Browsing by Author "Abidin, D"

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    Effects of Image Filters on Various Image Datasets
    Abidin, D
    Image classification is a very common research area, on which researchers work with various classification techniques. The aim of this study is to apply different filters on four different datasets and evaluate their performances in image classification. The study was performed in WEKA environment with Random Forest algorithm and image filters are applied to the datasets one by one and as a combination. Filter combinations got better performance than applying single filter on data. Filter combinations got the worst result on artworks with a percentage of 83.42%. However they were very successful on classifying the images in natural images dataset with a performance of 99.76%.
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    A Hybrid Genetic - Differential Evolution Algorithm (HybGADE) for a Constrained Sequencing Problem
    Abidin, D
    For researchers, evolutionary algorithms are mostly preferable because of their effectiveness in finding the optimum solutions to many problems. Among these problems, sequencing is one of the most popular. In daily life, it is a must to find the best solution to a sequencing problem in order to save time, money and labour. Education is also one of the application areas of optimization where sequencing matters. In this paper, a hybrid genetic - differential algorithm is introduced, which finds better solutions to sequencing problem in education. The correct order of educational data is crucial because it directly affects the students' performance. In this study, educational material of Database course in Ege University Tire Kutsan Vocational School Computer Programming Department is used as the data set with two different evolutionary algorithms (EA). In these data sets, there are some constraints which should be considered while sequencing. We called them prerequisites that tells us the rules about the order of the modules of a course. That is why, the study can be considered as an application of Precedence-Constrained Sequencing. The sequencing performances of pure genetic algorithm (GA) and hybridized differential evolution (DE) with genetic algorithms (HybGADE) are compared with a computer program implemented. It is observed that, HybGADE can be used with 99.54% of reliability where pure GA has an effectiveness of 98.53% The percentage of the students passing the class has been observed for four years. The ratio of students passing the class has increased from 39% to 65%, which can be considered as a remarkable increase.
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    DDSS: denge decision support system to recommend the athlete-specific workouts on balance data
    Abidin, D; Cinsdikici, MG
    Monitoring the balance conditions and physical abilities of athletes is important to track their current situations which enables us to apply appropriate training programs for recovery. For different branches of sports, there are three main balance board types to be used; not swaying board (i.e. Wii board), semi-spherical fulcrum (i.e. Wobble board), and springboard (i.e. Spring Balance Board). In this study, the Balance springboard, which is new to the literature, is used. The springboard equipped with sensors uses Bluetooth technology to transmit collected balance data. There are various previous studies developed for assessing the balance performance of athletes regarding the first two types of balance-boards. Most of them are based on statistical analysis and machine learning (ML) techniques. In this study, the usage of a shallow deep learning model trained with the balance data, which is a contribution to the literature, gathered from the springboard is introduced. This model (DDSS, Denge Decision Support System) is compared with the base ANN model -which leads the study to tend our DDSS model- and ML techniques. Our DDSS model outperforms when compared with the base ANN and ML techniques, Sequential Minimal Optimization and Random Forest, and offers appropriate training program suggestions with a success rate of 92.11%.
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    A case study on player selection and team formation in football with machine learning
    Abidin, D
    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 Altinordu 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.
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    Using data mining for makam recognition in Turkish traditional art music
    Abidin, D; Öztürk, Ö; Öztürk, TÖ
    Computer science has become a popular reseach topic in musicology with the transfer of musical works to digital media. Musical works are used as data in scientific researches and the computational music field is developing rapidly with the work done in this area. Representing Western musical works in symbolic form is easier than Turkish musical works and as a result most of the studies in this area focus on Western Music. However, in the last few years there are some interesting studies on using data mining, machine learning and classification techniques on Turkish maqam system. This study represents an experimental work that uses machine learning to recognize the maqams of the 1261 Turkish musical works. These musical works are assumed to be obtained by note recognition from audio files. We developed a software for using the data in MusicXML format with machine learning. This software also adds four different derived variables to the original data set in order to incerase the performance of the machine learning process. As a result of the study, we observed the perfomance of the Random Forest algorithm as 89.7%.