scholarly journals Assessment of Dual Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

2021 ◽  
Author(s):  
Bassam Al-Naami ◽  
Hossam Fraihat ◽  
Jamal Al-Nabulsi ◽  
Abdel-Razzak Al-Hinnawi
Keyword(s):  
Wavelet Transform ◽  
Time Domain ◽  
Heart Sound ◽  
Fuzzy Inference ◽  
Signal To Noise Ratio ◽  
Complex Wavelet Transform ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Complex Wavelet ◽  
Anfis Classifier

Abstract Here we propose a novel de-noising method to improve the outcome of heart sound (HS)-based heart condition identification. We applied Dual Tree Complex Wavelet Transform (DTCWT) in collaboration with Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The method consisted of three steps. First, preprocess to eliminate 50 Hz noise. Second, application of DTCWT to de-noise and reconstruct time-domain HS signal. Third, evaluation of ANFIS on total 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was a 11% increase in SNR after DTCWT, representing a significant improvement in de-noising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc in comparison to other attempts on the same dataset. Therefore, DTCWT is a successful technique in de-noising information such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.

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.

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.

Classification of EEG Signals for Motor Imagery Based on Mutual Information and Adaptive Neuro Fuzzy Inference System

Fuzzy Systems ◽  
2017 ◽  
pp. 347-366
Author(s):  
Shereen A. El-aal ◽  
Rabie A. Ramadan ◽  
Neveen Ghali
Keyword(s):  
Mutual Information ◽  
Motor Imagery ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Imagery Task ◽  
Eeg Signals ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Anfis Classifier ◽  
Maximal Relevance

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.

scholarly journals Mammogram Image Retrieval using Ipso Optimized Anfis Classifier

2019 ◽  
Vol 8 (9S2) ◽  
pp. 799-804
Keyword(s):  
Image Retrieval ◽  
Fuzzy Inference ◽  
Noise Removal ◽  
Research Area ◽  
Query Image ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Dataset ◽  
Mammogram Image ◽  
Anfis Classifier

Content-based image retrieval (CBIR) is an research area over the past years that has attracted research. In various medical applications like mammogram analysis CBIR techniques helps the medical team to get similar set of images from a large medical records to help in diagnosis of a disease. This paper proposes an efficient Content-Based Mammogram Image Retrieval method by using an Optimized Classifier. Initially, the input dataset is preprocessed, in which noise removal and contrast enhancement are done. Next, pectoral muscles of the mammogram images are removed using Single Sided Edge Marking (SSEM). Now, feature extraction is done, in which GLCM features, Gabor features and the Local Pattern with Binary features are being removed. The features that are being removed are classified into three classes namely benign, malignant and normal. An optimized classifier named as Adaptive Neuro Fuzzy Inference System (ANFIS), which is optimized by using the Improved Particle Swarm Optimization (IPSO) technique, is used for classification purpose. Finally, similarity is assessed between the trained feature distance vectors and the feature distance vectors of the input query image. Similarity assessment is done using Euclidean Distance metric and the image that has the lowest distance compared with the query is retrieved. The experimental results are obtained for the proposed system and they are compared with the existing techniques.

scholarly journals Denoising Images by Dual–Tree Complex Wavelet Transform Combined with Meta Heuristic Optimization Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 2621-2627
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Optimization Algorithms ◽  
Optimization Technique ◽  
Threshold Value ◽  
Heuristic Optimization ◽  
Algorithm Optimization ◽  
Complex Wavelet Transform ◽  
Complex Wavelet

Denoising is a prime objective technique for processing images. Image denoising techniques removes the noises present in an image without interrupting its features and contents. The image gets interrupted by channel or processing noise depending on the applications. Thus, the contaminated noises produce degradable image qualities with respect to subjective and objective approach. To overcome this, image denoising approaches were suggested. In the present research, Dual–Tree Complex Wavelet transform (DTCWT) is utilized to achieve image denoising since they perform multi resolution decomposition by two DWT trees. Soft and hard thresholding methods are used to threshold wavelet coefficients. The present research proposes a novel technique to denoise images which gives image information clearly by thresholding and optimization technique. The optimization is carried through different Meta-heuristic optimization Algorithms Genetic Algorithm (GA) and Grey-wolf optimization (GWO) algorithm. Optimization of threshold value is performed after Bayesian method and the observed output produces better results when compared to other techniques involving Visu shrink, Sure shrink and Bayes shrinkbased on peak signal to noise ratio (PSNR) and visual qualities.

scholarly journals Dual tree complex wavelet transform based image denoising for Kalpana satellite images

2018 ◽  
Vol 7 (3.29) ◽  
pp. 269
Author(s):  
Naga Lingamaiah Kurva ◽  
S Varadarajan
Keyword(s):  
Wavelet Transform ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Pictorial Representation ◽  
Complex Wavelet Transform ◽  
Raw Data ◽  
Complex Wavelet ◽  
Kalpana Satellite

This paper presents a new algorithm to reduce the noise from Kalpana Satellite Images using Dual Tree Complex Wavelet Transform technique. Satellite Images are not simple photographs; they are pictorial representation of measured data. Interpretation of noisy raw data leads to wrong estimation of geophysical parameters such as precipitation, cloud information etc., hence there is a need to improve the raw data by reducing the noise for better analysis. The satellite images are normally affected by various noises. This paper mainly concentrates on reducing the Gaussian noise, Poisson noise and Salt & Pepper noise. Finally the performance of the DTCWT wavelet measures in terms of Peak Signal to Noise Ratio and Structural Similarity Index for both noisy & denoised Kalpana images.   

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