International Journal of Neural Networks and Advanced Applications
E-ISSN: 2313-0563
Volume 8, 2021
Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.
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Volume 8, 2021
Title of the Paper: Robust Hybrid Control Using Recurrent Wavelet- Neural-Network Sliding-Mode Controller for Two- Axis Motion Control System
Authors: Fayez F. M. El-Sousy
Pages: 49-59
DOI: 10.46300/91016.2021.8.6 XML
Abstract: In this paper, a robust hybrid control system (RHCS) for achieving high precision motion tracking performance of a two-axis motion control system is proposed. The proposed AHCS incorporating a recurrent wavelet-neuralnetwork controller (RWNNC) and a sliding-mode controller (SMC) to construct a RRWNNSMC. The two-axis motion control system is an x-y table of a computer numerical control machine that is driven by two field-oriented controlled permanent-magnet synchronous motors (PMSMs) servo drives. The RWNNC is used as the main motion tracking controller to mimic a perfect computed torque control law and the SMC controller is designed with adaptive bound estimation algorithm to compensate for the approximation error between the RWNNC and the ideal controller. The on-line learning algorithms of the connective weights, translations and dilations of the RWNNC are derived using Lyapunov stability analysis. A computer simulation and an experimental are developed to validate the effectiveness of the proposed RHCS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results using star and four leaves contours are provided to show the effectiveness of the RHCS. The motion tracking performance is significantly improved using the proposed RHCS and robustness to parameter variations, external disturbances, cross-coupled interference and frictional torque can be obtained as well for the two-axis motion control system.
Title of the Paper: Use an Efficient Neural Network to Improve the Arabic Handwriting Recognition
Authors: Husam Ahmed Al Hamad
Pages: 43-48
DOI: 10.46300/91016.2021.8.5 XML
Abstract: Using an efficient neural network for recognition and segmentation will definitely improve the performance and accuracy of the results; in addition to reduce the efforts and costs. This paper investigates and compares between results of four different artificial neural network models. The same algorithm has been applied for all with applying two major techniques, first, neural-segmentation technique, second, apply a new fusion equation. The neural techniques calculate the confidence values for each Prospective Segmentation Points (PSP) using the proposed classifiers in order to recognize the better model, this will enhance the overall recognition results of the handwritten scripts. The fusion equation evaluates each PSP by obtaining a fused value from three neural confidence values. CPU times and accuracies are also reported. Experiments that were performed of classifiers will be compared with each other and with the literature.
Title of the Paper: COVID-LIBERTY, A Machine Learning Computational Framework for the Study of the Covid-19 Pandemic in Europe. Part 2: Setting up the Framework with Ensemble Modeling
Authors: Nicholas Christakis, Panagiotis Tirchas, Michael Politis, Minas Achladianakis, Eleftherios Avgenikou, George Kossioris
Pages: 27-42
DOI: 10.46300/91016.2021.8.4 XML
Abstract: The Covid-19 pandemic has caused within a period of one year and eight months over 200,000,000 infections and more than 4,000,000 deaths. It is of paramount importance to design powerful and robust tools in order to be able to predict the evolution of the disease. In this paper, the computational framework COVID-LIBERTY is introduced, in order to assist the study of the pandemic in Europe. In Part 1, important parameters that should be taken into consideration and their parametrizations were given, as well as the details and mathematics of the computational engine of COVID-LIBERTY, a feed-forward, back-propagation Artificial Neural Network. In Part 2, the CPRT index is introduced, the framework setup around the Artificial Neural Network is presented and the algorithm of ensemble modeling is discussed, which improves the accuracy of the predictions. In the simulations, 4 European countries with similar population numbers were considered. The capabilities of the COVID-LIBERTY framework for accurate predictions for periods up to 19 days will be demonstrated.
Title of the Paper: COVID-LIBERTY, A Machine Learning Computational Framework for the Study of the Covid-19 Pandemic in Europe. Part 1: Building of an Artificial Neural Network and Analysis and Parametrization of Key Factors which Influence the Spread of the Virus.
Authors: Nicholas Christakis, Michael Politis, Panagiotis Tirchas, Minas Achladianakis, Eleftherios Avgenikou, Christina Kalafati Matthaiou, Maria Kalykaki, Argyri Kyriakaki, Panagiotis Paraschis, Evangelos Pilios, George Kossioris
Pages: 12-26
DOI: 10.46300/91016.2021.8.3 XML
Abstract: Covid-19 is the most recent strain from the corona virus family that its rapid spread across the globe has caused a pandemic, resulting in over 200,000,000 infections and over 4,000,000 deaths so far. Many countries had to impose full lockdowns, with serious effects in all aspects of everyday life (economic, social etc.). In this paper, a computational framework is introduced, aptly named COVID-LIBERTY, in order to assist the study of the pandemic in Europe. Initially, the mathematics and details of the computational engine of the framework, a feed-forward, back-propagation Artificial Neural Network are presented. 5 European countries with similar population numbers were chosen and we examined the main factors that influence the spread of the virus, in order to be taken into consideration in the simulations. In this way lockdown, seasonal variability and virus effective reproduction were considered. The effectiveness of lockdown in the spread of the virus was examined and the Lockdown Index was introduced. Moreover, the relation of Covid- 19 to seasonal variability was demonstrated and the parametrization of seasonality presented.
Title of the Paper: Forecasting Analysis of Covid-19 Cases with Wavelet Neural Network and Time Series Approach
Authors: Asli Kaya, Fatih Cemrek, Ozer Ozdemir
Pages: 6-11
DOI: 10.46300/91016.2021.8.2 XML
Abstract: COVID-19 is a respiratory disease caused by a novel coronavirus first detected in December 2019. As the number of new cases increases rapidly, pandemic fatigue and public disinterest in different response strategies are creating new challenges for government officials in tackling the pandemic. Therefore, government officials need to fully understand the future dynamics of COVID-19 to develop strategic preparedness and flexible response planning. In the light of the above-mentioned conditions, in this study, autoregressive integrated moving average (ARIMA) time series model and Wavelet Neural Networks (WNN) methods are used to predict the number of new cases and new deaths to draw possible future epidemic scenarios. These two methods were applied to publicly available data of the COVID-19 pandemic for Turkey, Italy, and the United Kingdom. In our analysis, excluding Turkey data, the WNN algorithm outperformed the ARIMA model in terms of forecasting consistency. Our work highlighted the promising validation of using wavelet neural networks when making predictions with very few features and a smaller amount of historical data.
Title of the Paper: The Streamers Dynamics Study by an Intelligent System based on Neural Networks
Authors: Fouad Khodja, Younes Mimoun, Riad Lakhdar Kherfane
Pages: 1-5
DOI: 10.46300/91016.2021.8.1 XML
Abstract: The formation and propagation of streamers is an important precursor to determine the characteristics of electrical breakdown of many HV electrode configurations. Understanding of the study of the interaction between the polymer surface and the development process of the streamer is of major importance when we want to improve internal and external performance insulation systems. In this context, a numerical tool using neural networks is developed. This model allows evaluating the speed of streamers as a function of the amplitude of voltage initiation and the nature of the insulating materials. For this, a database was created to train the neural model from a laboratory model. This investigation builds a database for predicting the propagation of streamers on the polymers surface by different neuronal methods and this presents an interesting tool for estimating the propagation phenomena in functions of very important parameters.
