ROBUST WAVELET-DOMAIN WATERMARK SCHEME BASED ON FUZZY TECHNOLOGY

2007 ◽  
Vol 15 (06) ◽  
pp. 737-751 ◽  
Author(s):  
CONG JIN
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Wavelet Coefficient ◽  
Multimedia Data ◽  
Discrete Wavelet ◽  
Original Image ◽  
Inference System ◽  
Watermark Scheme ◽  
Watermark Embedding ◽  
Texture Characteristics

Digital watermark technology has been proposed as an effective method to protect the copyright of multimedia data. An adaptive image watermark scheme based on fuzzy inference system that in discrete wavelet transform domain is proposed. By exploiting the brightness and texture characteristics of human visual system and considering properties of the original image wavelet coefficient, a fuzzy inference system is designed whose inputs are parameters of brightness and texture of the original image and output is the strength of embedded watermarks. It ensures that the watermark embedding strength is determined adaptively. The experimental results show that the watermarks embed by the proposed scheme are robust against attacks commonly used image processing methods such as JPEG compression, Gaussian noise, cropping, mean filtering, median filtering, rotation, and rescaling etc. Proposed scheme are shown to provide very good results in term of image imperceptibility, too.

scholarly journals High Impedance Fault Detection in MV Distribution Network using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Izzri Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Muhammad Mansoor ◽  
Mariammal Thirumeni
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Inference System ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Fault ◽  
Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

Characterization and Predictive Analysis of Volatile Financial Markets Using Detrended Fluctuation Analysis, Wavelet Decomposition, and Machine Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 1-31
Author(s):  
Manas K. Sanyal ◽  
Indranil Ghosh ◽  
R. K. Jana
Keyword(s):  
Machine Learning ◽  
Fuzzy Inference System ◽  
Detrended Fluctuation Analysis ◽  
Fuzzy Inference ◽  
Stock Exchange ◽  
Machine Learning Algorithms ◽  
Fluctuation Analysis ◽  
Discrete Wavelet ◽  
Inference System ◽  
Detrended Fluctuation

This paper proposes a granular framework for examining the dynamics of stock indexes that exhibit nonparametric and highly volatile behavior, and subsequently carries out the predictive analytics task by integrating detrended fluctuation analysis (DFA), maximal overlap discrete wavelet transformation (MODWT), and machine learning algorithms. DFA test ascertains the key temporal characteristics of the daily closing prices. MODWT decomposes the time series into granular components. Four pattern recognition algorithms—adaptive neuro fuzzy inference system (ANFIS), dynamic evolving neural-fuzzy inference system (DENFIS), bagging and deep belief network (DBN)—are then used on the decomposed components to obtain granular level forecasts. The entire exercise is performed on daily closing prices of Dow Jones Industrial Average (DJIA), National Stock Exchange of India (NIFTY), Karachi Stock Exchange (KSE), Taiwan Stock Exchange (TWSE), Financial Times Stock Exchange (FTSE), and German Stock Exchange (DAX). MODWT-Bagging and MODWT-DBN appear as superior forecasting models.

scholarly journals Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions

2021 ◽  
Vol 2021 ◽  
pp. 1-10 ◽  
Author(s):  
Abdullah H. Alenezy ◽  
Mohd Tahir Ismail ◽  
S. Al Wadi ◽  
Muhammad Tahir ◽  
Nawaf N. Hamadneh ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Arima Model ◽  
Least Square ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Stock Market Volatility ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. This study employs a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (Db), Least Square (LA-8), Best localization (BL14), and Coiflet (C6) in conjunction with adaptive network-based fuzzy inference system (ANFIS). We have selected oil price (Loil) and repo rate (Repo) as input values according to correlation, the Engle and Granger Causality test, and multiple regressions. The input variables in this study have been collected from Saudi Authority for Statistics and Saudi Central Bank. The output variable is obtained from Tadawul. The performance of the proposed model (MODWT-LA8-ANFIS) is evaluated in terms of mean error (ME), root mean square error (RMSE), and mean absolute percentage error (MAPE). Also, we have compared the MODWT-LA8-ANFIS model with traditional models, which are autoregressive integrated moving average (ARIMA) model and ANFIS model. The obtained results show that the performance of MODWT-LA8-ANFIS is better than that of the traditional models. Therefore, the proposed forecasting model is capable of decomposing in the stock markets.

scholarly journals Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model

2017 ◽  
Vol 49 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Honey Badrzadeh ◽  
Ranjan Sarukkalige ◽  
A. W. Jayawardena
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Stream Flow ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Performance Criteria ◽  
Discrete Wavelet ◽  
Inference System ◽  
Input Selection ◽  
Neuro Fuzzy

Abstract In this paper, an advanced stream flow forecasting model is developed by applying data-preprocessing techniques on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with an ANFIS model to develop a hybrid wavelet neuro-fuzzy (WNF) model. Different models with different input selection and structures are developed for daily, weekly and monthly stream flow forecasting in Railway Parade station on Ellen Brook River, Western Australia. The stream flow time series is decomposed into multi-frequency time series by discrete wavelet transform using the Haar, Coiflet and Daubechies mother wavelets. The wavelet coefficients are then imposed as input data to the neuro-fuzzy model. Models are developed based on Takagi-Sugeno-Kang fuzzy inference system with the grid partitioning approach for initializing the fuzzy rule-based structure. Mean-square error and Nash-Sutcliffe coefficient are chosen as the performance criteria. The results of the application show that the right selection of the inputs with high autocorrelation function improves the accuracy of forecasting. Comparing the performance of the hybrid WNF models with those of the original ANFIS models indicates that the hybrid WNF models produce significantly better results especially in longer-term forecasting.

scholarly journals High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3330 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Muhammad Mansoor ◽  
Mariammal Thirumeni ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Inference System ◽  
Medium Voltage ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Fault ◽  
Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage (MV) distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using MATLAB software R2014b and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three-phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault from other faults in the power system.

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