Designing of Morlet wavelet as a neural network for a novel prevention category in the HIV system

2021 ◽  
Author(s):  
Zulqurnain Sabir ◽  
Muhammad Umar ◽  
Muhammad Asif Zahoor Raja ◽  
Haci Mehmet Baskonus ◽  
Wei Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Mathematical Model ◽  
Initial Conditions ◽  
Error Function ◽  
Morlet Wavelet ◽  
Interior Point Algorithm ◽  
The Novel ◽  
Numerical Performance ◽  
Independent Trials

The aim of this work is to present a design of Morlet wavelet neural network (MWNN) for solving a novel prevention category (P) in the HIV system, known as HIPV mathematical model. The numerical performance of the novel HIPV mathematical model will be observed by exploiting the MWNN that works through the optimization procedures of global/local via “genetic algorithm (GA)” and local search “interior-point algorithm (IPA)”, i.e. MWNN-GA-IPA. An error function using the differential HIPV mathematical model and its initial conditions is presented and optimized by the MWNN-GA-IPA. The obtained results have been compared with the Adams method to check the competence of the MWNN-GA-IPA. For the reliability and stability of the scheme, the performance using different statistical operators has been performed based on the multiple independent trials to solve the novel HIPV mathematical model.

scholarly journals Web Application for Statistical Tracking and Predicting the Evolution of Active Cases with the Novel Coronavirus (SARS-CoV-2)

2021 ◽  
pp. 70-74
Author(s):  
Iulia Clitan ◽  
◽  
Adela Puscasiu ◽  
Vlad Muresan ◽  
Mihaela Ligia Unguresan ◽  
...  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Web Application ◽  
Point Of View ◽  
The Novel ◽  
End User ◽  
First Case ◽  
Novel Coronavirus ◽  
Second Wave ◽  
Over Time

Since February 2020, when the first case of infection with SARS COV-2 virus appeared in Romania, the evolution of COVID-19 pandemic continues to have an ascending allure, reaching in September 2020 a second wave of infections as expected. In order to understand the evolution and spread of this disease over time and space, more and more research is focused on obtaining mathematical models that are able to predict the evolution of active cases based on different scenarios and taking into account the numerous inputs that influence the spread of this infection. This paper presents a web responsive application that allows the end user to analyze the evolution of the pandemic in Romania, graphically, and that incorporates, unlike other COVID-19 statistical applications, a prediction of active cases evolution. The prediction is based on a neural network mathematical model, described from the architectural point of view.

Application of Neural Network and Genetic Algorithm to the Optimum Design of Perforated Tube Mufflers

2009 ◽  
Vol 25 (3) ◽  
pp. N7-N16 ◽  
Author(s):  
M.-C. Chiu ◽  
Y.-C. Chang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimum Design ◽  
Transmission Loss ◽  
Research Work ◽  
Sound Transmission ◽  
Optimum Process ◽  
Sound Transmission Loss

AbstractResearch on new techniques of perforated silencers has been well addressed. However, the research work on shape optimization for a volume-constrained silencer within a constrained machine room is rare. Therefore, the optimum design of mufflers becomes an essential issue. In this paper, to simplify the optimum process, a simplified mathematical model of the muffler is constructed with a neural network using a series of input design data (muffle dimensions) and output data (theoretical sound transmission loss) obtained by a theoretical mathematical model (TMM). To assess the optimal mufflers, the neural network model (NNM) is used as an objective function in conjunction with a genetic algorithm (GA). Moreover, the numerical cases of sound elimination with respect to pure tones (500, 1000, 2000Hz) are exemplified and discussed.Before the GA operation can be carried out, the accuracy of the TMM is checked by Crocker's experimental data. In addition, both the TMM and NNM are compared. It is found that the TMM and the experimental data are in agreement. Moreover, the TMM and NNM confirm.The results reveal that the maximum value of the sound transmission loss (STL) can be optimally obtained at the desired frequencies. Consequently, it is obvious that the optimum algorithm proposed in this study can provide an efficient way to develop optimal silencers.

scholarly journals Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6567
Author(s):  
Kashif Nisar ◽  
Zulqurnain Sabir ◽  
Muhammad Asif Zahoor Raja ◽  
Ag Asri Ag Ibrahim ◽  
Samy Refahy Mahmoud ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Objective Function ◽  
Economic System ◽  
Initial Conditions ◽  
Numerical Study ◽  
Technical Diagnostics ◽  
Interior Point Algorithm ◽  
Artificial Neural

In this study, the numerical computation heuristic of the environmental and economic system using the artificial neural networks (ANNs) structure together with the capabilities of the heuristic global search genetic algorithm (GA) and the quick local search interior-point algorithm (IPA), i.e., ANN-GA-IPA. The environmental and economic system is dependent of three categories, execution cost of control standards and new technical diagnostics elimination costs of emergencies values and the competence of the system of industrial elements. These three elements form a nonlinear differential environmental and economic system. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions.

