International Journal of Nonlinear Analysis and Applications
https://ijnaa.semnan.ac.ir/
International Journal of Nonlinear Analysis and Applicationsendaily1Tue, 21 Feb 2017 00:00:00 +0330Tue, 21 Feb 2017 00:00:00 +0330A common fixed point theorem via measure of noncompactness
https://ijnaa.semnan.ac.ir/article_2318.html
In this paper by applying the measure of noncompactness a common fixed point for the maps $T$ and $S$ is obtained, where $T$ and $S$ are self maps continuous or commuting continuous on a closed convex subset $C$ of a Banach space $E$ and also $S$ is a linear map.Common Fixed Point Theorems with Applications to Theoretical Computer Science
https://ijnaa.semnan.ac.ir/article_3674.html
Owing to the notion of L-fuzzy mapping, we establish some common $L$-fuzzy fixed point results&nbsp;for almost $\Theta$-contraction in the setting of complete metric spaces. An application to theoretical&nbsp;computer science is also provided to show the significance of the investigations.Fractals of Generalized $\Theta$-Hutchinson Operator
https://ijnaa.semnan.ac.ir/article_3675.html
The aim of this paper is to construct a fractal of generalized $\Theta$-Hutchinson Operator with the help&nbsp;of a finite family of $\Theta$-contraction mappings, a class of mappings more general than contractions,&nbsp;defined on a complete metric space. Our results unify, generalize and extend various results in the&nbsp;existing literature.Fixed points for Banach and Kannan contractions in $G$-metric spaces endowed with a graph
https://ijnaa.semnan.ac.ir/article_3678.html
In this paper, we discuss the existence of fixed points for Banach and Kannan contractions defined on $G$-metric&nbsp;spaces, which were introduced by Mustafa and Sims, endowed with a graph. Our results generalize and unify some recent results by Jachymski, Bojor and Mustafa and those contained therein. Moreover, we provide some examples to show that our results are substantial improvement of some known results in literature.$(G,\psi)$-Ciric-Reich-Rus contraction on metric space endowed with a graph
https://ijnaa.semnan.ac.ir/article_3724.html
In this paper, we introduce the $(G,\psi)$-Ciric-Reich-Rus contraction on metric space endowed with a&nbsp;graph, such that $(X,d)$ is a metric space, and $V (G)$ is the vertices of $G$ coincides with $X$. We give&nbsp;an example to show that our results generalize some known resultsThe Essential of Applying Nonlinear-Analysis to Validate Experiments, Assessing Superior Brain Functions: Case-Study of a Bayesian-Model of Inhibitory Control in ADHD
https://ijnaa.semnan.ac.ir/article_3946.html
In the last decades, nonlinear methods have been applied, in a large number of studies from the computational neuroscience field, to describe neuronal implementations of superior brain functions. Superior brain functions, called cognitive functions, control our behavior. Therefore, they should be assessed through evaluating individual performances in the experiments, using standard tasks, which represent the condition that cognitive functions are required. The mathematical models of cognitive functions, in neuronal implementation level, are based on real condition of standard cognitive tasks. However, it is not validated whether applied task conditions are appropriate to represent neuronal implementation of a cognitive function. Hence, as a case-study, we used a developed Bayesian Model to assess whether GoNoGo task is valid to be applied for neural measurement and modeling neural implementation of Inhibitory Control (IC). As GoNoGo is the most common task used for neural measurement of impaired cognitive function (IC) in ADHD, we fit the model to behavioral data of two groups of children/adolescents with and without ADHD. The results demonstrated that the model could simulated the behavioral data; and also the model parameters could differentiate the groups significantly. However, neural implementation of IC may not be represented through the rewarded condition of GoNoGo task. We concluded that before modeling neural implementation of cognitive functions, it is essential to apply nonlinear methods to validate current behavioral experiments computationally; or to design new model-based experiments for using in neural measurementsA Nonmonotone Hestenes ans Stiefel Conjugate Gradient Algorithm for Nonsmooth Convex Optimization
https://ijnaa.semnan.ac.ir/article_3958.html
