No abstract

No abstract

No abstract

The LED unit induced significantly higher temperature changes than did the HQTH. The temperature increase during orthodontic bonding was increased with long exposure time. A shorter light-tip tooth surface distance leads to greater increases in temperature.

No abstract

No abstract

Computer technology and software are widely used in every multi-discipline field. Geomatics engineering can be seen as a pioneer of these disciplines especially in photogrammetry and image processing. Photogrammetry is a method where geometric parameters of objects on digitally captured images are determined and make measurements on them. Capturing the digital images and photogrammetric processing include several fully defined stages, which allows to generate three-dimension or two-dimension digital models of the body as an end product. The aim of this study is to predict Holstein cows' live weight via artificial neural network whose body dimensions were determined with photogrammetry method. The body dimensions to be used in this study are obtained metric from analysis of cows' images captured by synchronized three-dimension camera environment from different aspects. Wither height, hip height, body length, hip width of cows determined with photogrammetry. Artificial neural network prediction model was developed by using these body measurements. Dataset is divided into two after preprocessing as training and testing dataset. Different structured artificial neural network models are generated and the artificial neural network model which has the best performance is determined. Then with this artificial neural network model live weight of animals is estimated by using measurements obtained from images. After comparison of estimated live weights and weights obtained from scale, correlation coefficient is found (R=0.995). The statistical analysis shows that both groups are meaningful and artificial neural network can be used in live weight prediction safely.

Football is a popular sport; however, it is a big business as well. From a managerial perspective, the important decisions that team managers make -Concerning player transfers, issues related to player valuation, especially the determination of transfer fees and market values, are of major concern. Market values can be understood as estimates of transfer fees-prices that could be paid for a player on the football market. Therefore, market values play an important role in transfer negotiations. The market has traditionally been estimated by football experts. However, expert judgments are inaccurate and not transparent. Data analytics may thus provide a sound alternative or a complementary approach to experts-based estimations of market value. In this study, we propose an objective quantitative method to determine football players' market values. The method is based on the application of machine learning algorithms to the performance data of football players. The data used in the experiment are FIFA 20 video game data, collected from sofifa.com. We estimate players' market values using four regression models that were tested on the full set of featureslinear regression, multiple linear regression, decision trees, and random forests. Moreover, we seek to analyze the data and identify the most important factors affecting the determination of the market value. In the experimental results, random forest performed better than other algorithms for predicting the players' market values. It has achieved the highest accuracy score and lowest error ratio compared to baseline. The results show that our methods are capable to address this task efficiently, surpassing the performance reported in previous works. Finally, we believe our results can play an important role in the negotiations that take place between football clubs and a player's agents. This model can be used as a baseline to simplify the negotiation process and estimate a player's market value in an objective quantitative way.INDEX TERMS Player value prediction, regression, Machine learning, Football analytics, FIFA Video game data.