scholarly journals Model Identifikasi Pemalsuan Ijazah menggunakan Gabor Wavelet dan Gaussian Mixture Models Super Vektor (GMM-SV)

2020 ◽  
Vol 4 (2) ◽  
pp. 87
Author(s):  
Alfina Alfina ◽  
Dzulgunar Muhammad Nasir

Various cases occur related to certificate falsification and some people and educational institutions have to deal with the law, this problem is not impossible to abuse along with advances and technological innovation with various tools that can be used by anyone. Identifying the diploma document must be of particular concern to tertiary institutions to minimize the associated fake diplomas and the diploma legalization process. In legalizing the diploma for STMIK Indonesia Banda Aceh students, checking the authenticity of the certificate is only by bringing the original certificate and photocopy of the certificate or by contacting the academic party who issued the certificate, this process is sometimes missed by officers when the queue is crowded. The specific objectives of the research include implementing a model and feature method of Gabor Wavelet and Gaussian Mixture Models Super Vector (GMM-SV) for document identification to speed up diploma identification. The flow of this research starts from the input in the form of a basic image as an image that a reference for the authenticity of the diploma. Then the test image input is an image that will be tested for authenticity. The results showed that using the Gabor Wavelet feature and the Gaussian Mixture Models Super Vector (GMM-SV) could identify fake diplomas with an accuracy rate of 92.8%.Keywords:Model, Identification, Certificate Falsification, Gabor Wavelet, Gaussian Mixture Models Super Vector (GMM-SV).

2017 ◽  
Vol 34 (10) ◽  
pp. 1399-1414 ◽  
Author(s):  
Wanxia Deng ◽  
Huanxin Zou ◽  
Fang Guo ◽  
Lin Lei ◽  
Shilin Zhou ◽  
...  

2013 ◽  
Vol 141 (6) ◽  
pp. 1737-1760 ◽  
Author(s):  
Thomas Sondergaard ◽  
Pierre F. J. Lermusiaux

Abstract This work introduces and derives an efficient, data-driven assimilation scheme, focused on a time-dependent stochastic subspace that respects nonlinear dynamics and captures non-Gaussian statistics as it occurs. The motivation is to obtain a filter that is applicable to realistic geophysical applications, but that also rigorously utilizes the governing dynamical equations with information theory and learning theory for efficient Bayesian data assimilation. Building on the foundations of classical filters, the underlying theory and algorithmic implementation of the new filter are developed and derived. The stochastic Dynamically Orthogonal (DO) field equations and their adaptive stochastic subspace are employed to predict prior probabilities for the full dynamical state, effectively approximating the Fokker–Planck equation. At assimilation times, the DO realizations are fit to semiparametric Gaussian Mixture Models (GMMs) using the Expectation-Maximization algorithm and the Bayesian Information Criterion. Bayes’s law is then efficiently carried out analytically within the evolving stochastic subspace. The resulting GMM-DO filter is illustrated in a very simple example. Variations of the GMM-DO filter are also provided along with comparisons with related schemes.


2013 ◽  
Vol 61 (12) ◽  
pp. 1696-1709 ◽  
Author(s):  
Paulo Drews ◽  
Pedro Núñez ◽  
Rui P. Rocha ◽  
Mario Campos ◽  
Jorge Dias

PLoS ONE ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. e0157239 ◽  
Author(s):  
Anna Magdalena Vögele ◽  
Rebeka R. Zsoldos ◽  
Björn Krüger ◽  
Theresia Licka

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