Yazar "Er, Mehmet Bilal" seçeneğine göre listele

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    Analysis and classification of the mobile molecular communication systems with deep learning
    (Springer Heidelberg, 2022) Isik, Ibrahim; Er, Mehmet Bilal; Isik, Esme
    Nano networks focused on communication between nano-sized devices (nanomachines) is a new communication concept which is known as molecular communication system (MCs) in literature. The researchers have generally used fixed transmitter and receiver for MCs models to analyze the fraction of received molecules and signal to interference rate etc. In this study, contrary to the literature, a mobile MC model has been used in a diffusion environment by using five bits. It is concluded that when the receiver and transmitter are mobile, distance between them changes and finally this affects the probability of the received molecules at the receiver. After the fraction of received molecules is obtained for different mobility values of Rx and Tx (Drx and Dtx), deep learning's bi-directional long short-term memory (Bi-LSTM) model is applied for the classification of Rx and Tx mobilities to find the best MC model with respect to fraction of received molecules. Finally it is obtained that when the mobilities of Rx and Tx increase, the fraction of received molecules also increases. Bi-LSTM model of Deep learning is used on a data set consisting of five classes. The suggested model's accuracy, precision, and sensitivity values are obtained as 98.05, 96.49, and 98.01 percent, respectively.
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    Classification of Diffusion Constants of Transmitter and Receiver and Distance Between Them Using Mobile Molecular Communication via Diffusion Model
    (Springer Heidelberg, 2024) Er, Mehmet Bilal; Isik, Ibrahim; Kuran, Umut; Isik, Esme
    Molecular communication (MC) holds promise for enabling communication in scenarios where traditional wireless methods may be impractical or ineffective, offering unique capabilities for a range of applications in both natural and engineered systems. In this research, a novel approach to MC is explored, diverging from the standard use of stationary transmitter and receiver models typically found in the field. The study introduces a dynamic MC model, where both the transmitter and receiver are mobile within a diffusion environment. This model operates using a 5-bit system. The key finding is that the mobility of these nanodevices alters their distance, which in turn impacts the likelihood of molecule reception at the receiver. The study employs deep learning techniques, specifically a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to categorize the mobility patterns of the receiver (Rx) and transmitter (Tx). By analyzing various mobility rates (Drx and Dtx) and distances between the Tx and Rx, the research successfully identifies the most efficient mobile MC model in terms of molecule reception rates. The use of Linear Support Vector Machine alongside the CNN and LSTM hybrid feature vector resulted in an 87.68% accuracy in predicting diffusion coefficients. Moreover, using a Cubic Support Vector with the same hybrid feature vector, the study achieved an 88.09% accuracy in estimating the distance between the transmitter and receiver. The study concludes that an increase in the mobilities of Rx and Tx correlates with a higher rate of molecule reception.
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    LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini
    (2021) Er, Mehmet Bilal; Işık, İbrahim
    Öz: Diyabet, vücudun yeterli miktarda insülini üretmemesi veya iyi kullanamadığı durumda kan şekerinin normalin üstüne çıkması ile ortaya çıkan bir hastalıktır. Kan şekeri insanların ana enerji kaynağıdır ve bu enerji tüketilen yiyeceklerden gıdalardan gelir. Bu hastalık tedavi edilmez ise ölümcül olabilir. Ancak, erken tanı konulup tedaviye başlandığında tedavisi en olanaklı hastalıklardan biridir. Geleneksel diyabet teşhis süreci zorlu olduğundan, diyabetin klinik ve fiziksel verileri kullanılarak yapay sinir ağı, görüntü işleme ve derin öğrenme gibi sistemler kullanılarak hastalık teşhis edilebilmektedir. Bu araştırmada diyabet teşhisi için derin öğrenmeye dayalı bir model sunulmaktadır. Bu bağlamda Evrişimsel Sinir Ağı (ESA), Uzun Kısa Süreli Bellek (Long-short Term Memory Networks- LSTM) modelinin hibrit kullanımı sınıflandırma için tercih edilmiştir. Ayrıca ESA ve LSTM modelleri deneylerde ayrı ayrı kullanılmıştır. Önerilen modelin performansını değerlendirmek için literatürde yaygın olarak kullanılan Pima Indians Diabetes veri seti kullanılmıştır. En yüksek sınıflandırma başarısı %86,45 olarak ESA+LSTM modelinden elde edilmiştir.
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    Parkinson's detection based on combined CNN and LSTM using enhanced speech signals with Variational mode decomposition
    (Elsevier Sci Ltd, 2021) Er, Mehmet Bilal; Isik, Esme; Isik, Ibrahim
    Parkinson's disease (PD) can cause many non-motor and motor symptoms such as speech and smell. One of the difficulties that Parkinson's patients can experience is a change in speech or speaking difficulties. Therefore, the right diagnosis in the early period is important in reducing the possible effects of speech disorders caused by the disease. Speech signal of Parkinson patients shows major differences compared to normal people. In this study, a new approach based on pre-trained deep networks and Long short-term memory (LSTM) by using melspectrograms obtained from denoised speech signals with Variational Mode Decomposition (VMD) for detecting PD from speech sounds is proposed. The proposed model consists of four steps. In the first step, the noise is removed by applying VMD to the signals. In the second step, mel-spectrograms are extracted from the enhanced sound signals with VMD. In the third step, pre-trained deep networks are preferred to extract deep features from the mel-spectrograms. For this purpose, ResNet-18, ResNet-50 and ResNet-101 models are used as pre-trained deep network architecture. In the last step, the classification process is occurred by giving these features as input to the LSTM model, which is designed to define sequential information from the extracted features. Experiments are performed with the PC-GITA dataset, which consists of two classes and is widely used in the literature. The results obtained from the proposed method are compared with the latest methods in the literature, it is seen that it has a better performance in terms of classification performance.