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
คณะวิทยาศาสตร์
With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. Machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector Machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments.
คณะวิทยาศาสตร์
This research will begin with a review of literature and related studies to examine existing technologies and methods for hand gesture recognition and their applications in controlling electronic devices such as drones, robots, and gaming systems. Subsequently, a hand gesture recognition system will be designed and developed using machine learning and computer vision techniques, with a focus on creating an algorithm that operates quickly and accurately, making it suitable for real-time control. The developed system will be tested and refined using various simulated scenarios to evaluate its efficiency and accuracy in diverse environments. Additionally, a user-friendly interface will be developed to ensure accessibility for all user groups. The research will also incorporate qualitative studies to gather feedback from both novice users and experts, which will contribute to further system improvements, ensuring it effectively meets user needs. Ultimately, the findings of this research will lead to the development of a functional prototype for gesture-based control, which can be applied in industries and entertainment. This will contribute to advancements in innovation and new technologies in the future.
คณะวิศวกรรมศาสตร์
This project aims to design and develop an electric tuk-tuk by converting the traditional combustion engine system to an electric system, supporting the reduction of air pollution and promoting sustainable automotive technology. The electric tuk-tuk is designed using a BLDC electric motor and a control system specifically adapted for the unique driving style of three-wheeled vehicles in Thailand. The study considers suitable energy systems and includes interviews with traditional tuk-tuk drivers to ensure the vehicle meets everyday usability needs. The findings suggest that adopting electric tuk-tuks not only reduces emissions and PM2.5 particulate matter but also enhances an eco-friendly image for Thailand’s tourism sector while supporting domestic innovation and economic growth.