Here, we propose a practical method for solving nonsmooth convex problems by using conjugate gradient type methods. We present a modified HS conjugate gradient method, as one of the most remarkable methods to solve smooth and large-scale optimization problems. In the case that we have a nonsmooth convex problem, by way of the Moreau-Yosida regularization, we convert the nonsmooth objective function to a smooth function and then we use our method, by making use of a nonmonotone line search, for solving a nonsmooth convex optimization problem. We prove that our algorithm converges to an optimal solution under standard condition. Our algorithm inherits the performance of HS conjugate gradient method.Existence result of solutions for a class of nonlinear differential systems
https://ijnaa.semnan.ac.ir/article_5006.html
In this paper, we will discuss the existence of bounded positive solutions for a class of nonlinear&nbsp; differential systems. The objective will be achieved by applying some results and techniques of&nbsp; functional analysis such as Schauder's fixed point theorem and potential theory tools.Solving generalized pantograph equations by shifted orthonormal Bernstein polynomials
https://ijnaa.semnan.ac.ir/article_5007.html
The purpose of this paper is to propose the spectral collocation method to solve&nbsp;linear and nonlinear stochastic It^o-Volterra integral equations (SVIEs). The proposed approach is different from other numerical techniques as we consider the&nbsp;Legendre Gauss type quadrature for estimating It^o integrals. The main characteristic of the presented method is that it reduces SVIEs into a system of algebraic&nbsp;equations. Thus, we can solve the problem by Newton's method. Furthermore,&nbsp; the&nbsp;convergence analysis of the approach is established. The method is computationally attractive, and to reveal the accuracy, validity, and efficiency of the proposed&nbsp;method, some numerical examples and convergence analysis are included.Mittag-Leffler-Hyers-Ulam stability of Prabhakar fractional integral equation
https://ijnaa.semnan.ac.ir/article_5008.html
In this paper, we define and investigate Mittag-Leffler-Hyers-Ulam&nbsp;and Mittag-Leffler-Hyers-Ulam-Rassias stability of Prabhakar fractional integral&nbsp;equation.Fixed point results in partially ordered partial $b_{v}(s)$-metric spaces
https://ijnaa.semnan.ac.ir/article_5010.html
In this paper, some fixed point results for generalized Geraghty type $&alpha;$-admissible contractive mappings and rational type generalized Geraghty contraction mappings are given in partially ordered partial &nbsp;$b_{v}(s)$-metric spaces. Also, a modified version of a partial &nbsp;$b_{v}(s)$-metric space is defined and a fixed point theorem is proved in this space. Finally, some examples are given related to the results.Estimate survival function of the Topp-Leone exponential distribution with application
https://ijnaa.semnan.ac.ir/article_5014.html
This is a new lifetime Exponential "distribution using the Topp-Leone generated family of distributions proposed by Rezaei et al. The new distribution is called the Topp-Leone Exponential (TLE) distribution". What is done in this paper is an estimation of the "unlabeled two parameters for Topp-Leone Exponential distribution model by using the maximum likelihood estimator method to get the derivation of the point estimators for all unlabeled parameters according to iterative techniques as Newton $-$ Raphson method, then to derive Ordinary least squares estimator method". "Applying all two methods to estimate related probability functions; death density function, cumulative distribution function, survival function and hazard function (rate function)".&nbsp;When examining the numerical results for probability survival function by employing mean squares error measure and mean absolute percentage measure, this may lead to work on the best method in modeling a set of real data.New subclasses of meromorphic bi-univalent functions by associated with subordinate
https://ijnaa.semnan.ac.ir/article_5017.html
&lrm;In the present paper&lrm;, &lrm;we define two subclasses $\Sigma(\lambda&lrm;, &lrm;\alpha&lrm;, &lrm;\beta)$&lrm;, &lrm;$\Sigma_{\mathcal C}(\alpha&lrm;, &lrm;\beta)$ of meromorphic univalent functions and subclass $\Sigma_{{\mathcal B},{\mathcal C}}(\alpha&lrm;, &lrm;\beta&lrm;, &lrm;\lambda)$ of meromorphic bi-univalent functions&lrm;. &lrm;Furthermore&lrm;, &lrm;we obtain estimates on the general coefficients $|b_n|~(n \ge1)$ for functions in the subclasses $\Sigma(\lambda&lrm;, &lrm;\alpha&lrm;, &lrm;\beta)$&lrm;, &lrm;$\Sigma_{\mathcal C}(\alpha&lrm;, &lrm;\beta)$ and estimates for the early coefficients of functions in subclass $\Sigma_{{\mathcal B},{\mathcal C}}(\alpha&lrm;, &lrm;\beta&lrm;, &lrm;\lambda)$ by associated subordination&lrm;. &lrm;The results obtained in this paper would generalize and improve those in related works of several earlier authors&lrm;.Expected mean square rate estimation of repeated measurements model
