# computer network 2019-IEEE PAPERS

computer network is a digital telecommunications network which allows nodes to share resources. In computer networks, computing devices exchange data with each other using connections (data links) between nodes.

** Mathematical Models for Stability in Computer Network **

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o protect cyber world from different kind of malicious objects, an attempt has been made to Develop an e-epidemic SIQRS (Susceptible, Infectious, Quarantine, Recovered Susceptible) model for the transmission of malicious objects in computer network . Basic reproduction

** User acceptance in a computer -supported collaborative learning (CSCL) environment with social network awareness (SNA) support**

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theoretical models used to investigate the determinants that affect the acceptance of Information technologies include the theory of reasoned action (TRA)(Ajzen Fishbein, 980) and the theory of planned behaviour (TPB)(Ajzen, 1991), as well as the technology

** Improve Computer Visualization of Architecture Based on the Bayesian Network **

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computer visualization has marvelous effects when it is applied in various fields, especially n architectural design. As an emerging force in the innovation industry, architects and Design agencies have already demonstrated the value of architectural visual products in

** Sensing and Compensating the Thermal Deformation of a Computer -numerical-control Grinding Machine Using a Hybrid Deep-learning Neural Network **

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thermal error plays a deterministic role in the machining precision of computer – Numerical control (CNC) tool machinery. Previously, three ways had been proposed to overcome thermal error problems: prevention, restraint, and compensation. The first two

** A DYNAMIC e-EPIDEMIC MODEL FOR THE ATTACK AGAINST THE SPREAD OF VIRUS IN COMPUTER NETWORK **

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Internet worms/viruses cause a serious threat to the Internet security. In order to successfully defend against Internet worms/virus, vaccination is one of most effective measures for the minimize the spared of computer virus. In this paper we develop a new e-epidemic (e-SVIR)

** through a computer network . This model is a discrete-time analog of the model presented in . Suppose there are N computers in the network . Each computer **

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We are interested in the probability distribution for the number of infected computers i. therefore, our Markov chain will have N+ 1 nodes each of which corresponds to i= 0, 1,, N. et at each moment of time the probability for a healthy computer with i infected neighbors to

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