Here, We Luckier in Love Everyday". Introducing you a Lakshmi 2025 Edition. Amidst the buzz of the mall, take charge of your love destiny—because fate is so last season.
พระแม่ลักษมี เทพีเเห่งความเจริญรุ่งเรืองในศาสนาฮินดู โดยนอกจากจะเป็นเทพีเเห่งความรุ่งเรืองแล้ว ในไทยยังนับถือพระแม่ในฐานะเทพเจ้าเเห่งความรักจนเกิดปรากฏการรณ์ I Told พระแม่ หรือ I Told Lakshmi จากเหตุผลดังกล่าว จึงนำมาสู่การประยุกษ์ใช้ลักษณะดังกล่าวมาออกแบบ และผลิตเป็นเครื่องทำนายโชคชะตาเพื่อเสริมสร้างความพึงพอใจของลูกค้า รวมไปถึงการสร้างพฤติกรรมผู้บริโภคของลุกค้า
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
This project is a carbon safe haven of Bangkok, aspiring to be the prototypal gateway of the future's carbon net zero ambitions. The project aims to answer the fundamental "flaw" of the existing urban fabric, still being extremely inefficient and highly polluting. Conversely, Carbon Oasis would not only create its own energy, but look to provide its excess energy and water surplus' back to the city and its surroundings. Taking parts of the existing city and implementing new concepts to inspire a change in the urban fabric and its people.
คณะวิศวกรรมศาสตร์
A platform that aims to connect students from all faculties and departments to promote joint activities and develop effective social and collaborative skills, focusing on: Promoting learning and self-development through reviewing lessons and collaborative learning that are relevant to all faculties and departments in the university, creating a space for negotiation and exchange of knowledge, and supporting joint activities to build relationships and cooperation among students.
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.