scholarly journals Image Recognition and Simulation Based on Distributed Artificial Intelligence

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tao Fan
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Image Recognition ◽  
Detection Efficiency ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Distributed Artificial Intelligence ◽  
Support Vector ◽  
Processing Unit ◽  
Detection Speed

This paper studies the traditional target classification and recognition algorithm based on Histogram of Oriented Gradients (HOG) feature extraction and Support Vector Machine (SVM) classification and applies this algorithm to distributed artificial intelligence image recognition. Due to the huge number of images, the general detection speed cannot meet the requirements. We have improved the HOG feature extraction algorithm. Using principal component analysis (PCA) to perform dimensionality reduction operations on HOG features and doing distributed artificial intelligence image recognition experiments, the results show that the image detection efficiency is slightly improved, and the detection speed is also improved. This article analyzes the reason for these changes because PCA mainly uses the useful feature information in HOG features. The parallelization processing of HOG features on graphics processing unit (GPU) is studied. GPU is used for high parallel and high-density calculations, and the calculation of HOG features is very complicated. Using GPU for parallelization of HOG features can make the calculation speed of HOG features improved. We use image experiments for the parallelized HOG feature algorithm. Experimental simulations show that the speed of distributed artificial intelligence image recognition is greatly improved. By analyzing the existing digital image recognition methods, an improved BP neural network algorithm is proposed. Under the premise of ensuring accuracy, the recognition speed of digital images is accelerated, the time required for recognition is reduced, real-time performance is guaranteed, and the effectiveness of the algorithm is verified.

Artificial intelligence-based classification with classical Turkish music makams: Possibilities to Turkish music education

2021 ◽  
Vol 9 (2) ◽  
pp. 570-580
Author(s):  
Mert Kayış ◽  
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Music Education ◽  
Success Rate ◽  
Classical Music ◽  
Graphics Processing Unit ◽  
Support Vector ◽  
Processing Unit ◽  
Percent Success ◽  
Graphics Processing

Makams of Classical Turkish Music have been tried to be classified through various studies for the past years. Significant differences of opinion have emerged in the classification process of the makams in Music Education and Literacy from past to present. This situation creates problems in learning the makams related to music education and recognizing the makams heard. Additionally, there are uncertainties in the classification of the makam genre of the song, as individual mistakes were made while notating the musical notes. Apart from that, this situation constitutes a problem not only for the ones studying Turkish Classical Music but also for the ones interested in this certain type of Music. Therefore, the objective of the research is to contribute to the makam classification in Classical Turkish Music Education by developing an MIR system that determines the makam of the songs. Theoretically, we can extract the properties of sound signals with Time Wavelet Scattering Feature Extraction, classify them with SVM and distinguish between types of makams. In this study, upon eight different Makams, a Musical Information Retrieval system has been created via the Artificial Intelligence (AI) method of Support Vector Machines (SVM) and Time Wavelet Scattering Feature Extraction and through using a Graphics Processing Unit (GPU) accelerator for the sake of feature extraction. We performed the classification process by modeling it on the MATLAB program. The study's success rate was identified as 98.21% and it acquired a higher success rate compared to the other studies in the literature. After completing the classification procedure, the Makams were identified by sending samples belonging to different sound files from the system consisting of a database belonging to eight different Makams. In our study, the classification and detection processes were realized with nearly a hundred percent success. The difficulties encountered in classifying the makams in Classical Turkish Music mentioned above, with the application of artificial intelligence, the classification difficulty of individuals who have received this type of training or are interested in this subject has been overcome.

scholarly journals An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuanyuan Xu ◽  
Genke Yang ◽  
Jiliang Luo ◽  
Jianan He
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Electronic Component ◽  
Support Vector ◽  
Learning Networks ◽  
Component Recognition ◽  
And Performance

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6   level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

2016 ◽  
Vol 231 (1) ◽  
pp. 98-108 ◽  
Author(s):  
Ke Li ◽  
Yalei Wu ◽  
Shimin Song ◽  
Yi sun ◽  
Jun Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Electrical Characteristics ◽  
Complex Signals ◽  
Fcm Clustering

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

scholarly journals Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 380 ◽  
Author(s):  
Kai Ye
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Recognition ◽  
False Positive Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

Feature Extraction of Surface Electromyography Based on Improved Small-World Leaky Echo State Network

2020 ◽  
Vol 32 (4) ◽  
pp. 741-758
Author(s):  
Xugang Xi ◽  
Wenjun Jiang ◽  
Seyed M. Miran ◽  
Xian Hua ◽  
Yun-Bo Zhao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Surface Electromyography ◽  
Contractile Activity ◽  
Random Network ◽  
Principal Component ◽  
Small World ◽  
Support Vector ◽  
Echo State Network ◽  
Better Than ◽  
Semg Signals