scholarly journals Swarm Intelligence Procedures Using Meyer Wavelets as a Neural Network for the Novel Fractional Order Pantograph Singular System

2021 ◽  
Vol 5 (4) ◽  
pp. 277
Author(s):  
Zulqurnain Sabir ◽  
Muhammad Asif Zahoor Raja ◽  
Juan L. G. Guirao ◽  
Tareq Saeed
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Numerical Solutions ◽  
Singular System ◽  
Standard Form ◽  
Merit Function ◽  
Stability And Convergence ◽  
Interior Point Algorithm ◽  
The Novel ◽  
Meyer Wavelets

The purpose of the current investigation is to find the numerical solutions of the novel fractional order pantograph singular system (FOPSS) using the applications of Meyer wavelets as a neural network. The FOPSS is presented using the standard form of the Lane–Emden equation and the detailed discussions of the singularity, shape factor terms along with the fractional order forms. The numerical discussions of the FOPSS are described based on the fractional Meyer wavelets (FMWs) as a neural network (NN) with the optimization procedures of global/local search procedures of particle swarm optimization (PSO) and interior-point algorithm (IPA), i.e., FMWs-NN-PSOIPA. The FMWs-NN strength is pragmatic and forms a merit function based on the differential system and the initial conditions of the FOPSS. The merit function is optimized, using the integrated capability of PSOIPA. The perfection, verification and substantiation of the FOPSS using the FMWs is pragmatic for three cases through relative investigations from the true results in terms of stability and convergence. Additionally, the statics’ descriptions further authorize the presentation of the FMWs-NN-PSOIPA in terms of reliability and accuracy.

scholarly journals Comparison of Artificial Neural Network Architecture in Solving Ordinary Differential Equations

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Susmita Mall ◽  
S. Chakraverty
Keyword(s):  
Neural Network ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Network Architecture ◽  
Initial Conditions ◽  
Error Function ◽  
Back Propagation ◽  
Feed Forward Neural Network ◽  
Error Back Propagation ◽  
Hidden Layer

This paper investigates the solution of Ordinary Differential Equations (ODEs) with initial conditions using Regression Based Algorithm (RBA) and compares the results with arbitrary- and regression-based initial weights for different numbers of nodes in hidden layer. Here, we have used feed forward neural network and error back propagation method for minimizing the error function and for the modification of the parameters (weights and biases). Initial weights are taken as combination of random as well as by the proposed regression based model. We present the method for solving a variety of problems and the results are compared. Here, the number of nodes in hidden layer has been fixed according to the degree of polynomial in the regression fitting. For this, the input and output data are fitted first with various degree polynomials using regression analysis and the coefficients involved are taken as initial weights to start with the neural training. Fixing of the hidden nodes depends upon the degree of the polynomial. For the example problems, the analytical results have been compared with neural results with arbitrary and regression based weights with four, five, and six nodes in hidden layer and are found to be in good agreement.

The Application of Improved Genetic Neural Network in IDS for Material Detecting

2011 ◽  
Vol 321 ◽  
pp. 213-217
Author(s):  
Qing Yu ◽  
Jin Lin Wang ◽  
Xiao Chen Sui
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Network Model ◽  
Bp Neural Network ◽  
Error Function ◽  
Detection Algorithm ◽  
Test Time ◽  
Training Time ◽  
Network Intrusion

The basic idea of twice genetic algorithm optimization of BP neural network model(TGB)is rough selection network model using genetic algorithm, then use BP neural network to determine the parameters which can make the error function obtained the minimum and determine its position in the parameter space, then the genetic algorithm again to solve the problem of possible local minima.Feature selection is a new formulation of dimension reduction methods. It can simplify the size of neural network and improve real-time and the accuracy of the system.The simulation results TGB-based network intrusion detection algorithm improve intrusion detection rate of samples in different degrees. It can reduce significantly training time and test time. It further demonstrates the effectiveness and feasibility of this method. The study is very useful to detect materials. So from the analysis, you can learn some skills for materials detecting.

scholarly journals Artificial Neural Network Approach for Solving Fuzzy Fractional order initial value problems under H-differentiability

2020 ◽  
Author(s):  
Somayeh Ezadi ◽  
Tofigh Allahviranloo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Initial Conditions ◽  
Error Function ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Neural Network Approach ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper aims to solve the celebrated Fuzzy Fractional Differential Equations (FFDE) using an Artificial Neural Network (ANN) technique. Compared to the integer order differential equation, the proposed FFDE can better describe several real application problems of various physical systems. To accomplish the aforementioned aim, the error back propagation algorithm and a multi-layer feed forward neural architecture are utilized using the unsupervised learning in order to minimize the error function as well as the modification of the parameters such as weights and biases. By combining the initial conditions with the ANN, output provides an appropriate approximate solution of the proposed FFDE. Then, two illustrative examples are solved to confirm the applicability of the concept as well as to demonstrate both the precision and effectiveness of the developed method. By comparing with some traditional methods, the obtained results reveals a close match that confirms both accuracy and correctness of the proposed method.

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