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Effective transfer entropy approach to information flow between exchange rates and stock markets

Sensoy A. | Sobaci C. | Sensoy S. | Alali F.

Article | 2014 | Chaos, Solitons and Fractals68 , pp.180 - 185

We investigate the strength and direction of information flow between exchange rates and stock prices in several emerging countries by the novel concept of effective transfer entropy (an alternative non-linear causality measure) with symbolic encoding methodology. Analysis shows that before the 2008 crisis, only low level interaction exists between these two variables and exchange rates dominate stock prices in general. During crisis, strong bidirectional interaction arises. In the post-crisis period, the strong interaction continues to exist and in general stock prices dominate exchange rates. © 2014 Elsevier Ltd. All rights reserved.

Impact of hybrid neural network on the early diagnosis of diabetic retinopathy disease from video-oculography signals

Kaya, Ceren | Erkaymaz, Okan | Ayar, Orhan | Özer, Mahmut

Article | 2018 | Chaos, Solitons and Fractals114 , pp.164 - 174

In this study, we introduce two hybrid artificial neural network models with particle swarm optimization algorithm to diagnose diabetic retinopathy based on the Video-Oculography signals. The hybrid models use Discrete Wavelet Transform and Hilbert-Huang Transform separately to extract features from the signals. The classification performance of both models is analyzed comparatively. We show that the model based on Hilbert–Huang Transform exhibits better classification performance than the model based on the Discrete Wavelet Transform. © 2018 Elsevier Ltd

A new generalization of Fibonacci hybrid and Lucas hybrid numbers

Kızılateş C.

Article | 2020 | Chaos, Solitons and Fractals130 , pp.164 - 174

In the paper, we define the q-Fibonacci hybrid numbers and the q-Lucas hybrid numbers, respectively. Then, we give some algebraic properties of q-Fibonacci hybrid numbers and the q-Lucas hybrid numbers. © 2019 Elsevier Ltd

An effective approach to numerical soliton solutions for the Schrödinger equation via modified cubic B-spline differential quadrature method

Bashan A. | Yagmurlu N.M. | Ucar Y. | Esen A.

Article | 2017 | Chaos, Solitons and Fractals100 , pp.45 - 56

In this study, an effective differential quadrature method (DQM) which is based on modified cubic B-spline (MCB) has been implemented to obtain the numerical solutions for the nonlinear Schrödinger (NLS) equation. After separating the Schrödinger equation into coupled real value differential equations,we have discretized using DQM and then obtained ordinary differential equation systems. For time integration, low storage strong stability-preserving Runge–Kutta method has been used. Numerical solutions of five different test problems have been obtained. The efficiency and accuracy of the method have been measured by calculating error . . . norms L2 and Linfinity and two lowest invariants I1 and I2. Also relative changes of invariants are given. The newly obtained numerical results have been compared with the published numerical results and a comparison has shown that the MCB-DQM is an effective numerical scheme to solve the nonlinear Schrödinger equation. © 2017 Elsevier Lt Daha fazlası Daha az

Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes

Erkaymaz, Okan | Özer, Mahmut

Article | 2016 | Chaos, Solitons and Fractals83 , pp.178 - 185

Artificial intelligent systems have been widely used for diagnosis of diseases. Due to their importance, new approaches are attempted consistently to increase the performance of these systems. In this study, we introduce a new approach for diagnosis of diabetes based on the Small-World Feed Forward Artificial Neural Network (SW- FFANN). We construct the small-world network by following the Watts-Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the . . . conventional FFANN. The SW-FFANN approach also results in both the highest output correlation and the best output error parameters. We also perform the accuracy analysis and show that SW-FFANN approach exhibits the highest classifier performance. © 2015 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Impacts of hybrid synapses on the noise-delayed decay in scale-free neural networks

Yılmaz, Ergin

Article | 2014 | Chaos, Solitons and Fractals66 , pp.1 - 8

We study the phenomenon of noise-delayed decay in a scale-free neural network consisting of excitable FitzHugh-Nagumo neurons. In contrast to earlier works, where only electrical synapses are considered among neurons, we primarily examine the effects of hybrid synapses on the noise-delayed decay in this study. We show that the electrical synaptic coupling is more impressive than the chemical coupling in determining the appearance time of the first-spike and more efficient on the mitigation of the delay time in the detection of a suprathreshold input signal. We obtain that hybrid networks including inhibitory chemical synapses have h . . .igher signal detection capabilities than those of including excitatory ones. We also find that average degree exhibits two different effects, which are strengthening and weakening the noise-delayed decay effect depending on the noise intensity. © 2014 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Noise-delayed decay in the response of a scale-free neuronal network

