1,721,010 research outputs found
Heart sound classification with signal instant energy and stacked autoencoder network
No full textRecently, different signal processing and classification methods have been tried to increase the success of classification for a heart sound analysis. For this purpose, in many studies, S1 and S2 segments of heart sounds were obtained by using methods such as Shannon energy, discrete time wavelet transform, Hilbert transform, and then classified. In this study, the use of signal energy, which is generally used to segment S1-S2 sounds in heart sounds, in direct classification was investigated. The instant energies of the heart sounds obtained by the resampled energy method were used directly for classification. The classification was done with a stacked autoencoder network. Experiments were carried out with the PASCAL B-training data set to test the performance of the proposed method. The results were compared with the data from previous studies for the same data set. As a result of the research, it is seen that the classification performance criterias obtained with the proposed method are as similar as the segmented classification. Thus, it was concluded that the instant energy of the heart sounds, and a stacked autoencoder networks can be very easily used for the diagnosis of heart diseases from heart sounds and a more efficient, and effective classification performance can be obtained
Power electronics converter control based on rule based algorithm
Get PDFThe exact modeling of power converter circuit that includes several semiconductor switching devices is not easy due to the non-linear and time varying characteristics of the switching devices. Thus, controlling the system effectively without exact mathematical model is very important. The rule based controller (RBC) can easily be used in the control of any systems when an exact mathematical model of the system cannot be obtained. In this paper, a RBC for DC-DC converter is proposed for output voltage control of DC-DC converter. As compared to conventional fuzzy logic control (FLC), it provides improved performances in terms of overshoot limitation and sensitivity to load and line voltage variations. Simulation and experimental results of buck converter confirm the validity of proposed control technique
TIBBİ VERİ KÜMELERİNDE GENETİK ALGORİTMALARLA ÖZELLİK SEÇİMİ VE SINIFLANDIRMA BAŞARIMINA ETKİSİ
Get PDFGünümüzde çok büyük boyuttaki tıbbi veri tabanlarından, klinik karar destek sistemlerinin faydalı bilgiler elde etmesi oldukça zorlaşmıştır. Genetik algoritmalar (GA) yaygın olarak kullanılan bir özellik seçme yöntemidir ve en iyi çözümleri verebilir. Bu çalışmada, çok sayıda karmaşık verilere sahip olan tıbbi verilerden özellik seçimi yapmak ve en uygun özellik alt kümesini oluşturarak sınıflandırma başarısını artırmak için GA içeren bir model önerilmiştir. Önerilen yöntemin performansını değerlendirmek için çalışmada en çok bilinen ve rahatlıkla ulaşılabilen 5 tıbbi veri kümesi ve 7 farklı denetimli sınıflandırma yöntemi kullanılmıştır. Her veri kümesi ile her sınıflandırıcı için ayrı ayrı özellik seçimi ve sınıflandırma uygulamaları yapılmıştır. Bu uygulamalarda elde edilen sonuçlar, önerilen yaklaşımla yapılan sınıflandırmalarda, veri kümesine bağlı olarak, Doğruluk oranında dolayısıyla makine öğrenmesi modeli performansında ortalama %2 ile %21 arasında artış sağlandığını ortaya koymuştur. Ayrıca yapılan çalışmalarda denetimli sınıflandırma algoritmalarından Rastgele Ormanın bütün veri kümelerinde diğer algoritmalardan daha iyi sonuçlar verdiği görülmekte ve tıbbi veri kümelerindeki sınıflandırma başarısı ile öne çıktığı görülmüştür
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Kalp Seslerinin Yeniden Örneklenmis Enerji Yöntemi ile Siniflandirilmasi
Get PDF26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYDue to the high number of heart diseases in the world and the increasing number of deaths resulting from them, the studies about early diagnosis of heart diseases has also increased. Studies on cardiac signals focus especially on the classification of heart sounds. Naturally, researches are generally concerned with increasing classification success. For this purpose, many studies use the separation of heart sounds into S1 and S2 segments by methods such as shannon energy, discreat wavelet transform and Hilbert transform. In this study, the use of signal energy, which is used as a sub-method of segmentation, has been explored for directly classification. For this purpose, the energy of the heart sounds was calculated by the re-sampled energy method and the obtained data were classified by artificial neural