scholarly journals Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Qingqing Lu ◽  
Jiexin Pu ◽  
Zhonghua Liu
Keyword(s):  
Feature Extraction ◽  
Ground Penetrating Radar ◽  
Classification Accuracy ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Noisy Environment ◽  
Feature Based ◽  
Approximation Coefficients ◽  
Ground Penetrating

Ground penetrating radar (GPR) is a powerful tool for detecting objects buried underground. However, the interpretation of the acquired signals remains a challenging task since an experienced user is required to manage the entire operation. Particularly difficult is the classification of the material type of underground objects in noisy environment. This paper proposes a new feature extraction method. First, discrete wavelet transform (DWT) transforms A-Scan data and approximation coefficients are extracted. Then, fractional Fourier transform (FRFT) is used to transform approximation coefficients into fractional domain and we extract features. The features are supplied to the support vector machine (SVM) classifiers to automatically identify underground objects material. Experiment results show that the proposed feature-based SVM system has good performances in classification accuracy compared to statistical and frequency domain feature-based SVM system in noisy environment and the classification accuracy of features proposed in this paper has little relationship with the SVM models.

Feature Extraction and Classification of Images Based on Corner Invariant Moments

2013 ◽  
Vol 475-476 ◽  
pp. 374-378
Author(s):  
Xue Ming Zhai ◽  
Dong Ya Zhang ◽  
Yu Jia Zhai ◽  
Ruo Chen Li ◽  
De Wen Wang
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Image Feature ◽  
Support Vector ◽  
System Management ◽  
Image Feature Extraction ◽  
Invariant Moments ◽  
Harris Corner ◽  
Classification Of Images

Image feature extraction and classification is increasingly important in all sectors of the images system management. Aiming at the problems that applying Hu invariant moments to extract image feature computes large and too dimensions, this paper presented Harris corner invariant moments algorithm. This algorithm only calculates corner coordinates, so can reduce the corner matching dimensions. Combined with the SVM (Support Vector Machine) classification method, we conducted a classification for a large number of images, and the result shows that using this algorithm to extract invariant moments and classifying can achieve better classification accuracy.

scholarly journals Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

2019 ◽  
Vol 11 (4) ◽  
pp. 405
Author(s):  
Xuan Feng ◽  
Haoqiu Zhou ◽  
Cai Liu ◽  
Yan Zhang ◽  
Wenjing Liang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Decomposition Methods ◽  
Support Vector ◽  
Classification Methods ◽  
Target Classification ◽  
Data Set ◽  
Particle Center ◽  
Ground Penetrating ◽  
New Criterion

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

scholarly journals Wavelet Statistical Feature based Malware Class Recognition and Classification using Supervised Learning Classifier

2017 ◽  
Vol 10 (2) ◽  
pp. 400-406 ◽  
Author(s):  
Aziz Makandar ◽  
Anita Patrot
Keyword(s):  
Feature Extraction ◽  
Automated Analysis ◽  
Input Image ◽  
Access Time ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Learning Classifier ◽  
Feature Based ◽  
Three Stages ◽  
Analysis System

Malware is a malicious instructions which may harm to the unauthorized private access through internet. The types of malware are incresing day to day life, it is a challenging task for the antivius vendors to predict and caught on access time. This paper aims to design an automated analysis system for malware classes based on the features extracted by Discrete Wavelet Transformation (DWT) and then by applying four level decomposition of malware. The proposed system works in three stages, pre-processing, feature extraction and classification. In preprocessing, input image is normalized in to 256x256 by applying wavelet we are denoising the image which helps to enhance the image. In feature extraction, DWT is used to decompose image into four level. For classification the support vector machine (SVM) classifiers are used to discriminate the malware classes with statistical features extracted from level 4 decomposition of DWT such as Daubechies (db4), Coiflet (coif5) and Bi-orthogonal (bior 2.8). Among these wavelet features the db4 features effectively classify the malware class type with high accuracy 91.05% and 92.53% respectively on both dataset. The analysis of proposed method conducted on two dataset and the results are promising.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

scholarly journals Best Feature Extraction and Classification Algorithms for EEG Signals in Neuromarketing

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Boniadi Naieni
Keyword(s):  
Feature Extraction ◽  
Brain Activity ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
High Efficient ◽  
The Brain

Purpose: There are many methods for advertisements of products and neuromarketing is new area in this field. In neuromarketing, we use neuroscience information for revealing Consumer behavior by extracting brain activity. Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG), and Electroencephalography (EEG) are high efficient tools for investigating the brain activity in neuromarketing. EEG signal is a high temporal resolution and a cheap method for examining the brain activity. Materials and Methods: 32 subjects (16 males and 16 females) aging between 20-35 years old participated in this study. We proposed neuromarketing method exploit EEG system for predicting consumer preferences while they view E-commerce products. We apply some important preprocessing steps for noise and artifacts elimination of the EEG signal. In next step feature extraction methods are applied on the EEG data such as Discrete Wavelet Transform (DWT) and statistical features. The goal of this study is classification of analyzed EEG signal to likes and dislikes using supervised algorithms. We use Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) for data classification. The mentioned methods were used for whole and lobe brain data. Results: The results show high efficacy for SVM algorithms than other methods. Accuracy, sensitivity, specificity and precision parameters were used for evaluation of the model performance. The results show high performance of SVM algorithms for classification of the data with accuracy more than 87% and 84% for whole and parietal lobe data. Conclusion: We designed a tool with EEG signals for extraction brain activity of consumers using neuromarketing methods. We investigated the effects of advertising on brain activity of consumers by EEG signals measures.

