Abstract

     The similarity measure in this paper is verified for Pythagorean fuzzy sets (PFSs). We point out that a similar measure for two PFSs is a Pythagorean fuzzy value (PFV). To calculate the similarity measure of two PFSs, it is logical that their similarity should be vague. Therefore, we introduce for the first time, as far as we know, the similarity measures for two PVFs is the same as PFV. For calculating PFV of similar measure, we first present the method for calculating Pythagorean similar measure for two PFV by using T-norm and S-norm and then extend it to calculate similar measure for two PFSs. Finally, a numerical example is provided to illustrate the validity and applicability of the presented decision-making method.

Introduction

     In many multi-criteria decision making (MCDM) problems, the decision maker has to use vague (qualitative) values to decide. In the face of qualitative values, for the first time, Bellman and his associates introduced the theory of fuzzy sets (FSs) using mathematical modeling. In making decision with fuzzy sets, the decision maker considers only the degree of correctness of the option and cannot take into account the degree of incorrectness. Continuing this trend, Atanassov in [1] introduced the intuitionistic fuzzy sets (IFSs) theory which is a generalization from FSs which included both the degrees of membership and non-membership.

     Atanassov and Gargov [2] presented an interval valued IFS (IVIFS) as a generalization and an intuitionist fuzzy sets (IFS) that makes use of interval value instead of a real number.

Conclusion

     We have successfully introduced a new definition for similarity measure for Pythagorean collections, which is also a PFV. We define the similarity measure of the two PFVs with a PFV, using T-norm, and S-norm and we generalized it to PFS. This method is used for decision making with Pythagoras value, using similarity measure. The advantages of this method include:

     Calculating the similarity size in the form of PFSs takes more information into decision making, and we lose a lot of information, if converted to a real number.

Abstract

     For multi-attribute decision-making problems with interval numbers whose attribute weights are completely unknown, it is difficult to compare the value of interval numbers. So this paper proposes a method based on TOPSIS and the weighted parameter to deal with it. Firstly, this paper transforms the interval number matrix into two exact number matrices which reduces sorting complexity. Secondly, a parameter is given when determining the weights with entropy weight in order to reflect all the information of the interval numbers. Then TOPSIS is used to determine the order of each scheme. In addition, the average value of the ranking number is used to reflect the actual situation better. Finally, the analysis of the car purchase case shows the proposed method is feasible and practical. And the comparative analysis with the other method based on practical application dataset demonstrates the proposed method is stable and effective.

Introduction

     Due to the complex objective world and its uncertainty, it is difficult to describe the relevant attributes in decision-making problems with exact numbers. To make the model closer to the facts, interval numbers without clear preference information are often used when processing data, thus causing interval number multi-attribute decision-making problems, such as [1].

     At present, the research on such problems has gained attention from scholars at home and abroad. The research focuses on the following four aspects. The first is to sort the interval numbers according to possibility degree, relative superiority degree. Li Z W [2] used relative superiority degree and defined a new sorting vector to rank interval numbers. Li D Q [3] found the ranking method of possibility degree may obtain contrary results to the meaning of possibility degree. So a revised ranking by Boolean matrix was discussed. But the scientificity of the new method needs to be further verified. Yao N [4] defined an interval number ranking method considering symmetry axis compensation which consider multiple attitudes of decision makers with different risk appetites. Firozja, M [5] proposed a new interval distance of two fuzzy numbers that satisfy on metric properties. The second is to determine the weight by studying the application of entropy weight method [6], [7]. Dong P Y [8] proposed a combined weight method. Yue Z L [9] developed a determining weights method for group decision-making problems which each individual decision information is interval numbers. The third is to sort the schemes with the help of projection, grey relation analysis, etc [8], [10], [11], [12], [13], [14], [15], [16], [17], [18]. The forth is to propose the methods for group decision-making problems [9], [19], [20].

Conclusion and promotion

     It is difficult to compare the value of two intervals. In this paper, a novel method is proposed to solve multi-attribute decision-making problems with interval numbers. Two types of exact numbers which are left end point and length are used to replace an interval number which facilitates the determination of attribute weights. At the same time, when determining weights, a variety of possible rankings are considered, and the information brought by interval numbers is fully utilized to ensure the accuracy of results.

