In professional basketball games, big data has been largely used in analyzing the reasons for winning or losing games and further to design relevant stratagem according to the analytic results to attain victory. Nonetheless, the High School Basketball League (HBL) in Taiwan never used big data or relevant research to analyze game results. The study aims to conduct big data analyses to discuss the key winning factors and trends for HBL. Using Excel and multiple linear regression to understand the importance level and trend of each variable to the winning rate. Additionally, combining with the Support Vector Machine (SVM) prediction to confirm whether the big data analytic result is applicable for implementing in realistic games. After implementing the analysis of multiple linear regression, based on the yearly trends, the significant influence factors are 2P%, 3P%, FTM, TRB, OREB, STL, and TOV. Consequently, the prediction has reached 85% after inputting these data into SVM.

No abstract

Today, climate change has caused a decrease in agricultural output or overall yields that are not as expected; however, with the ongoing population explosion, many undeveloped countries have transformed into emerging countries and have transformed farmland to be used in other types of applications. The resulting decline in agricultural output further increases the severity of the food crisis. In this context, this study proposes an outdoor agricultural robot that uses Long Short-Term Memory (LSTM). The key features of this innovation include: (1) the robot is portable, and it uses green power to reduce installation cost, (2) the system combines the current environment with weather forecasts through LSTM to predict the correct timing for watering, (3) detecting the environment and utilizing information from weather forecasts can help the system to ensure that growing conditions are suitable for the crops, and (4) the robot is mainly for outdoor applications because such farms lack sufficient electricity and water resources, which makes the robot critical for environmental control and resource allocation. The experimental results indicate that the robot developed in this study can detect the environment effectively to control electricity and water resources. Additionally, because the system is planned to increase agricultural output significantly, the study predicts the variables through multivariate LSTM, which controls the power supply from the solar power system.

No abstract