเต็มรูปแบบ ผู้สูงอายุบางกลุ่มประสบปัญหา การเดิน การทรงตัวทำให้มีความเสี่ยงต่อการหกล้ม ซึ่งอาจส่งผลกระทบรุนแรงไม้เท้าจึงเป็นอุปกรณ์ที่ผู้สูงอายุนิยมใช้เพื่อช่วยในการเคลื่อนไหว อย่างไรก็ตาม ไม้เท้าที่พบในท้องตลาดมักมีข้อจำกัด เช่น ขนาดใหญ่ พกพายากเมื่อใช้งานเป็นเวลานานอาจทำให้รู้สึกปวดมือ ทำให้กลุ่มของพวกเราต้องการออกแบบไม้เท้าในรูปแบบใหม่ที่พกพาง่าย และปลอดภัยกับการใช้งานต่อผู้สูงอายุโดยเฉพาะ
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 report is part of applying the knowledge gained from studying machine learning models and methods for developing a predictive model to identify customers likely to cancel their credit card services with a bank. The project was carried out during an internship at a financial institution, where the creator developed a model to predict customers likely to churn from their credit card services using real customer data through the organization's system. The focus was on building a model that can accurately predict customer churn by selecting features that are appropriate for the prediction model and the unique characteristics of the credit card industry data to ensure the highest possible accuracy and efficiency. This report also covers the integration of the model into the development of a website, which allows related departments to conveniently use the prediction model. Users can upload data for prediction and receive model results instantly. In addition, a dashboard has been created to present insights from the model's predictions, such as identifying high-risk customers likely to cancel services, as well as other important analytical information for strategic decision-making. This will help support more efficient marketing planning and customer retention efforts within the organization.
คณะวิทยาศาสตร์
This project presents the development of an automatic recycling machine for plastic bottles and cans, utilizing Machine Learning for packaging classification through image processing, integrated with smart sensor systems for quality inspection and operation control. The system connects to a Web Application for real-time monitoring and control. Once the packaging type is verified, the system automatically calculates the refund value and processes payment through e-wallet or issues cash vouchers. The system can be installed in public spaces to promote waste segregation at source, reduce contamination, and increase recycling efficiency. It also provides financial incentives to encourage public participation in waste management. This project demonstrates the potential of combining Machine Learning and smart sensor systems in developing accurate, convenient, and sustainable waste management solutions.