     When extracting exact numbers from interval numbers, other methods can also be applied, that is, interval numbers can be replaced with the right end point and length, or the left end point and the midpoint, or the right end point and the midpoint. And the other steps are the same as the corresponding content in this paper. All of them can reduce the difficulty of the problem, and the optimal solution obtained is the same as the method presented in this paper.

     Finally, the feasibility and effectiveness of the proposed method have been demonstrated by car purchasing. The method can also be used to solve the group decision-making problems with intervals.

Abstract

Introduction

Survey on related models

Problem statement

Findings

Managerial implications and practical insights

Conclusion and outlook

References

Author information

Additional information

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Abstract

This research develops a regression-based Robust Optimization (RO) approach to efficiently predict the number of patients with confirmed infection caused by the recent Coronavirus Disease (COVID-19). The main idea is to study the dynamics of the COVID-19 outbreak at the first stage and then provide efficient insights to estimate the necessary resources accordingly. The convex RO with Mean Absolute Deviation (MAD) objective function is utilized to project the course of COVID-19 epidemic in Iran. To validate the performance of the suggested model, a real-case study is investigated and compared to several well-known forecasting models including Simple Moving Average, Exponential Moving Average, Weighted Moving Average and Exponential Smoothing with Trend Adjustment models. Furthermore, the effect of parameter uncertainties is examined using a set of sensitivity analyses. The results demonstrate that by increasing the degree (coefficient) of regression up to 8, MAD value decreases to 1378.12, and consequently, the corresponding equation becomes more accurate. On the other hand, from the 8th degree onwards, MAD value follows an upward trend. Furthermore, by increasing the level of regression uncertainty, MAD value follows a downward trend to reach 1309.28 and the estimation accuracy of the model increases accordingly. Finally, our proposed model achieves the least MAD and the greatest correlation coefficient against the other models.

Introduction

For the first time in December 2019, a new strain of Coronavirus was identified in Wuhan, China. People were diagnosed with pneumonia for no apparent reason and existing vaccines, and treatments were ineffective. The novel Coronavirus disease took the official name of COVID-19 from World Health Organization (WHO) when the number of deaths surpassed 1000 people. On January 30, 2020, WHO issued a statement declaring the novel Coronavirus, a public health emergency, posed a threat to the entire world, not just China. Following the COVID-19 pandemic crisis in various regions of the world, the government of Iran officially confirmed the epidemic on 19 Feb 2020. Since the beginning of the Coronavirus epidemic coincided with the H1N1 flu epidemic, the health system mistakenly recognized it as flu and was slow to notice the onset of the disease (Mardani et al., 2020). One of the challenging issues in the outbreak of COVID-19 is the incubation period, which varies from 3 to 14 days (Tirkolaee, Abbasian, et al., Tirkolaee, Hadian, et al., 2020). That is why the treatments did not work on patients infected by the COVID-19.

To date, some countries product vaccine and only some of them is successful. According to WHO, COVID-19 is an acute respiratory syndrome that is transmitted through respiratory droplets and airways. The examination of this disease needs the serious attention of governments to take the essential measures to reduce the impact of this global epidemic.

From 22 Feb 2020, the COVID-19 risk status went from white to yellow in Iran. In mid-February 2020, Iran became the second focal point for spreading the Coronavirus in the world after China. In early March 2020 and after the COVID-19 epidemic in Iran, the Iranian minister of health stated that health centers throughout the country should be ready to provide services to the COVID-19 patients. Meanwhile, many public places and events, including schools, higher education institutions and universities, movie theaters, concerts and theater performances, national sports competitions and leagues in Tehran and other cities were gradually closed and government office hours were reduced in several provinces. According to official statistics published by WHO, Iran had the highest number of COVID-19 deaths after the USA, Brazil, India, Mexico, UK, Italy and France by the end of November 2020 (WHO, 2020).

Abstract

Keywords

1. Introduction

2. Derivation of the model

2.1. Original domain

2.2. Construction of the reference domain

2.3. Navier-Stokes equations

2.4. Asymptotic analysis

3. A new generalized lubrication model

4. A new thin fluid layer model

5. Conclusions

Appendix A. Change of variable

Appendix B. Coefficients definition

Appendix C. Derivation of equations to calculate

References

Abstract

In this paper we study the behavior of an incompressible viscous fluid moving between two very close surfaces also in motion. Using the asymptotic expansion method we formally justify two models, a lubrication model and a shallow water model, depending on the boundary conditions imposed. Finally, we discuss under what conditions each of the models would be applicable.

