[PDF] Top 20 Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform
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Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform
... As sputum sounds are non-stationary signals, the method of independent time and frequency domain representations is not highly successful in analyzing and classifying the sputum ...of sputum ... See full document
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An Application of Wavelet Transform and Artificial Neural Network for Microarray Gene Expression based Brain Tumor Sub-classification.
... Discrete cosine transform (DCT) is a function that maps the input signal from spatial domain to frequency domain. DCT transforms the input into linear combinations of weighted basis functions. These basis ... See full document
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EEG Signal Classification into Seizure and Non-Seizure Class using Discrete Wavelet Transform and Artificial Neural Network
... Clustering is an example of Unsupervised Learning where class labels are not presented to system that is trying to discover natural classes in a dataset. Clustering often fails to find known classes because distinction ... See full document
7
Transmission lines fault detection using Discrete Wavelet Transform and artificial neural network algorithm
... transmitting network is to collect the proper electrical transmission, that's used in the way of fault recognition and ...transmitting network offers purest two signals that can become received and ... See full document
8
Emotion Classification from EEG Signals Using Time Frequency DWT Features and ANN
... them, wavelet transform coefficients have been used very commonly with good results [1] ...and Artificial Neural Network (ANN), have been investigated for emotion classification ... See full document
5
Automatic Classification of Transmission Line Faults Using Probabilistic Neural Network and Discrete Wavelet Transform
... an Artificial Neural Network (ANN) and it is observed that ANN is motivated by some features that exist in the brain, this ANN has three basic elements, nodes, weights and activation ... See full document
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Detection of inrush current using Wavelet Transform and Artificial Neural Network (ANN)
... Develops wavelet transform for filtering harmonic currents and the results are classified by using probabilistic neural network ...used. Wavelet transform separates ... See full document
7
Online Full Text
... continuous wavelet transform technique and faults classification using artificial neural network for the purpose of the fault detection has been ...developed. ... See full document
6
Detection and Classification of Heart Premature Contractions via α-Level Binary Neyman-Pearson Radius Test: A Comparative Study
... arrhythmia classification schemes has been on morphology assessment and/or geometrical parameters of the ECG ...Then, using an appropriate mapping for instance, filter banks, discrete or continuous ... See full document
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Accurate Fault Classification of Transmission Line Using Wavelet Transform and Probabilistic Neural Network
... proposed wavelet energy and artificial neural network (ANN) for fault detection and fault classification, ...- transform and PNN for fault classification in thyristor ... See full document
12
Wavelet Transform for Classification of EEG Signal using SVM and ANN
... the classification of EEG signals using wavelet transform (WT) in the year 1997 and also described the application of an artificial neural network (ANN) technique ...the ... See full document
9
AN APPROACH FOR FEATURES MATCHING BETWEEN BILATERAL IMAGES OF STEREO VISION SYSTEM APPLIED FOR AUTOMATED HETEROGENEOUS PLATOON
... discrete wavelet transform) and in terms of artificial intelligence (the artificial neural network multilayer perceptron ...correct classification rate DC and in terms of ... See full document
10
Mallats Wavelet Transform for Face Recognition using Artificial Neural Network
... are using Artificial Neural Networks (ANN) classifiers approaches combined with appropriate feature extraction for recognition of human ...for classification such as K Nearest Neighbors, ... See full document
6
Automated Classification of Brain Tumors using Image Pre Processing and Probabilistic Neural Networks
... tumor classification using an amalgamation of image processing techniques and artificial ...discrete wavelet transform along with threshold based segmentation for separation and de- ... See full document
5
Beat classification of an ecg signal using photoplethysmography and neural network
... This paper presents a simple method to indirectly estimate the range of certain important electrocardiogram (ECG) parameters using photoplethysmography (PPG). The proposed method, termed as PhotoECG, extracts a ... See full document
6
A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network
... sound classification and recognition, more sound characteristic parameters, which includes Linear Predictive Coding (LPC), Linear Predictive Cepstral Coding (LPCC), Linear Spectrum Pair (LSP), formant frequency, ... See full document
13
Artificial Neural Network Classification for Gunshot Detection and Localization System
... Fig 3:- Experimental set-up for recording M16 gunshot Microphones were used as primary sensors. Sound signal properties were used to identify or differentiate gunshot from background noise and other explosive acoustics; ... See full document
5
Shape Optimization of Pedestals Using Artificial Neural Network
... decisions. To sets of arrow are shown in the figure, those pointing from left to right indicate the forward transmission of information-bearing signals through the system. The arrows pointing from right to left signify ... See full document
7
Protection and Controlling of Transmission Lines by using Machine Learning Techinique
... (BP) neural system was connected for assignment of deficiency in ...(RBF) neural system envelops a quicker learning pace and also the adaptability of optional work ...problems, neural network ... See full document
7
Genome-wide classification of dairy cows using decision trees and artificial neural network algorithms
... algorithms for identifying a subset of SNP markers for predicting genomic breeding values (GEBV). Their phenotype of interest was the live body weight from 2093 Brahman cattle. They used a traditional statistical method ... See full document
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