* When Machine Learning Meets Fractional-Order Chaotic Signals: Detecting Dynamical Variations

Gürkan Kavuran, When machine learning meets fractional-order chaotic signals: detecting dynamical variations, Chaos, Solitons & Fractals, Volume 157, 2022, 111908, ISSN 0960-0779,


Abstract: The challenge of classifying multivariate time series generated by discrete and continuous dynamical systems according to their chaotic or non-chaotic behavior has been studied extensively in the literature. The examination of noise or the variation of variables that affect a dynamic system’s chaoticity will not be beneficial in analyzing structures employing random number generators (RNG) that are already assured to be chaotic. However, detecting the structural changes and their time intervals in deterministic systems with proven chaoticity can contribute to the literature in encryption applications. Machine Learning algorithms provide flexible possibilities to analyze and predict manipulations that may occur in the dynamics of chaotic and complex systems. This study proposes a deep Long-Short-Term-Memory (LSTM) network with a classification process to predict dynamical changes in a fractional-order chaotic (FOC) system. First, the appropriate system parameters are calculated to satisfy the chaotic behavior in the fractional-order Chen system. The predictive-corrective Adams-Bashforth-Moulton algorithm is used to simulate the FOC Chen system in the time domain. The Lyapunov exponents of the system were obtained according to the Wolf method. Next, three different scenarios have been designed to test and demonstrate the effectiveness of the proposed method. Synthetic FOC signals obtained after sub-sampling and statistical feature extraction processes fed the input of the deep bidirectional LSTM (BiLSTM) network to perform the training and testing process. The classification performance for “q” and “c” classes reaches 100% with the proposed model. The overall average testing accuracy, sensitivity, specificity, precision, F1 score and MCC are 98%, 98%, 99.3%, 98.1%, 98%, and 97.3%, respectively. Our results demonstrate the utility of using a deep BiLSTM network for detecting dynamical variations in nonlinear FOC systems.
Keywords: Fractional-order dynamical systems; Chaos; Deep learning; BiLSTM; Time series; Classification

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* SEM-Net: Deep features selections with Binary Particle Swarm Optimization Method for classification of scanning electron microscope images

Gürkan Kavuran, SEM-Net: Deep features selections with Binary Particle Swarm Optimization Method for classification of scanning electron microscope images, Materials Today Communications, Volume 27, 2021, 102198, ISSN 2352-4928, https://doi.org/10.1016/j.mtcomm.2021.102198.

Abstract: Materials Science is increasingly handling artificial intelligence methods to address the complexity in the field of everyday life necessities. Researchers in both academia and industry are interested in imaging techniques used in the characterization of nanomaterial with designed properties to meet the needs of applications in the literature. However, the increase in image size and complexity in its content restricts the use of traditional methods. Recent advances in machine learning have been used to benefit computers’ potential to make sense of these images. The approach proposed in this paper aims for the feature reduction with the Binary Particle Swarm Optimization method to execute the classification process on SEM images by concatenating the deeper layers of pre-trained CNN models AlexNet and ResNet-50. The feature vectors were used as input to support vector machine classifier (SVMC) after dimension reduction to obtain the final model. Finally, the trained model’s performance was tested using SEM images of Ag-doped SnO2 nanoparticles, which were prepared by the author using the low-temperature hydrothermal method. To the best of the author knowledge, these images were not available in the databases. The best accuracy value was observed with 3112 features for the SEM dataset with optimized vectors as 99.3 %. An example was illustrated where the feature selection with the BPSO technique could provide novel insight into nanoscience research and test the model with the SEM images of Ag-doped SnO2 particles that are obtained by the hydrothermal method.
Keywords: Hydrothermal method; Nanoscience; Ag-doped SnO2; Feature selection; Binary Particle Swarm Optimization; Deep learning

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* MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net

Malatya Turgut Özal Üniversitesi (MTÜ) Tıp Fakültesi ve Mühendislik Fakültesi öğretim üyelerince geliştirilen ve MTU-COVNet olarak isimlendirilen yapay zekâ yazılımı, Covid-19 tanısında oldukça yüksek başarı elde etti. Geliştirilen yapay zekâ programı ile Covid-19 hastalarının teşhisinde yaklaşık yüzde 98 oranında başarıya ulaşıldı.


Uygulama, uluslararası tıp dergisi Clinical Imaging’de de yayımlanırken Doç. Dr. Erdal İn, Dr. Öğretim Üyesi Ayşegül Altıntop Geçkil, Dr. Öğretim Üyesi Nurcan Kırıcı Berber, Elektrik Elektronik Mühendisliği Bölüm Başkanı Dr. Öğretim Üyesi Gürkan Kavuran ile MTÜ Eğitim ve Araştırma Hastanesinden Uzman Dr. Mahmut Şahin bu çalışmalarıyla ‘Solunum2021 Dijital Kongresi’nde “Dikkat Çeken Araştırmacı” ödülüne layık görüldü.

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