https://ijnaa.semnan.ac.ir/article_5018.html
In this paper, we obtained the estimation corresponding to the expected mean square rate of repeated measurement model depend on maximum likelihood method (MLM), restricted maximum likelihood method (REMLM) and modied restricted maximum likelihood method (MREMLM), and got eight&nbsp; cases that were classified into three types.On the Cauchy dual and complex symmetric of composition operators
https://ijnaa.semnan.ac.ir/article_5020.html
In this paper, firstly we show that some classical properties for Cauchy dual and Moore-Penrose inverse of composition operators, such as complex symmetric and Aluthge transform on $L^{2}(\Sigma)$. Secondly we give a characterization for some operator classes of weak $p$-hyponormal via Moore-Penrose inverse of composition operators. Finally, some examples are then presented to illustrate that, the Moore-Penrose inverse of composition operators lie between these classes.On the $\Psi$-instability of a nonlinear Lyapunov matrix differential equation with integral term as right side
https://ijnaa.semnan.ac.ir/article_5021.html
The aim of this paper is to give sufficient conditions for $\Psi$-instability of trivial solution of a nonlinear Lyapunov matrix differential equation with integral term as right side.A new method for solving three-dimensional nonlinear Fredholm integral equations by Haar wavelet
https://ijnaa.semnan.ac.ir/article_5022.html
In this paper, a new iterative method of successive approximations based on Haar wavelets is proposed for solving three-dimensional nonlinear Fredholm integral equations. The convergence of the method is veriﬁed. The error estimation and numerical stability of the proposed method are provided in terms of Lipschitz condition. Conducting numerical experiments conﬁrm the theoretical results of the proposed method and endorse the accuracy of the method.Predicting drug-target interaction based on bilateral local models using a decision tree-based hybrid support vector machine
https://ijnaa.semnan.ac.ir/article_5023.html
Optimally Local Dense Conditions for the Existence of Solutions for Vector Equilibrium Problems
https://ijnaa.semnan.ac.ir/article_5026.html
With the rapid development of technology, this development led to the emergence of microarray
technology. It has the effect of studying the levels of gene expression in a way that makes it easier
for researchers to observe the expression levels of millions of genes at the same time in a single experiment. Development also helped in the emergence of powerful tools to identify interactions between
target genes and regulatory factors. The main aim of this study is to build models to predicate the
relationship (Interaction) between Transcription Factors (TFs) proteins and target genes by selecting
the subset of important genes (Relevant genes) from original dataset. The proposed methodology
comprises into three major stages: the genes selection, merge datasets and the prediction stage. The
process of reducing the computational space of gene data has been accomplished by using proposed
mutual information method for genes selection based on the data of gene expression. In the prediction, the proposed prediction regression techniques are utilized to predict with binding rate between
single TF-target gene. It has been compared the efficiency of two different proposed regression techniques including: Linear Regression and Random Forest Regression. Two available data sets have
been utilized to achieve the objectives of this study: Gene&rsquo;s expression data of Yeast Cell Cycle
dataset and Transcription Factors dataset. The evaluation of predictions performance has been performed depending on two performance prediction measures (Root Mean Square Error (RMSE) and
Mean Absolute Error (MAE) with (10) Folds-Cross Validation.A Novel Method for Detection of Fraudulent Bank Transactions using Multi-Layer Neural Networks with Adaptive Learning Rate
https://ijnaa.semnan.ac.ir/article_4562.html
