A Photographic series that expresses the abstract states of myself, towards the question of existence that results from being surrounded by expectations of both surrender and freedom of expression, this series focuses on my own subjectivities in order to bring back memories of almost forgotten feelings and make them clear once more.
การที่เติบโตมาจากครอบครัวที่คาดหวังในตัวเรา ที่สมาชิกคาดหวังในตัวเราไม่เหมือนกัน ถ้าเราทำแบบใดแบบหนึ่งที่คนใดคนหนึ่งต้องการอีกคนจะไม่พอใจ จนเราเกิดสงสัยว่าเราต้องเป็นแบบไหน เมื่อเข้ามาอยู่ในสังคมใหม่ทำให้เราตั้งคำถามกับตนเองเมื่อเข้าหาผู้คนว่าเราต้องเป็นไปแบบที่เขาต้องการหรือเปล่าเราถึงจะเข้าถึงเขาได้ ทำให้เราสับสนกับตัวเองและต้องสร้างตัวตนใหม่ไปตามที่คนคนนั้นพอใจ จนเราเองเริ่มเกิดคำถามว่าจริงๆแล้วตัวตนของเราจริงๆเป็นแบบไหน
คณะวิทยาศาสตร์
This special problem aims to compare the performance of machine learning methods in time series forecasting using lagged time periods as independent variables. The lagged periods are categorized into three groups: lagged by 10 units, lagged by 15 units, and lagged by 20 units. The study employs four machine learning methods: Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The time series data simulated as independent variables diverse including characteristics: Random Walk data, Trending data, and Non-Linear data, with sample sizes of 100, 300, 500, and 700. The research methodology involves splitting the data into 90% for training and 10% for testing. Simulations and analysis are performed using the R programming language, with 1,000 iterations conducted. The results are evaluated based on the average mean squared error (AMSE) and the average mean absolute percentage error (AMAPE) are calculated to identify the best performing method. The research findings revealed that for Random Walk data, the best performing methods are Random Forest and Support Vector Machine. For Trend data, the best performing methods are Random Forest. For Non-Linear data, the best performing methods are Support Vector Machine. When tested with real-world data, the results show that for the Euro-to-Thai Baht exchange rate, the best methods are Random Forest and Support Vector Machine. For the S&P 500 Index in USD, the best performing methods are Random Forest. For the Bank of America Corp Index in USD, the best performing methods are Support Vector Machine.
คณะเทคโนโลยีสารสนเทศ
Cancer is one of the major health issues in Thailand, particularly as the country enters an aging society. The risk of chronic diseases among the elderly often results in limitations in treatment, making it difficult for most patients to achieve a complete recovery. This necessitates continuous care and the provision of accurate information and guidance about cancer. However, current health record systems for patients lack effective interconnectivity, which hinders data analysis and the development of patient care models. Additionally, incorrect information about cancer spread across social media can lead to misunderstandings among elderly patients. To address these issues, researchers have developed a chatbot system that utilizes Natural Language Processing (NLP) technology to understand human language and accurately respond to questions about elderly cancer patient care. The chatbot provides reliable and up-to-date information based on medical knowledge sourced from a database reviewed by healthcare professionals. Furthermore, a web application has been developed to record and analyze patient assessments according to medical standards, enabling healthcare providers to plan and develop appropriate treatment approaches in a better way. This system also facilitates data sharing and connectivity across hospital systems, allowing information to be used to enhance the precision and modernity of treatment approaches. In addition, the chatbot acts as an assistant, providing information and guidance to patients, reducing the workload of healthcare staff in answering questions and encouraging patients to take a more active role in managing their own health.
วิทยาลัยการจัดการนวัตกรรมและอุตสาหกรรม
Diabetes is a significant global health issue, particularly due to complications related to diabetic wounds. Studies indicate that approximately 15-25% of diabetic patients develop foot ulcers, with more than 50% of severe cases leading to amputation. This results in a substantial decline in the quality of life for patients. Current treatments for diabetic wounds face challenges such as antibiotic-resistant bacterial infections and delayed wound healing, highlighting the need for innovative solutions to accelerate the healing process and reduce the risk of limb loss. Cotylelobium lanceolatum Craib, a medicinal plant long utilized in traditional Thai medicine, is known for its anti-inflammatory and wound-healing properties. This study focuses on developing an extract from Cotylelobium lanceolatum Craib in the form of nano silver (Nano Silver) to enhance the effectiveness of diabetic wound treatment. Nano silver technology enables deeper penetration into the skin, provides potent antibacterial activity, and promotes wound healing by reducing inflammation and stimulating tissue regeneration. The development of nano silver derived from Cotylelobium lanceolatum Craib extract is expected to help reduce chronic wounds in diabetic patients, lower the risk of infection, and decrease the incidence of limb amputation and mortality associated with diabetic wound complications. This research represents a significant step toward creating a safer and more effective treatment alternative for diabetic wound care.