Machine learning has been widely used in different domains to extract information from raw data. Sports is one of the popular domains for researchers to work on recently. Although score prediction for matches is the most preferred application area for artificial intelligence, player selection, and team formation is also an application area worth working on. There are some studies in the literature about player selection and team formation which are examined in this study. The study has two important contributions: First one is to apply seven different machine learning algorithms on our dataset to find the best player combination for the U13 team of Altınordu Football Academy and comparing the results with that of the coach's lineup and lineups of 20 matches played in 2019-2020 season. Second is combining the data obtained from the trainings of the players and coach evaluations of the players and feeding the machine to make more accurate predictions. The data from the trainings is gathered with Hit/it Assistant and the coach evaluations of the players are stated by the golden standard according to eighteen criteria stated in the literature. Synthetically generated data is also used in the final dataset to obtain more accurate classification results. Another remarkable aspect of the study is that no match data is used to form the team to be proposed for the next match, instead real match data is only used for evaluation. The results show that machine learning algorithms can be used for player selection and team formation process because random forest algorithm, which is executed on WEKA environment, can make player selections with 93.93% reliability and the lineup suggestions of these algorithms are 97.16% similar to coach's ideal team and also the best performing algorithm has an average performance of 89.36% for team formation when compared with the match lineups of 2019-2020 football season.

Turkish Music pieces are used in various studies including makam recognition in computational music domain. Turkish Music pieces offer a rich content to the researchers because of their different makam properties. SymbTr is one of the most referred Turkish Music data sets in this area. In this study, the pieces from SymbTr data set belonging to 13 makams are used to execute 10 different machine learning algorithms for makam recognition and the performances of these algorithms are evaluated. These algorithms were executed on WEKA application environment and the performances in makam recognition were obtained with F-measure and recall metrics. The machine learning algorithms performed between 82% and 88%.

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Abstract:In corporations, accurate planning should be applied to manage the in-service training task within an optimum time period and without hindering the working tempo of the employees. For this reason, it is better to consider the curriculum planning task as a timetabling problem. However, when the timetables are prepared manually, it may turn out to be a complicated and time-consuming problem. In this study, it is aimed to evaluate the results of software introduced previously, which seeks to find a solution to the curriculum planning problem of in-service training programs in corporations using a rule-based genetic algorithm (GA). The input data of the GA is the prerequisite rule set of the modules of the training program, where these rules are used for the fitness function of the system. The results are compared with the suggestion of an expert trainer using a nonparametric correlation test, and the best parameter combination of the GA giving the most similar result to that of the expert's is determined. According to the tests, the results gathered are considered to be 97% reliable when compared with the suggested module range.