Fraud refers to earn wealth including property, goods and services through immoral and non-legalchannels. Fraud has always been in action and experiences an increasing trend worldwide. Fraud infinancial transactions not only leads to losing huge financial resources, but also leads to reductionin trust of customers on using modern banking systems and hence, reduction in efficiency of thesystems and optimal management of financial transactions. In recent years, by emerging new tech-nologies of banking industry, new means of fraud are discovered. Although a new information systemcarry advantages and benefits, new opportunities are made for fraudsters. The applications of frauddetection methods encompasses detection of frauds in an organization, analysis of frauds and alsouser/customer behavior analytics in order to predict future behavior and reduce the fraud risks. Inrecent decades, employing new technologies in management of banking transactions has risen. Banks and financial institutions inevitably migrated from traditional banking to modern online banking to provide effective services. Although, the use of online banking systems improves the management of financial processes and speeds up services to customers of institutions, but some issues would also be carried. Financial frauds is one of the issues which organizations seek to prevent and reduceeffects. In this paper, a novel machine learning based model is presented to detect fraud in electronicbanking transactions using profile data of bank customers. In the proposed method, transactionaldata from banks are leveraged and a multi-layer perceptron neural network with adaptive learningrate is trained to prove the validity of a transaction and hence, improve the fraud detection in elec-tronic banking. The proposed method shows promising results compared with logistic regression andsupport vector machines.Energy Aware Multi Objective Algorithm for Task Scheduling on DVFS-Enabled Cloud Datacenters using Fuzzy NSGA-II
https://ijnaa.semnan.ac.ir/article_4666.html
Nowadays, energy consumption is curtailed in an effort to further protect the environment as well as to avoid service level agreement (SLA) breach, as critical issues in task scheduling on heterogeneous computing centers. Any reliable task scheduling algorithm should minimize energy consumption, makespan, and cost for cloud users and maximize resource utilization. However, reduction of energy consumption leads to larger makespan and decreases load balancing and customer satisfaction. Therefore, it is essential to obtain a set of non-domination solutions for these multiple, conflicting objectives, as a non-linear, multi-objective, NP-hard problem. This paper formulates the energy efficient task scheduling in green data centers as a multi-objective optimization problem so that fuzzy Non-dominated Sorting Genetic Algorithm 2 (NSGA-II) has been applied using the concept of Dynamic Voltage Frequency Scaling (DVFS). In this procedure, we adopted fuzzy crossover and mutation for optimal convergence of initial solutions. For this purpose, the binary variance function of gene values and the mean variance function of objective values are proposed for fuzzy control of mutation rate, increasing the variation in the optimal Pareto front as well as the correct frequency variance function of the processors engaged in scheduling to control the crossover rate. This serves to add the objective of indirect load balancing to the optimization objectives, thereby to replace the three-objective optimization process with four-objective optimization. In the experiments, the proposed NSGA-II with fuzzy algorithm is compared against the NSGA-II algorithm, involving three scheduling strategies namely Green, Time and Cost Oriented Scheduling Strategy. The simulation results illustrate that the newly method finds better solutions than others considering these objectives and with less iteration. In fact, the optimal Pareto solutions obtained from the proposed method improved the objectives of makespan, cost, energy and load balance by 4%, 17%, 1% and 13%, respectively.Coupled fixed point theorems in partially ordered complex valued metric spaces with application
https://ijnaa.semnan.ac.ir/article_4921.html
In this paper, we prove some coupled fixed point theorems for nonlinear contraction type&nbsp;mappings in complete complex valued metric spaces endowed with partial order. We support our results by establishing an illustrative example. Also we give an application of this results to the solution of the Urysohn type integral equations.&nbsp;