HALF-WAVE SEGMENT FEATURE EXTRACTION OF EEG SIGNALS OF PATIENTS WITH DEPRESSION AND PERFORMANCE EVALUATION OF NEURAL NETWORK CLASSIFIERS

2017 ◽  
Vol 17 (01) ◽  
pp. 1750006 ◽  
Author(s):  
SUBHA D. PUTHANKATTIL ◽  
PAUL K. JOSEPH
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Time Domain ◽  
Classification Accuracy ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Wavelet Entropy ◽  
Wavelet Energy ◽  
Significant Difference

A detailed understanding of key signal characteristics has enabled the use of artificial neural networks (ANN) for feature detection and classification of EEG signals in clinical research. The present study is performed to classify EEG signals of normal and depression patients with wavelet parameters as key input features. The characteristics of depression cannot be made out by visual inspection of EEG records unlike epilepsy which is well characterized by sudden recurrent and transient waveforms. In this study, a comparison is made between the performance of feedforward neural network (FFNN) and probabilistic neural network (PNN) while classifying the EEG signals of normal and depression patients. Classification capabilities of both the methods are validated with the EEG recordings from 30 normal controls and 30 depression patients. One-way ANOVA provided a statistical significant difference between the two classes of EEG signals recorded. Preprocessing for feature extraction is done using discrete wavelet transform (DWT). The time domain and relative wavelet energy (RWE) features calculated from the sub-bands are given as a set of input to the neural network. Another set of feature used independently for training the network is the wavelet entropy (WE). The FFNN achieved a classification accuracy of 100% and PNN gave an accuracy of 58.75% with time domain and wavelet energy as the input features. With wavelet entropy as the input feature, FFNN further showed 98.75% classification accuracy while PNN gave an accuracy of only 46.5%. The results indicate that FFNN with the given input features is more suitable for the classification of EEG signals with mood changing depressive disorders.

Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform

2008 ◽  
Vol 12 (4) ◽  
pp. 189-199 ◽  
Author(s):  
Frank-Michael Schleif ◽  
Mathias Lindemann ◽  
Mario Diaz ◽  
Peter Maaß ◽  
Jens Decker ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Proteomic Profile

scholarly journals WBC-CNN: Efficient CNN-Based Models to Classify White Blood Cells Subtypes

2021 ◽  
Vol 17 (13) ◽  
pp. 135-150
Author(s):  
Najla Alofi ◽  
Wafa Alonezi ◽  
Wedad Alawad
Keyword(s):  
Feature Extraction ◽  
Blood Cells ◽  
Classification Accuracy ◽  
White Blood Cells ◽  
Hybrid Models ◽  
Learning Technologies ◽  
Support Vector ◽  
Svm Model ◽  
Vector Machines

Blood is essential to life. The number of blood cells plays a significant role in observing an individual’s health status. Having a lower or higher number of blood cells than normal may be a sign of various diseases. Thus it is important to precisely classify blood cells and count them to diagnose different health conditions. In this paper, we focused on classifying white blood cells subtypes (WBC) which are the basic parts of the immune system. Classification of WBC subtypes is very useful for diagnosing diseases, infections, and disorders. Deep learning technologies have the potential to enhance the process and results of WBC classification. This study presented two fine-tuned CNN models and four hybrid CNN-based models to classify WBC. The VGG-16 and MobileNet are the CNN architectures used for both feature extraction and classification in fine-tuned models. The same CNN architectures are used for feature extraction in hybrid models; however, the Support Vector Machines (SVM) and the Quadratic Discriminant Analysis (QDA) are the classifiers used for classification. Among all models, the fine-tuned VGG-16 performs best, its classification accuracy is 99.81%. Our hybrid models are efficient in detecting WBC as well. 98.44% is the classification accuracy of the VGG-16+SVM model, and 98.19% is the accuracy of the MobileNet+SVM.

Electromyography (EMG) based Classification of Finger Movements using SVM

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Nurazrin Mohd Esa ◽  
Azlan Mohd Zain ◽  
Mahadi Bahari
Keyword(s):  
Feature Extraction ◽  
Control System ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Myoelectric Control ◽  
Support Vector ◽  
Zero Crossing ◽  
Single Root ◽  
Artificial Hand

Myoelectric control prostheses hand are currently popular developing clinical option that offers amputee person to control their artificial hand by analyzing the contacting muscle residual. Myoelectric control system contains three main phase which are data segmentation, feature extraction and classification. The main factor that affect the performance of myoelectric control system is the choice of feature extraction methods. There are two types of feature extraction technique used to extract the signal which are the Hudgins feature consist of Zero Crossing, Waveform Length (WL), Sign Scope Change (SSC) and Mean Absolute Value (MAV), the single Root Mean Square (RMS). Then, the combination of both is proposed in this study. An analysis of these different techniques result were examine to achieve a favorable classification accuracy (CA). Our outcomes demonstrate that the combination of RMS and Hudgins feature set demonstrate the best average classification accuracy for all ten fingers developments. The classification process implemented in this studies is using Support Vector Machine (SVM) technique.

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