1. Introduction

The asymptotic analysis method is a mathematical tool that has been widely used to obtain and justify reduced models, both in solid and fluid mechanics, when one or two of the dimensions of the domain in which the model is formulated are much smaller than the others.

After the pioneering works of Friedrichs, Dressler and Goldenveizer (see [28] and [30]) the asymptotic development technique has been used successfully to justify beam, plate and shell theories (see, for example, [43], [16], [17], [15], [5], [54], and many others).

This same technique has also been used in fluid mechanics to justify various types of models, such as lubrication models, shallow water models, tube flow models, etc. (see, for example, [25], [24], [18], [3], [37], [55], [36], [31], [6], [2], [29], [26], [32], [33], [9], [23], [45], [46], [47], [48], [49], [50], [21], [22], [34], [35], [40], [41], [42], [10], [11], and many others).

In this work, we are interested in justifying, again using the asymptotic development technique, a lubrication model in a thin domain with curved mean surface. Following the steps of [3], but with a different starting point, we devote sections 2 and 3 to this justification. During the above process we have observed that, depending on the boundary conditions, other models can be obtained, which we show in section 4. In this section we derive a shallow water model changing the boundary conditions that we had imposed in section 3: instead of assuming that we know the velocities on the upper and lower boundaries of the domain, we assume that we know the tractions on these upper and lower boundaries.

Thus, two new models are presented in sections 3 and 4 of this article. These models can not be found in the literature, as far as we know. In addition, the method used to justify them allows us to answer the question of when each of them is applicable. In section 5 we discuss the models yielded, as well as the difference between one model and another depending on the boundary conditions, reaching the conclusion that the magnitude of the pressure differences at the lateral boundary of the domain is key when deciding which of the two models best describes the fluid behavior.

1- Introduction

2- Modelling and deformation mechanism

3- Characterization of mechanical properties

4- Conclusions

References

Abstract

Artificial auxetic materials with negative Poisson’s ratio enable distinctive elastic response in the direction orthogonal to the loaded direction, i.e. shrinking when compressed and expanding when stretched, compared to conventional materials. Such distinctive mechanical characteristic makes auxetic materials unique in practice. Current studies in this aspect focus mainly on the realization of beam-dominated microstructures such as re-entrant and chiral lattices and of cellular microstructures with orthogonal elliptical hole pattern. In this study, a novel two-dimensional auxetic microstructure is designed by introducing peanut-shaped holes in solid bulk matrix. Compared to the microstructure with elliptical hole pattern, the present design can produce slightly larger negative Poisson’s ratio and achieve significantly lower stress level. The samples consisting of a number of centimeter-scale unit cells with the peanut-shaped holes are fabricated efficiently via additive manufacturing technique. Experiment and finite element simulation of tensile test are carried out on the specific sample to demonstrate the auxetic effect of the present design and simultaneously verify the computational model. Finally, effects of some parameters on Poisson’s ratio, which may control the auxetic behavior of the present microstructure, are discussed for better understanding deformation mechanism of the proposed auxetic material.

Introduction

The concept of metamaterials with specially designed artificial microstructure made by additive manufacturing technology has recently received more and more attention because of its unconventional physical properties that are previously inaccessible in conventional materials [1–4]. With these man-made metamaterials, engineering materials with various properties have been developed including negative Poisson’s ratio (auxetic properties) [5–11], zero/negative thermal expansion materials [12,13], cloaking materials [14,15], phononic materials [16–19], twistable materials [20,21], and materials with vibration absorption [22,23]. Among these examples, mechanical consistence between longitudinal and orthogonal transverse elastic deformations has been achieved in two-dimensional (2D) and three-dimensional (3D) cellular metamaterials with well-designed cellular topologies [24], i.e. re-entrant shape (Fig. 1a), chiral shape (Fig. 1b) and elliptical hole shape (Fig. 1c) [5–11]. In the static case, such mechanical consistence is conflicted to the fact that a conventional elastic solid cannot expand or shrink laterally when stretched or compressed longitudinally (Fig. 1d) [6,25,26]. It has been revealed that the auxetic behavior of a material can be regarded as a consequence of rotation of elastic cell when it is subjected to a compressive or tensile load [5]. Therefore, auxetic structures with negative Poisson’s ratio are beneficial to enhancing shear rigidity, indentation resistance, toughness, energy dissipation ability and acoustic absorption ability in various applications [27–31]. However, the current re-entrant and chiral metamaterials [9,10] with negative Poisson’s ratio are generally designed to have beamdominated microstructures with bending dominated topologies. As a result, they are known to have low macroscopic stiffness, high flexibility, and high porosity. More seriously, joints with sharp corners in the re-entrant and chiral auxetic structures could lead to high stress concentration [32]. As an alternative to the beam-dominated auxetic microstructures, the orthogonal elliptical hole pattern in solid material was revealed to be beneficial in achieving controllable auxetic response. However, the elliptical hole usually leads to high stress level too. In order to achieve the balance of stiffness and flexibility, and meanwhile possess smoothed boundary and controllable porosity, in this study, a new two-dimensional elastic cellular metamaterial with a negative Poisson’s ratio is designed, fabricated, and characterized.

