Browsing by Author "Abidin D."

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    Using data mining for makam recognition in Turkish traditional art music; [Klasik Törk möziǧinde makam tanima için veri madenciliǧi kullanimi]
    (Gazi Universitesi Muhendislik-Mimarlik, 2017) Abidin D.; Öztörk Ö.; Öztörk T.O.
    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%.
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    KORAL: Türk Müziǧi için Makam Tabanli Öneri Motoru Tasarimi
    (Institute of Electrical and Electronics Engineers Inc., 2019) Öztürk O.; Özacar T.; Abidin D.
    This work describes the design of an application that can create a playlist having 'fasil' characteristic by finding similar pieces with a given Turkish Art Music piece in terms of 'makam' and 'usul'. The pieces used in this work will be stored in a graph database and this graph will be published as linked data. In this context, for the first time, a knowledge base about Turkish Music pieces will be published as Linked Data. © 2018 IEEE.
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    A Hybrid Genetic - Differential Evolution Algorithm (HybGADE) for a Constrained Sequencing Problem
    (Institute of Electrical and Electronics Engineers Inc., 2019) 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. © 2018 IEEE.
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    Effects of image filters on various image datasets
    (Association for Computing Machinery, 2019) 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%. © 2019 Association for Computing Machinery.
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    The Effect of Derived Features on Art Genre Classification with Machine Learning
    (Sakarya University, 2021) Abidin D.
    Classification of the artwork according to their genres is being done for years. Although this process was used to be done by art experts before, now artificial intelligence techniques may help people manage this classification task. The algorithms used for classification are already improved, and now they can make classifications and predictions for any kind of genre classification. In this study, two different machine learning algorithms are used on an artwork dataset for genre classification. The primary purpose of this study is to show that the derived features about the artwork have a remarkable effect on correct genre classification. These features are derived from the metadata of the dataset. This metadata contains information about the nationalities and the period that the artist lived. Image filters are also applied to the images but the results show that applying only image filters on the dataset used in the study did not perform well. Instead, adding derived features extracted from the metadata increased the classification performances dramatically. © 2021, Sakarya University. All rights reserved.
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    DDSS: denge decision support system to recommend the athlete-specific workouts on balance data
    (Springer Science and Business Media Deutschland GmbH, 2022) Abidin D.; Cinsdikici M.G.
    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%. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.