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Bekir Karlik
Researcher at Montreal Neurological Institute and Hospital
Publications - 46
Citations - 1741
Bekir Karlik is an academic researcher from Montreal Neurological Institute and Hospital. The author has contributed to research in topics: Artificial neural network & Fuzzy clustering. The author has an hindex of 18, co-authored 43 publications receiving 1466 citations. Previous affiliations of Bekir Karlik include Beykent University & Mevlana University.
Papers
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A fuzzy clustering neural network architecture for classification of ECG arrhythmias
TL;DR: This study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture named multi-layered perceptron (MLP) with backpropagation training algorithm, and a new fuzzy clustering NN architecture (FCNN) for early diagnosis.
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A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis
TL;DR: The results suggest that FCNN can generalize better than other NN algorithms and help the user learn better and faster and has the potential of being very efficient in real-time applications.
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A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network
TL;DR: An improved classifier for automated diagnostic systems of electrocardiogram (ECG) arrhythmias using type-2 fuzzy c-means clustering (T2FCM) algorithm and neural network to constitute the best classification system with high accuracy rate for ECG beats is presented.
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Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier
TL;DR: The research showed that accuracy rate was found as 99% using this system, and the proposed classifier, type-2 fuzzy clustering wavelet neural network (T2FCWNN), is compared with the structures formed by type-1 FCM and WT.
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Diagnosing diabetes using neural networks on small mobile devices
TL;DR: A novel approach for diagnosing diabetes using neural networks and pervasive healthcare computing technologies and the initial results for a simple client (patient's PDA) and server (powerful desktop PC) two-tier pervasive healthcare architecture are presented.