1- Introduction

2- Weak Galerkin finite element schemes

3- Well posedness

4- Error estimates in energy norm

5- Error estimates in L 2 norm

6- Numerical experiments

References

Abstract 

A stabilizing/penalty term is often used in finite element methods with discontinuous approximations to enforce connection of discontinuous functions across element boundaries. Removing stabilizers from discontinuous Galerkin finite element methods will simplify formulations and reduce programming complexity significantly. The goal of this paper is to introduce a stabilizer free weak Galerkin (WG) finite element method for second order elliptic equations on polytopal meshes. This new WG method keeps a simple symmetric positive definite form and can work on polygonal/polyhedral meshes. Optimal order error estimates are established for the corresponding WG approximations in both a discrete H 1 norm and the L 2 norm. Numerical results are presented verifying the theorem.

6. Numerical experiments

We solve the following Poisson equation on the unit square:
− ∆u = 2π 2 sin πx sin πy, (x, y) ∈ Ω = (0, 1)2 , (6.1)
with the boundary condition u = 0 on ∂Ω. In the first computation, the level one grid consists of two unit right triangles cutting from the unit square by a forward slash. The high level grids are the half-size refinements of the previous grid. The first three levels of grids are plotted in Fig. 6.1. The error and the order of convergence are shown in Table 6.1. The numerical results confirm the convergence theory. In Fig. 6.2, we plot the finite element solution and the discretization errors on triangular and on polygonal grids. We can see, with same number of unknowns, the solutions on triangular grids are more accurate than those on polygonal grids. This can also be seen from the two data tables. In the next computation, we use a family of polygonal grids (with 12-side polygons) shown in Fig. 6.3. The numerical results in Table 6.2 indicate that the polynomial degree j for the weak gradient needs to be larger, which confirms the theory: j depending on the number of edges of a polygon. The convergence history confirms the theory.

1- Introduction

2- Basic Terminologies

3- Applications of Game Theory in Various fields of Computer Science

4- Comparative Study of Uses of Different Game Theoretic Models

5- Conclusion and Future Work

References

Abstract

Game theory has found widespread use in various fields like Economics, Biology, Political Science, etc. and forms an aegis for logical decision making in these areas. In computer science, due to advancing technologies, there has been a pressing need to use game theory in various problems due to the lack of scalability of traditional solutions. There has been ongoing research in various fields of computer science like security, machine learning, cloud computing, etc. where game theoretic approaches are extensively used. In this paper, we present a review on game theoretical approaches to various fields in computer science such as privacy preservation, network security and intrusion detection and resource optimization. In the end, this paper provides a comparative study of various game models used in different applications in a tabular format.

Introduction

Game Theory is described as the study of strategic interactions among rational individuals. One of the major assumptions of Game Theory is that all individuals are “rational”. This means that each individual has well-defined objectives and each individual will implement the best available strategy to achieve them. Generally, Game Theory includes:
● Identification of the players and analysing the payoff for each player.
● Using various equilibrium strategies to make descriptive or prescriptive predictions.[8][9] Thus, it can be said that Game Theory attempts at finding solutions that are in the best interests of all the players. Game Theory is used to develop strategies to maintain an equilibrium between data utility and preservation of privacy of data in Privacy Preservation. Game Theory in Network Security and Intrusion Detection is used to provide a mathematical framework of the strategies between the Network Attackers and System Defenders. The layout of the paper is as follows: Section 2 consists of the basic terminologies required to understand Game Theory and its various components. Section 3 describes the applications of Game Theory in Computer Science. This is followed by a comparative table that provides the details of the various types of Game Models used to solve different problems in Computer Science, which is present in Section 4 of the paper. Section 5 provides the conclusion of the survey.

