Machine Learning Based Classifier for Service Function Chains

Using service function chains, Internet Service Providers can customize the use of service functions that process the network flows belonging to their customers. Each network flow is injected into a service chain according to the flow features. Since most of the malicious applications try not to get the proper analysis by imitating some valid and famous applications, classification based on simple flow features may waste processing power by using inappropriate service chains for evasive flows. In this paper, we have explored an application-aware classification approach using machine learning methods. Using CatBoost as a machine learning method, a model is created and used for traffic classification. We have provided some statistical reports on how this approach is compared with simple flow feature-based approaches in malicious environments and how feature selection can impact classification correctness. Choosing the most suitable number of features at the right time can beat traditional approaches in classification quality and provide better results in the service function chaining environment

Eamining and Comparing Data Mining-Based Techniques for Hepatitis Diagnosis

ABSTRACT: Increasing advances in information technology has led to significant growth in sciences. One of the fields in which significant changes has occurred is the medical field. Using data-mining techniques in this branch of science has helped physicians in all subjects, in particular diagnosis of sicknesses. Hepatitis diagnosis is highly difficult due to limited clinical diagnosis of the disease in its early stages. To this end, this paper tries to introduce and recommend the best way to diagnose hepatitis as well as to compare common clustering methods such as decision trees, neural networks, and SVM. Evaluation criteria of classification methods are the accuracy of each of methods and Clementine software along with data base in the University of California has been used to test each method. Obtained results show that neural network algorithm enjoys higher accuracy in comparison with other algorithms. Using neural network algorithm can accurately predict 89.74% hepatitis

Application of the homotopy method for analytical solution of non-Newtonian channel flows

Abstract This paper presents the homotopy series solution of the Navier-Stokes and energy equations for non-Newtonian flows. Three different problems, Couette flow, Poiseuille flow and Couette-Poiseuille flow have been investigated. For all three cases, the nonlinear momentum and energy equations have been solved using the homotopy method and analytical approximations for the velocity and the temperature distribution have been obtained. The current results agree well with those obtained by the homotopy perturbation method derived by Siddiqui et al (2008 Chaos Solitons Fractals 36 182-92). In addition to providing analytical solutions, this paper draws attention to interesting physical phenomena observed in non-Newtonian channel flows. For example, it is observed that the velocity profile of non-Newtonian Couette flow is indistinctive from the velocity profile of the Newtonian one. Additionally, we observe flow separation in non-Newtonian Couette-Poiseuille flow even though the pressure gradient is negative (favorable). We provide physical reasoning for these unique phenomena

A characterization property of the simple group {\rm PSL}\sb 4(5) by the set of its element orders

summary:Let $\omega (G)$ denote the set of element orders of a finite group $G$. If $H$ is a finite non-abelian simple group and $\omega (H)=\omega (G)$ implies $G$ contains a unique non-abelian composition factor isomorphic to $H$, then $G$ is called quasirecognizable by the set of its element orders. In this paper we will prove that the group $PSL_{4}(5)$ is quasirecognizable