Surface electromyography (sEMG) is an electrophysiological reflection of skeletal muscle contractile activity that can directly reflect neuromuscular activity. It has been a matter of research to investigate feature extraction methods of sEMG signals. In this letter, we propose a feature extraction method of sEMG signals based on the improved small-world leaky echo state network (ISWLESN). The reservoir of leaky echo state network (LESN) is connected by a random network. First, we improved the reservoir of the echo state network (ESN) by these networks and used edge-added probability to improve these networks. That idea enhances the adaptability of the reservoir, the generalization ability, and the stability of ESN. Then we obtained the output weight of the network through training and used it as features. We recorded the sEMG signals during different activities: falling, walking, sitting, squatting, going upstairs, and going downstairs. Afterward, we extracted corresponding features by ISWLESN and used principal component analysis for dimension reduction. At the end, scatter plot, the class separability index, and the Davies-Bouldin index were used to assess the performance of features. The results showed that the ISWLESN clustering performance was better than those of LESN and ESN. By support vector machine, it was also revealed that the performance of ISWLESN for classifying the activities was better than those of ESN and LESN.

scholarly journals Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Tsun-Kuo Lin
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
High Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Image Features ◽  
Image Feature ◽  
Support Vector ◽  
Feature Identification ◽  
Adaptive Pca

This paper developed a principal component analysis (PCA)-integrated algorithm for feature identification in manufacturing; this algorithm is based on an adaptive PCA-based scheme for identifying image features in vision-based inspection. PCA is a commonly used statistical method for pattern recognition tasks, but an effective PCA-based approach for identifying suitable image features in manufacturing has yet to be developed. Unsuitable image features tend to yield poor results when used in conventional visual inspections. Furthermore, research has revealed that the use of unsuitable or redundant features might influence the performance of object detection. To address these problems, the adaptive PCA-based algorithm developed in this study entails the identification of suitable image features using a support vector machine (SVM) model for inspecting of various object images; this approach can be used for solving the inherent problem of detection that occurs when the extraction contains challenging image features in manufacturing processes. The results of experiments indicated that the proposed algorithm can successfully be used to adaptively select appropriate image features. The algorithm combines image feature extraction and PCA/SVM classification to detect patterns in manufacturing. The algorithm was determined to achieve high-performance detection and to outperform the existing methods.

An Automatic Target Recognition Algorithm Based on Support Vector Machine

2014 ◽  
Vol 496-500 ◽  
pp. 1873-1876
Author(s):  
He Zhang ◽  
Jie Li ◽  
Bei Bei Xu
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Support Vector Machine Model ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Machine Model ◽  
Fast Recognition ◽  
High False Alarm Rate

To improve the performance of automatic target recognition technology and solve the problems of traditional methods, such as high false alarm rate and poor adaptability to environment changes, a new algorithm based on support vector machine is proposed. We have realized the feature extraction of the target and the parameter optimization of the support vector machine to get the support vector machine model applied to the target recognition of unknown images. Experiment results show that the algorithm has a good recognition effect, a fast recognition speed and certain anti-interference abilities based on sufficient samples training.

scholarly journals Spectral Feature Extraction Based on the DCPCA Method

2013 ◽  
Vol 30 ◽  
Author(s):  
BU YUDE ◽  
PAN JINGCHANG ◽  
JIANG BIN ◽  
CHEN FUQIANG ◽  
WEI PENG
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Spectral Feature ◽  
Cataclysmic Variables ◽  
Principal Component ◽  
Support Vector ◽  
Sparse Principal Component Analysis ◽  
Astronomical Data ◽  
Feature Lines ◽  
Feature Extraction Technique

AbstractIn this paper, a new sparse principal component analysis (SPCA) method, called DCPCA (sparse PCA using a difference convex program), is introduced as a spectral feature extraction technique in astronomical data processing. Using this method, we successfully derive the feature lines from the spectra of cataclysmic variables. We then apply this algorithm to get the first 11 sparse principal components and use the support vector machine (SVM) to classify. The results show that the proposed method is comparable with traditional methods such as PCA+SVM.

scholarly journals Biometric authenticator algorithm based on multiresolution analysis

2020 ◽  
Vol 20 (3) ◽  
pp. 1332
Author(s):  
Soumia Kerrache ◽  
Beladgham Mohammed ◽  
Hamza Aymen ◽  
Kadri Ibrahim
Keyword(s):  
Feature Extraction ◽  
Multiresolution Analysis ◽  
Nearest Neighbor ◽  
Curvelet Transform ◽  
Principal Component ◽  
Image Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Fusion Approach

Features extraction is an essential process in identifying person biometrics because the effectiveness of the system depends on it. Multiresolution Analysis success can be used in the system of a person’s identification and pattern recognition. In this paper, we present a feature extraction method for two-dimensional face and iris authentication.  Our approach is a combination of principal component analysis (PCA) and curvelet transform as an improved fusion approach for feature extraction. The proposed fusion approach involves image denoising using 2D-Curvelet transform to achieve compact representations of curves singularities. This is followed by the application of PCA as a fusion rule to improve upon the spatial resolution. The limitations of the only PCA algorithm are a poor recognition speed and complex mathematical calculating load, to reduce these limitations, we are applying the curvelet transform. <br /> To assess the performance of the presented method, we have employed three classification techniques: Neural networks (NN), K-Nearest Neighbor (KNN) and Support Vector machines (SVM).<br />The results reveal that the extraction of image features is more efficient using Curvelet/PCA.

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.

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