![]() Furthermore, parents seemed to have stronger impacts on child mental health than teachers did. Like previous studies, we found that there was a general pattern that showed that good child-mentor relationships, defined specifically by the frequency of praise and fights, had an overall positive impact on child mental health, specifically when it came to symptoms of depression. This information was used to create predictions of future cases. ![]() We conducted classification and coefficient weight analyses to see how strong of an impact different variables representing indicators of healthy relationships had on different aspects of child mental health. To expand upon these past findings, we used different measures of mental health and relationship qualities to create a predictive model. Previous studies have come to similar conclusions: positive parent-student and teacher-student relationships often lead to signs of positive mental health in children. In this paper, we use a dataset from the Substance Abuse and Mental Health Data Archive to analyze the impacts of child-mentor relationships on child mental health. The LSTM model in this project achieved accura- cies of 73.33% and 79.31% when classifying shot type (forehand, forehand volley, forehand slice, backhand, backhand volley, backhand slice, over- head/smash, and serve) for players on the close side and opposite side of the net, respectively, and 55.17% and 60.00% when classifying the di- rection of a shot (cross-court, down the line, down the middle, inside in, inside out, out wide, down the t, and body) for players on the close side and opposite side of the net, respectively. Looking at a transcript of each point will be far more efficient than watching entire match footage for a player to understand how they are losing and winning and analyze patterns in their game. ![]() This paper investigates an AI approach to transcribing tennis match footage, combining a deep convolution neural network (YOLOv4), a pose estimation model (Movenet), and a long short- term memory (LSTM) deep neural network. However, watching match footage and documenting each point shot by shot is a very time-consuming process. One of the best ways for tennis players to improve their game is to record and watch their own match footage, find patterns in the points they win and lose, and practice based on these realizations. Our study highlights the implementation of a novel convolutional neural network (CNN) in detecting solar panels through low resolution Google Static Maps API satellite imagery data. ![]() However, satellite imagery in developing countries such as Bangladesh is of much lower resolution and quality, and performed poorly with the original DeepSolar model by Yang et. al developed a deep-learning framework and national solar deployment database for the US using high-quality satellite imagery, which proved to be a much more efficient and accurate approach. Manual surveys have shown to be inaccurate. There is a critical need for highly accurate, comprehensive national databases of solar systems, which would allow policymakers, researchers, and the government to study socioeconomic trends in solar deployment. However, the decentralised nature of solar makes it difficult to keep track of the different photovoltaic (PV) systems deployed across a country. Due to its environmental benefits and decreasing costs, the supply of solar energy is growing at an accelerating pace globally. ![]()
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