scholarly journals Brain Tumor Epilepsy Seizure Identification using Multi-Wavelet Transform, Neural Network and Clinical Diagnosis Data

2013 ◽  
Vol 67 (2) ◽  
pp. 10-17
Author(s):  
Sharanreddy. M ◽  
P. K. Kulkarni
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Wavelet Transform ◽  
Clinical Diagnosis

scholarly journals Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm

2019 ◽  
Vol 64 (2) ◽  
pp. 195-205 ◽  
Author(s):  
Behrouz Alizadeh Savareh ◽  
Hassan Emami ◽  
Mohamadreza Hajiabadi ◽  
Seyed Majid Azimi ◽  
Mahyar Ghafoori
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Brain Tumor ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Machine Learning Techniques ◽  
Tumor Segmentation ◽  
Mathematical Functions ◽  
Brain Tumor Segmentation ◽  
Learning Techniques

Abstract Purpose: Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. Materials and methods: In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Results: Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Conclusion: Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

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.

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