networks. Especially in these studies, the effect of sampling time on classification success was investigated. The obtained results were compared within themselves and with classification by dataset of S1-S2 sounds segmented. It is show that if the sampling rate is reduced in the direct classification by the re-sampled energy method, the accuracy of the classification is seen to increase. In addition, accuracy of classification is higher than the classification made by the segmented data set. Here I have reached the conclusion that the energy of heart sounds used to segment heart sounds can be used directly in classification studies and a more efficient classification performance can be achieved.IEEE,Huawei,Aselsan,NETAS,IEEE Turkey Sect,IEEE Signal Proc Soc,IEEE Commun Soc,ViSRATEK,Adresgezgini,Rohde & Schwarz,Integrated Syst & Syst Design,Atilim Univ,Havelsan,Izmir Katip Celebi Uni
Segmentation of heart sounds by Re-Sampled signal energy method
Get PDFAuscultation, which means listening to heart sounds, is one of the most basic medical methods used by physicians to diagnose heart diseases. These voices provide considerable information about the pathological cardiac condition of arrhythmia, valve disorders, heart failure and other heart conditions. This is why cardiac sounds have a great prominence in the early diagnosis of cardiovascular disease. Heart sounds mainly have two main components, S1 and S2. These components need to be well identified to diagnose heart conditions easily and accurately. In this case, the segmentation of heart sounds comes into play and naturally a lot of work has been done in this regard. The first step in the automatic analysis of heart sounds is the segmentation of heart sound signals. Correct detection of heart sounds components is crucial for correct identification of systolic or diastolic regions. Thus, the pathological conditions in these regions can be clearly demonstrated. In previous studies, frequency domain studies such as Shannon energy and Hilbert transformation method were generally performed for segmentation of heart sounds. These methods involve quite long and exhausting stages. For this reason, in this study, a re-sampled
energy method which can easily segment heart sounds in the time domain has been developed. The results obtained from the experiments show that the proposed method segments S1 and S2 sounds very efficiently
Classification of phonocardiograms with convolutional neural networks
Get PDFThe diagnosis of heart diseases from heart sounds is a matter of many years. This is the effect of having too many people with heart diseases in the world. Studies on heart sounds are usually based on classification for helping doctors. In other words, these studies are a substructure of clinical decision support systems. In this study, three different heart sound data in the PASCAL Btraining data set such as normal, murmur, and extrasystole are classified. Phonocardiograms which were obtained from heart sounds in the data set were used for classification. Both Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used for classification to compare obtained results. In these studies, the obtained results show that the CNN classification gives the better result with 97.9% classification accuracy according to the results of ANN. Thus, CNN emerges as the ideal classification tool for the classification of heart sounds with variable characteristics
Development of a relational database for learning management systems
Get PDFIn today’s world, Web-Based Distance Education Systems have a great importance. Web-based Distance Education Systems are usually known as Learning Management Systems (LMS). In this article, a database design, which was developed to create an educational institution as a Learning Management System, is described. In this sense, developed Learning Management System consists of basis of Virtual Education Institutions. In this study, a fully relational database design has been realized in compliance with SCORM standards and got ready to be used as Virtual Education Institutions. This system can be used for any required education institute and it can be run within the same interface. In LMS that will be generated, a faculty or institute can be defined and academic and all administrative processes of the defined institute can be managed with the designed system. Proposed database design has been used in a LMS of Afyon Kocatepe University. In this system, many processes like indexing, uploading, downloading, production and editing of web based learning materials can also be performed easily and safely
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