Introduction

In recent years, there are many researches use Lie symmetry analysis to partial differential equations (PDEs) which arising from physics, chemistry , economics and other fields [1–7]. The investigation of exact analytical solutions of PDEs play an important role for a long time. Lie symmetry analysis is one of the most effective methods for finding the exact analytical solutions of differential equations, and many authors used this method to find the analytical solutions of PDEs [8– 12]. Lie symmetry analysis method was originally developed in the 19th century by the Norwegian mathematician Sophus Lie and developed in differential equations since Bluman and Cole proposed similarity theory for differential equations in 1970s [3, 7]. Over the last forty years, there was a considerable development in PDEs which arise in mathematical finance [13–19]. It was worth pointing out that Bordag and Chmakova’s pioneering paper studied the evaluation of an option hedge-cost under relaxation of the price-taking assumption by Lie symmetry analysis method. In particular, they found some analytical solutions to a nonlinear Black-Scholes equation which incorporates the feedback-effect of a large trader in case of market illiquidity and showed that the typical solution would have a payoff which approximates a strangle, then used these solutions to test numerical schemes for solving a nonlinear Black-Scholes equation [16]. In this paper, we consider the following parabolic Monge-Ampere equation in ` the optimal investment theory [17]: usuyy + ryuyuyy − θu 2 y = 0, (1.1) where r, θ > 0 are constants. Existence of solutions to initial value problem for this equation were showed in [18]. The rest of the paper is organized as follows. In Section 2, we recall the model Eq.(1.1) arise from optimal investment of mathematical finance theory. In Section 3, vector fields and optimal system are given by employing Lie symmetry analysis method. In Section 4, the similarity variables and analytical solutions of Eq.(1.1) are obtained by using optimal system. Finally, conclusions are presented at the end of the paper.

1- Introduction

2- Related study

3- Methodology

4- Experimental result

5- Discussion

6- Conclusion and future work

References

Abstract

Diabetes causes a large number of deaths each year and a large number of people living with the disease do not realize their health condition early enough. In this study, we propose a data mining based model for early diagnosis and prediction of diabetes using the Pima Indians Diabetes dataset. Although K-means is simple and can be used for a wide variety of data types, it is quite sensitive to initial positions of cluster centers which determine the final cluster result, which either provides a sufficient and efficiently clustered dataset for the logistic regression model, or gives a lesser amount of data as a result of incorrect clustering of the original dataset, thereby limiting the performance of the logistic regression model. Our main goal was to determine ways of improving the k-means clustering and logistic regression accuracy result. Our model comprises of PCA (principal component analysis), k-means and logistic regression algorithm. Experimental results show that PCA enhanced the k-means clustering algorithm and logistic regression classifier accuracy versus the result of other published studies, with a k-means output of 25 more correctly classified data, and a logistic regression accuracy of 1.98% higher. As such, the model is shown to be useful for automatically predicting diabetes using patient electronic health records data. A further experiment with a new dataset showed the applicability of our model for the predication of diabetes.

Introduction

Diabetes stands among the top 10 causes of death for 2016. Diabetes killed 1.6 million people in 2016, up from less than 1 million in 2000. With this figure diabetes replaced HIV/AIDS as the seventh top cause of death [1]. The number of people with diabetes has risen from 108 million in 1980 to 422 million in 2014, with the global prevalence of diabetes among adults over 18 years of age rising from 4.7% in 1980 to 8.5% in 2014 [2]. By 2040, 642 million adults (1 in 10 adults) are expected to have diabetes. Also, 46.5% of those with diabetes have not been diagnosed [3]. In order to reduce the number of deaths attributable to diabetes, it is essential that methods and techniques that will aid in early diagnosis of diabetes be devised, because a large number of deaths in diabetic patients are due to late diagnosis. In order to achieve cutting-edge techniques for the early diagnosis of diabetes, we need to utilize advanced information technology, and data mining is a suitable field for this. Data mining offers the ability to extract and discover previously unknown, hidden, but interesting patterns from a large database repository. These patterns can aid medical diagnosis and decision-making. Various techniques and algorithms have been designed for application in extracting knowledge and information in the diagnosis and treatment of disease from medical databases. PCA is a simple, nonparametric method for extracting relevant information from confusing data sets [4]. When a large dataset is to be clustered into a user specified number of clusters (k), which are represented by their centroids, k-means will cluster the data by minimizing the squared error function [5], and often misclassifies some data due to outliers; also the time complexity will be greater. To overcome these problems, principal components analysis (PCA) can be used to reduce the dataset to a lower dimension, while ensuring that the least information is lost, and providing a better centroid point for clustering. K-means clustering partitions a dataset into different groups of similar objects. Clusters that are highly dissimilar from the others are regarded as outliers and discarded. Logistic regression is an efficient regression predictive analysis algorithm. Its application is efficient when the dependent variable of a dataset is dichotomous (binary).

