Yazar "Saracoglu, Ridvan" seçeneğine göre listele

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    A biomedical system based on fuzzy discrete hidden Markov model for the diagnosis of the brain diseases
    (PERGAMON-ELSEVIER SCIENCE LTD, 2008) Uguz, Harun; Oeztuerk, Ali; Saracoglu, Ridvan; Arslan, Ahmet
    Because it is a non-invasive, easy to apply and reliable technique, transcranial doppler (TCD) study of the adult intracerebral circulation has increased enormously in the last 10 years. In this study, a biomedical system has been implemented in order to classify the TCD signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. The diseases were investigated cerebral aneurysm, brain hemorrhage, cerebral oedema and brain tumor. The system is composed of feature extraction and classification parts, basically. In the feature extraction stage, the linear predictive coding analysis and cepstral analysis were applied in order to extract the cepstral and delta-cepstral coefficients in frame level as feature vectors. In the classification stage, discrete hidden Markov model (DHMM) based methods were used. In order to avoid loosing information due to vector quantization and to increase the classification performance, a fuzzy approach based similarity was applied to implement the DHMM. The performance of the proposed Fuzzy DHMM (FDHMM) was compared with some methods such as DHMM, artificial neural network (ANN), neuro-fuzzy approaches and obtained better classification performance than these methods. (C) 2007 Elsevier Ltd. All rights reserved.
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    Biomedical system based on the Discrete Hidden Markov Model using the Rocchio-Genetic approach for the classification of internal carotid artery Doppler signals
    (ELSEVIER IRELAND LTD, 2011) Uguz, Harun; Guraksin, Gur Emre; Ergun, Ucman; Saracoglu, Ridvan
    When the maximum likelihood approach (ML) is used during the calculation of the Discrete Hidden Markov Model (DHMM) parameters, DHMM parameters of the each class are only calculated using the training samples (positive training samples) of the same class. The training samples (negative training samples) not belonging to that class are not used in the calculation of DHMM model parameters. With the aim of supplying that deficiency, by involving the training samples of all classes in calculating processes, a Rocchio algorithm based approach is suggested. During the calculation period, in order to determine the most appropriate values of parameters for adjusting the relative effect of the positive and negative training samples, a Genetic algorithm is used as an optimization technique. The purposed method is used to classify the internal carotid artery Doppler signals recorded from 136 patients as well as of 55 healthy people. Our proposed method reached 97.38% classification accuracy with fivefold cross-validation (CV) technique. The classification results showed that the proposed method was effective for the classification of internal carotid artery Doppler signals. (C) 2010 Elsevier Ireland Ltd. All rights reserved.
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    Detection of heart valve diseases by using fuzzy discrete hidden Markov model
    (PERGAMON-ELSEVIER SCIENCE LTD, 2008) Uguz, Harun; Arslan, Ahmet; Saracoglu, Ridvan; Turkoglu, Ibrahim
    In the present study, biomedical based application was developed to classify the data belongs to normal and abnormal samples generated by Doppler ultrasound. This study consists of raw data obtaining and pre-processing, feature extraction and classification steps. In the pre-processing step, a high-pass filter, white de-noising and normalization were used. During the feature extraction step, wavelet entropy was applied by wavelet transform and short time fourier transform. Obtained features were classified by fuzzy discrete hidden Markov model (FDHMM). For this purpose, a FDHMM that consists of Sugeno and Choquet integrals and lambda fuzzy measurement was defined to eliminate statistical dependence assumptions to increase the performance and to have better flexibility. Moreover, Sugeno integral was used together with triangular norms that are mentioned frequently in the literature in order to increase the performance. Experimental results show that recognition rate obtained by Sugeno fuzzy integral with triangular norm is more successful than recognition rates obtained by standard discrete HMM (DHMM) and Choquet integral based FDHMM. In addition to this, it is shown in this study that the performance of the Sugeno integral based method is better than the performances of artificial neural network (ANN) and HMM based classification systems that were used in previous studies of the authors. (c) 2007 Elsevier Ltd. All rights reserved.
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    A fuzzy clustering approach for finding similar documents using a novel similarity measure
    (PERGAMON-ELSEVIER SCIENCE LTD, 2007) Saracoglu, Ridvan; Tutuncu, Kemal; Allahverdi, Novruz
    Searching for similar documents has a crucial role in document management. This paper aims for developing a fast and high quality method of searching similar documents based on fuzzy clustering in large document collections. In order to perform these requirements, a two layers structure is proposed. Formerly, finding the similarity in documents is based on the strategy that uses word-by-word comparison. The proposed method in this study uses two layers structure and lets the documents pass through it to find the similarities. In this system, predefined fuzzy clusters are used to extract feature vectors of related documents for finding similar documents of them. Similarity measure is estimated based on these vectors. To do this, a distance based similarity measure is proposed. It has been seen in empirical results that the proposed system uses new similarity measure and has better performance compared with conventional similarity measurement systems. (c) 2006 Elsevier Ltd. All rights reserved.
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    Hidden Markov Model-based Classification of Heart Valve Disease with PCA for Dimension Reduction
    (Pergamon-elsevier Science Ltd, 2012) Saracoglu, Ridvan
    In this study, a biomedical system to classify heart sound signals obtained with a stethoscope, has been proposed. For this purpose, data from healthy subjects and those with cardiac valve disease (pulmonary stenosis (PS) or mitral stenosis (MS)) have been used to develop a diagnostic model. Feature extraction from heart sound signals has been performed. These features represent heart sound signals in the frequency domain by Discrete Fourier Transform (DFT). The obtained features have been reduced by a dimension reduction technique called principal component analysis (PCA). A discrete hidden Markov model (DHMM) has been used for classification. This proposed PCA-DHMM-based approach has been applied on two data sets (a private and a public data set). Experimental classification results show that the dimension reduction process performed by PCA has improved the classification of heart sound signals. (C) 2012 Elsevier Ltd. All rights reserved.
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    A new approach on search for similar documents with multiple categories using fuzzy clustering
    (PERGAMON-ELSEVIER SCIENCE LTD, 2008) Saracoglu, Ridvan; Tuetuencue, Kemal; Allahverdi, Novruz
    Searching for similar document has an important role in text mining and document management. In whether similar document search or in other text mining applications generally document classification is focused and class or category that the documents belong to is tried to be determined. The aim of the present study is the investigation of the case which includes the documents that belong to more than one category. The system used in the present study is a similar document search system that uses fuzzy clustering. The situation of belonging to more than one category for the documents is included by this system. The proposed approach consists of two stages to solve multicategories problem. The first stage is to find out the documents belonging to more than one category. The second stage is the determination of the categories to which these found documents belong to. For these two aims alpha-threshold Fuzzy Similarity Classification Method (alpha-FSCM) and Multiple Categories Vector Method (MCVM) are proposed as written order. Experimental results showed that proposed system can distinguish the documents that belong to more than one category efficiently. Regarding to the finding which documents belong to which classes, proposed system has better performance and success than the traditional approach. (c) 2007 Elsevier Ltd. All rights reserved.