Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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2015 - 20204

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    Author Recognition from Lyrics
    (2015) Kirmaci, Basar; Ogul, Hasan
    Music information retrieval has been an important task due to the wide use of internet and related technologies for entertainment. In previous studies, the problem has been considered using the meta-data or melodic content. The use of lyrics in this context is not that common. There is not study either for Turkish songs in this respect. In this study, we discuss the predictability of the author using the text data in a Turkish lyric. To this end, we propose a system that can predict the author using the features extracted from text content. The performance of the system is evaluated on a large data set collected from writers with different music styles.
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    Heart Disease Prediction by Using Machine Learning Algorithms
    (2020) Erdogan, Alperen; Guney, Selda
    Nowadays, one of the most important illness is heart disease which cause of mostly patients dead. Medical diagnosis of heart diseases is very difficult. While heart diseases are diagnosed medically, they can be confused with other diseases that show same symptoms such as chest pain, shortness of breath, palpitations and nausea. This makes it difficult to diagnose heart diseases medically. In this study, the presence of heart diseases was determined by using machine learning algorithms. In this study, the data obtained from the patients were weighted according to their effects on the success rate. In this study, a method is proposed for determine weight coefficient. According to proposed method's results, 86,90% success was achieved with 13 different features obtained from the patients.
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    Classification of Heart Sound Recordings With Continuous Wavelet Transform Based Algorithm
    (2018) Karaca, Busra Kubra; Oltu, Burcu; Kantar, Tugce; Kilic, Erkin; Aksahin, Mehmet Feyzi; Erdamar, Aykut
    Cardiovascular diseases are the major cause of death in the world. Early diagnosis of heart diseases provide an effective treatment. Heart diseases can be diagnosed using data obtained from heart sounds. Heart sounds are listened by a physician with auscultation method and the disease diagnosis can vary depending on the physician's experience and hearing ability. For this reason, automatic detection of anomalies in heart sounds can give more objective results. In this study, features were obtained by processing phonocardiogram signals taken from Physionet database. The heart sounds are classified as normal and abnormal using these features and the k - nearest neighbor method. As a result, sensitivity, specificity and accuracy were determined as 100%, 96.1% and 98.2%, respectively.
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    Automatic Classification of Respiratory Sounds During Sleep
    (2018) Kilic, Erkin; Erdamar, Aykut
    Sounds like snoring, coughing, sneezing, whistling, which have different acoustic properties, can emerge involuntarily during the sleep. These sounds may affect negatively the sleep quality of the other people in the same environment, just as it may affect directly the sleep quality. To increase the sleep quality, these sounds should be recorded and evaluated by a sleep expert. This is an expertise required process that can be time-consuming and subjective results. In this study, it has been aimed that developing a computer-aided diagnosing algorithm which will classify the sounds emerging during the sleep automatically with high accuracy by analyzing the records in a fast and effective way to help the sleep expert to diagnose. The mathematical features have been obtained in frequency and time domains by applying continuous wavelet transform for the different type of sounds. Support vector machine used as a classifier. 390 and 449 segments were used for training and testing respectively. As a result of the study, six different parameters which are exhalation, simple snoring, high frequency duplex snoring, low frequency duplex snoring, triplex snoring and coughing were classified with 96.44% accuracy rate.