1- Introduction

2- Preliminary results

3- Existence and uniqueness theorems

4- Stability analysis for the logistic models

5- Numerical discussion

6- Conclusion

References

Abstract

In this article, we study different types of fractional-order logistic models in the frame of Caputo type fractional operators generated by conformable derivatives (Caputo CFDs). We present the existence and uniqueness theorems to solutions of these models and discuss their stability by perturbing the equilibrium points. Finally, we furniture our results by illustrative numerical examples for the studied models.

Introduction

Fractional calculus is a branch of mathematical analysis that takes into consideration the integration and differentiation of real or complex order. In spite of the fact that this kind of calculus is old, it gained popularity and started to catch the interest of scientists only in the last 20 or 30 years because important results were reported when fractional derivatives and integrals were applied to describe many real world phenomena [1–10]. A big virtue of the fractional calculus is that there are many different fractional derivatives or integrals. This virtue gives the opportunity to choose the most appropriate derivative or integral in order to describe complex systems of real world problems eligibly. Nevertheless, in order to have better mathematical models of real world problems, scientists started to disclose some new types of fractional integrals and consequently fractional derivatives using two main methods. The first method is the traditional method based on iterating to find the nth order integral and then replacing n by any number α. Hadamard, generalized fractional operators and the fractional operators generated from conformable derivatives, can be considered as examples of fractional operators obtained using this approach [11–17]. These operators usually have singular kernels. The second method is subject to the limiting process and using some properties of the Dirac-Delta functions.

Introduction

Literature review

Methodology

Case study: information security RA for corrugated cardboard sector

Conclusion

References

Abstract

Risk analysis (RA) contains several methodologies that object to ensure the protection and safety of occupational stakeholders. Multi attribute decision-making (MADM) is one of the most important RA methodologies that is applied to several areas from manufacturing to information technology.With the widespread use of computer networks and the Internet, information security has become very important. Information security is vital as institutions are mostly dependent on information, technology, and systems. This requires a comprehensive and effective implementation of information security RA. Analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) are commonly used MADM methods and recently used for RA. In this study, a new RA methodology is proposed based on AHP–TOPSIS integration extended with Pythagorean fuzzy sets. AHP strengthened by interval-valued Pythagorean fuzzy numbers is used to weigh risk parameters with expert judgment. Then, TOPSIS with Pythagorean fuzzy numbers is used to prioritize previously identified risks. A comparison of the proposed approach with three approaches (classical RA method, Pythagorean fuzzy VIKOR and Pythagorean fuzzy MOORA) is also provided. To illustrate the feasibility and practicality of the proposed approach, a case study for information security RA in corrugated cardboard sector is executed.

Introduction

Information is a tool that people use to communicate among themselves from the moment they start living together. The nature and type of information technology have changed dramatically over the past decade. Simple and single batch applications are transformed into distributed computing environments including multitasking real-time control, and distributed processing. It is at least as important as the information itself to determine that information is valuable or worthless, or to measure the value carried by it. The most general definition of information security is that our own information is not passed on to anyone else. It is a combination of three main elements called “privacy”, “integrity”, and “accessibility”. Information is protected from unauthorized access which is called privacy. Integrity defined as information that is not altered by unauthorized persons. Information is available when authorized people are needed. Information is reachable and available when authorized people are needed which is called accessibility. If any of these three basic security elements are damaged, a security weakness occurs. Information security RA is essential for any corporate organizational system. It is essential to ensure that controls and expenditures are in full compliance with the risks that the organization is experiencing or experienced before. Organizations’ heavy dependence on information systems necessitates managing risks related to them [1]. One of the most important aspects of information security is technical measures. Given better access control policy models, better tools for system assessment and assurance should be resolved, including better ways to detect cryptographic formal evidence, protocols, approved firewalls, intrusions and malicious codes [2].