![]() Con-volutional Neural Networks (CNNs) architectures are computerized Key words and phrases: Arch fingerprint, plain arch, tented arch, strong arch, dynamical system for arch fingerprint, CNNs architectures, deep learning. Most fingerprint datasets are not categorized to be retained by deep learning computer tools that allow to classify a new fingerprint input image to its class, so finding a dynamical system to categorize fingerprint image data set allows deep learning computer science program to be retrained with more accuracy. The orientation image of the arch fingerprint is visualized in smooth deformation of the phase portrait of a planar system. The global dynamics and the existence and stability of equilibria are studied. In this paper, the classes of the arch fingerprint are modelled in a dynamical system. There are three basic types of fingerprints, one of them is the arch fingerprint. ![]() The analysis of fingerprint is valuable tool to identify suspects and crimes. The time required to build a model is 262, 55, and 28 seconds for GoogleNet, ResNet, and AlexNet, respectively.įingerprints are unique patterns, made by friction ridges and furrows, which appear on the pads of the fingers and thumbs. The obtained result shows that the accuracy for classification is 100%, 75%, and 43.75% for GoogleNet, ResNet, and AlexNet, respectively. The proposed model was implemented and tested using MATLAB based on the FVC2004 dataset. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a pattern mathematical model using GoogleNet, AlexNet, and ResNet Convolutional Neural Network (CNN) architecture and matching techniques based on bifurcation minutiae extraction. Fingerprint classification enables this objective to be accomplished by splitting fingerprints into several categories, but it still poses some difficulties because of the wide intraclass variations and the limited interclass variations since most fingerprint datasets are not categories. It is important to reduce the time consumption during the comparison process in automated fingerprint identification systems when dealing with a large database. The fingerprint is one of the most important biometrics that can be easily captured in an uncontrolled environment without human cooperation. This eliminates identity recognition manual work and enables automated processing. We formulate the basic reinfection number for a variety of epidemiological models.Biometric based access control is becoming increasingly popular in the current era because of its simplicity and user-friendliness. The basic reinfection number with the basic reproduction number together gives a complete measure for disease control whenever reinfections (or relapses) matter. If the basic reinfection number is greater than one, the bifurcation is backward. This number characterizes the type of bifurcation when the basic reproduction number is equal to one. To measure the reinfection forces, this paper defines a second threshold: the basic reinfection number. Causes for this phenomenon include exogenous reinfection, super-infection, relapse, vaccination exercises, heterogeneity among subpopulations, etc. We formulate the basic reinfection number for a variety of epidemiological models.ĪB - Some epidemiological models exhibit bi-stable dynamics even when the basic reproduction number R0 is below 1, through a phenomenon known as a backward bifurcation. N2 - Some epidemiological models exhibit bi-stable dynamics even when the basic reproduction number R0 is below 1, through a phenomenon known as a backward bifurcation. © 2021 American Institute of Mathematical Sciences. T1 - Basic reinfection number and backward bifurcation
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