scholarly journals Diagnosis of Pulpitis from Dental Panoramic Radiograph Using Histogram of Gradients with Discrete Wavelet Transform and Multilevel Neural Network Techniques

2021 ◽  
Vol 38 (5) ◽  
pp. 1549-1555
Author(s):  
Antony Vigil ◽  
Subbiah Bharathi
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Oral Cavity ◽  
Discrete Wavelet Transform ◽  
Panoramic Radiograph ◽  
Discrete Wavelet ◽  
Median Filters ◽  
Pulp Inflammation ◽  
Dental Panoramic Radiograph ◽  
Histogram Of Gradients

Radiograph plays the major role of diagnosis, treatment and surgery in the Dental field. There are many types of Intra and extra oral radiographs in which Dental Panoramic Radiograph helps in visualising the full view of the oral cavity. Pulpitis is the dental diseases caused due to the inflammation of the dental pulp from untreated caries, trauma or multiple restorations which leads to Apical Periodontitis. To predict the severity of pulp vitality pulp inflammation has to be evaluated. Radiographs helps the dentist in diagnosing the extent of tooth decay and inflammation. An automatic diagnostic model is proposed using robust algorithms to diagnose pulpits. Dental Panoramic Radiograph is used in the proposed research to diagnose the pulpitis and to classify the normal teeth from the pulpitis. The collected images are pre-processed using Histogram Equalization and filtered using Gaussian and Median filters. Modified K-Means algorithm is applied to segment the bony and teeth area from the oral cavity area. Integral Histogram of Gradients with Discrete Wavelet Transform feature extraction techniques and Multi-Layer Neural Network Classifier is proposed to achieve the accuracy of 91.09% which can be used as an assistive tool by the dentist to diagnose pulpitis.

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

scholarly journals PERAMALAN DATA PENUMPANG KERETA API DENGAN MENGGUNAKAN MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM- RECURRENT NEURAL NETWORK (MODWT-RNN)

2019 ◽  
Vol 12 (2) ◽  
pp. 164
Author(s):  
Mira Andriyani ◽  
Subanar Subanar
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Recurrent Neural Network ◽  
Public Transportation ◽  
Accurate Result ◽  
Nonlinear Models ◽  
Forecasting Model ◽  
Discrete Wavelet ◽  
The Public

The train is one of the public transportation that is very popular because it is affordable and free of congestion. There is often a buildup of train passengers at the station so that it sometimes causes an accumulation of passengers at the station and makes the situation at the station to be not conducive. In order to avoid a buildup of passengers, forecasting the number of passengers can be done. Forecasting is determined based on data in previous times. Data of train passengers in Java (excluding Jabodetabek) forms a non-stationary and contains nonlinear relationships between the lags. One of the nonlinear models that can be used is Recurrent Neural Network (RNN). Before RNN modeling, Maximal Overlap Wavelet Transform (MODWT) was used to make data more stationary. Forecasting model of train passengers in Java excluding Jabodetabek, Indonesia using MODWT-RNN results forecasting with RMSE is 252.85, while RMSE of SARIMA and RNN are 434.97 and 320.48. These results indicate that the MODWT-RNN model gives a more accurate result than SARIMA and RNN.

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