Uzuntarla, Muhammet | Uzun, Rukiye | Yılmaz, Ergin | Özer, Mahmut | Perc, Matjaž

Article | 2013 | Chaos, Solitons and Fractals56 , pp.202 - 208

Noise-delayed decay occurs when the first-spike latency of a periodically forced neuron exhibits a maximum at particular noise intensity. Here we investigate this phenomenon at the network level, in particular by considering scale-free neuronal networks, and under the realistic assumption of noise being due to the stochastic nature of voltage-gated ion channels that are embedded in the neuronal membranes. We show that noise-delayed decay can be observed at the network level, but only if the synaptic coupling strength between the neurons is weak. In case of strong coupling or in a highly interconnected population the phenomenon vanis . . .hes, thus indicating that delays in signal detection can no longer be resonantly prolonged by noise. We also find that potassium channel noise plays a more dominant role in the occurrence of noise-delayed decay than sodium channel noise, and that poisoning the neuronal membranes may weakens or intensify the phenomenon depending on targeting. © 2013 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Multi-stage classification of congestive heart failure based on short-term heart rate variability

İşler, Yalçın | Narin, Ali | Özer, Mahmut | Perc, Matjaž

Article | 2019 | Chaos, Solitons and Fractals118 , pp.145 - 151

In this study, we propose an automatic system to diagnose congestive heart failure using short-term heart rate variability analysis. The system involves a multi-stage classifier. The features of heart rate variability are computed from time-domain and frequency-domain measures through power spectral density estimations of different transform methods. Nonlinear heart rate variability measures are also calculated by using Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy. Different combinations of heart rate variability features are selected according to their statistical significance levels and then . . . applied to the classifier. The first two stages of the classifier consist of simple perceptron classifiers that are trained by a genetic algorithm. Five different classifiers, namely k-nearest neighbors, linear discriminant analyses, multilayer perceptron, support vector machines, and radial basis function artificial neuronal network, are tested for the third stage. The proposed system results in a classification performance of an accuracy of 98.8%, specificity of 98.1%, and sensitivity of 100%. We show that our approach provides an effective and computationally efficient tool to automatically diagnose congestive heart failure patients. © 2018 Elsevier Lt Daha fazlası Daha az

Detection of directional eye movements based on the electrooculogram signals through an artificial neural network

Erkaymaz, Hande | Özer, Mahmut | Orak, İlhami Muharrem

Article | 2015 | Chaos, Solitons and Fractals77 , pp.225 - 229

The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The res . . .ults suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately. © 2015 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques

Altan A. | Karasu S. | Bekiros S.

Article | 2019 | Chaos, Solitons and Fractals126 , pp.325 - 336

The price forecasting of the digital currencies in the financial market is of great importance, especially after the recent global economic crises. Due to the nonlinear dynamics, which is including inherent fractality and chaoticity of the digital currencies, it is understood from the research conducted by many researchers that a single model is not sufficient in forecasting the digital currencies with very high accuracy. Since the single models used in the forecasting of digital currencies have weaknesses as well as their own strengths, they might not grant the best forecasting achievement in all situations for all the time. A new . . .hybrid-forecasting framework has been proposed in digital currency time-series to minimize this negative situation and increase forecasting achievement. In this study, a novel hybrid forecasting model based on long short-term memory (LSTM) neural network and empirical wavelet transform (EWT) decomposition along with cuckoo search (CS) algorithm is developed for digital currency time series. The model is obtained by combining the LSTM neural network and EWT decomposition technique, and optimizing the intrinsic mode function (IMF) estimated outputs with CS. The price of the four most traded digital currencies such as BTC, XRP, DASH and LTC, is estimated by the proposed model and the performance of the model has been tested. The experimental results show that the hybrid model proposed for digital currency forecasting can capture nonlinear properties of digital currency time series. © 2019 Elsevier Lt Daha fazlası Daha az

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