Authentication is the process of determining whether someone or something is,
in fact, who or what it is declared to be. As the dependence upon computers and
computer networks grows, the need for user authentication has increased. User’s
claimed identity can be verified by one of several methods. One of the most popular
of these methods is represented by (something user know), such as password or
Personal Identification Number (PIN). Biometrics is the science and technology of
authentication by identifying the living individual’s physiological or behavioral
attributes. Keystroke authentication is a new behavioral access control system to
identify legitimate users via their typing behavior. The objective of this paper is to
provide user authentication based on keystroke dynamic in order to avoid un
authorized user access to the system. Naive Bayes Classifier (NBC) is applied for
keystroke authentication using unigraph and diagraph keystroke features. The
unigraph Dwell Time (DT), diagraph Down-Down Time (DDT) features, and
combination of (DT and DDT) are used. The results show that the combination of
features (DT and DDT) produces better results with low error rate as compared
with using DT or DDT alone.
Breast cancer was one of the most common reasons for death among the women in the world. Limited awareness of the seriousness of this disease, shortage number of specialists in hospitals and waiting the diagnostic for a long period time that might increase the probability of expansion the injury cases. Consequently, various machine learning techniques have been formulated to decrease the time taken of decision making for diagnoses the breast cancer and that might minimize the mortality rate. The proposed system consists of two phases. Firstly, data pre-processing (data cleaning, selection) of the data mining are used in the breast cancer dataset taken from the University of California, Irvine machine learning repository in this stage we
... Show MoreBackground Direct-acting antivirals (DAAs) combination therapies from various mechanisms of action and families have been revolutionized the management landscape of chronic hepatitis C virus (HCV). Ombitasvir, paritaprevir with ritonavir (OBV/PTV/r) ± ribavirin (RBV) is approved to treat HCV genotype 4 (GT4) infection. Here, our objective was to delineate the efficacy and safety of OBV/PTV/r plus RBV in treating of Egyptian naïve patients infected with HCV GT4.
Methods a cohort of 100 Egyptian patients infected with HCV GT4 was allocated and administered orally OBV/PTV/r with RBV. The primary endpoint of our study was a sustained virological response (HCV RNA < 12 IU/mL) 12 weeks after the c
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stract This paper includes studying (dynamic of double chaos) in two steps: First Step:- Applying ordinary differential equation have behaved chaotically such as (Duffing's equation) on (double pendulum) equation system to get new system of ordinary differential equations depend on it next step. Second Step:- We demonstrate existence of a dynamics of double chaos in Duffing's equation by relying on graphical result of Poincare's map from numerical simulation.
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Due to the fact that living organisms do not exist individually, but rather exist in clusters interacting with each other, which helps to spread epidemics among them. Therefore, the study of the prey-predator system in the presence of an infectious disease is an important topic because the disease affects the system's dynamics and its existence. The presence of the hunting cooperation characteristic and the induced fear in the prey community impairs the growth rate of the prey and therefore affects the presence of the predator as well. Therefore, this research is interested in studying an eco-epidemiological system that includes the above factors. Therefore, an eco-epidemiological prey-predator model incorporating predation fear and
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In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.
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In this paper, a Monte Carlo Simulation technique is used to compare the performance of the standard Bayes estimators of the reliability function of the one parameter exponential distribution .Three types of loss functions are adopted, namely, squared error loss function (SELF) ,Precautionary error loss function (PELF) andlinear exponential error loss function(LINEX) with informative and non- informative prior .The criterion integrated mean square error (IMSE) is employed to assess the performance of such estimators
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This paper aims to introduce a concept of an equilibrium point of a dynamical system which will call it almost global asymptotically stable. We also propose and analyze a prey-predator model with a suggested function growth in prey species. Firstly the existence and local stability of all its equilibria are studied. After that the model is extended to an optimal control problem to obtain an optimal harvesting strategy. The discrete time version of Pontryagin's maximum principle is applied to solve the optimality problem. The characterization of the optimal harvesting variable and the adjoint variables are derived. Finally these theoretical results are demonstrated with numerical simulations.
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