Plagiarism Detection Framework Using Monte Carlo Based Artificial Neural Network for Nepali Language

2018 ◽  
Author(s):  
Rakesh Kumar Bachchan ◽  
Arun Kumar Timalsina
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Plagiarism Detection ◽  
Artificial Neural

scholarly journals Using Monte Carlo method to estimate the behavior of neural training between balanced and unbalanced data in classification of patterns

2018 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
Paulo Marcelo Tasinaffo ◽  
Gildárcio Sousa Gonçalves ◽  
Adilson Marques da Cunha ◽  
Luiz Alberto Vieira Dias
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Mean Squared Error ◽  
Classification Problem ◽  
Computational Environment ◽  
The Monte Carlo Method ◽  
Proposed Model ◽  
Artificial Neural

This paper proposes to develop a model-based Monte Carlo method for computationally determining the best mean squared error of training for an artificial neural network with feedforward architecture. It is applied for a particular non-linear classification problem of input/output patterns in a computational environment with abundant data. The Monte Carlo method allows computationally checking that balanced data are much better than non-balanced ones for an artificial neural network to learn by means of supervised learning. The major contribution of this investigation is that, the proposed model can be tested by analogy, considering also the fraud detection problem in credit cards, where the amount of training patterns used are high.

scholarly journals Investigating the Electronic Coefficients in Ne-Hg Mixtures: Comparing a Monte Carlo and Artificial Neural Network Model

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Rafid Abbas Ali ◽  
Faten Sajet Mater ◽  
Asmaa Satar Jeeiad Al-Ragehey
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Network Architecture ◽  
Vapor Concentration ◽  
Neural Network Architecture ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
New Applications

Electron coefficients such as drift velocity, ionization coefficient, mean electron energy and Townsend energy for different concentrations of Hg 0.1%, 1%, 10% and 50% in the Ne-Hg mixture at a reduced electric field were calculated using two approaches taking into account inelastic collisions: The Monte Carlo simulation, and an artificial neural network. The effect of Hg vapor concentration on the electron coefficients showed that insignificant additions of mercury atom impurities to Neon, starting from fractions of a percent, affect the characteristics of inelastic processes and discharge, respectively. The aim of this paper is to explore the new applications of neural networks. The Levenberg-Marquardt algorithm and artificial neural network architecture employed was presented in this work to calculate the electron coefficients for different concentrations of Hg in Ne-Hg mixtures. The artificial neural network has been trained with four models (M1, M2, M3, M4), and analysis of the regression between the values of an artificial neural network and Monte Carlo simulation indicates that the M2 output provided the best perfect correlation at 100 Epochs, and the output data obtained was closest to the target data required through using the different stages of artificial neural network development starting with design, training and testing.

Estimating the Uncertainty of a Multilayer Perceptron Using the Monte Carlo Method

2012 ◽  
Vol 628 ◽  
pp. 324-329
Author(s):  
F. García Fernández ◽  
L. García Esteban ◽  
P. de Palacios ◽  
A. García-Iruela ◽  
R. Cabedo Gallén
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Multilayer Perceptron ◽  
Good Accuracy ◽  
Output Uncertainty ◽  
The Monte Carlo Method ◽  
Artificial Neural

Artificial neural networks have become a powerful modeling tool. However, although they obtain an output with very good accuracy, they provide no information about the uncertainty of the network or its coverage intervals. This study describes the application of the Monte Carlo method to obtain the output uncertainty and coverage intervals of a particular type of artificial neural network: the multilayer perceptron.

Evaluation of virtual point detector, for HPGe spectrometers of different sizes, using Monte Carlo simulations, and artificial neural networks

2020 ◽  
Vol 17 (1) ◽  
pp. 15
Author(s):  
Sedigheh Sina ◽  
Zahra Molaeimanesh ◽  
Mehrnoosh Karimipoorfard ◽  
Zeinab Shafahi ◽  
Maryam Papie ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Pulse Height ◽  
Height Distribution ◽  
Hpge Detectors ◽  
Virtual Point ◽  
Artificial Neural ◽  
Point Detector

The virtual point detector concept is a useful concept in gamma ray spectroscopy. In this study, the virtual point detector, h0, was obtained for HPGe detectors of different sizes using MCNP5 Monte Carlo simulations. The HPGe detectors with different radii (rd), and height (hd), having Aluminum, or Carbon windows, were simulated. A point photon source emitting several gammas with certain energies was defined at distance x of the detectors. The pulse height distribution was scored using F8 tally. Finally, artificial neural network was used for predicting the h0 values for every value of hd, rd, and x. Because of the high simulation duration of MCNP code, a trained ANN is used to predict the value of h0 for each detector size. The results indicate that the Artificial Neural Network (ANN) can predict the virtual point detector good accuracy. 

Statistical Vibration Based Damage Identification Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Monte Carlo ◽  
Finite Element Model ◽  
Element Model ◽  
Point Estimation ◽  
Structural Vibration ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) telah digunakan dengan meluas bagi tujuan mengesan kerosakan dalam struktur menggunakan data–data mod dari gegaran. Walau bagaimanapun, ketidakpastian yang wujud dalam model unsur terhingga dan data dari lapangan yang tidak dapat dielakkan boleh menyebabkan kesilapan dalam meramalkan magnitud dan lokasi kerosakan. Dalam kajian ini kaedah statistik digunakan untuk mengambil kira ketidakpastian ini. ANN digunakan untuk meramalkan parameter–parameter kekukuhan dari frekuensi dan mod bentuk bagi sesebuah struktur. Untuk mengambil kira ketidakpastian dalam ramalan, kaedah statistik digunakan di mana kaedah Rossenblueth point estimation diperbandingkan dengan kaedah Monte Carlo diaplikasikan bagi mengambil kira ketidakpastian ini. Keputusan menunjukkan bahawa dengan mengambil kira ketidakpastian dalam membuat ramalan menggunakan ANN, kerosakan boleh diramalkan pada tahap keyakinan yang tinggi. Kata kunci: Artificial neural network; ketidakpastian; kesilapan rawak Artificial Neural Network (ANN) has been widely applied to detect damages in structures based on structural vibration modal parameters. However, uncertainties that inevitably exist in finite element model and measured vibration data might lead to false or unreliable prediction of structural damage. In this study, a statistical approach is proposed to include the effect of uncertainties in the ANN algorithm for damage prediction. ANN is used to predict the stiffness parameters of structures from measured structural vibration frequencies and mode shapes. Uncertainties in the measured data and finite element model of the structure are considered in the prediction. The statistics of the identified parameters are determined using Rossenblueth’s point estimation method and verified by Monte Carlo simulation. The results show that by considering these uncertainties in the ANN model, the damages can be detected with a higher confidence level. Key words: Artificial neural network; uncertainties; random error

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