scholarly journals Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhao Zhang ◽  
Wang Li ◽  
Yuyang Zhang
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Classification Problem ◽  
Human Motion ◽  
Small Samples ◽  
Support Vector ◽  
Convolutional Network ◽  
Multilabel Classification ◽  
Practical Applications ◽  
Automatic Construction

In this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. In this paper, support vector machines, which have better performance in the case of small samples, are selected as classifiers, and multiclass classifiers are constructed in a one-to-one manner to achieve the classification and recognition of human sports postures. The classifier for a single decomposed action is constructed to transform the automatic description problem of free gymnastic movements into a multilabel classification problem. With the increase in the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper for spatial-temporal feature extraction of the video. The extracted features are binary classified several times to achieve the goal of multilabel classification. To form a comparison experiment, the results of the classification are randomly combined into a sentence and compared with the results of the automatic description method to verify the effectiveness of the method. The multiclass classifier constructed in this paper is used for human motion pose classification and recognition tests, and the experimental results show that the human motion pose recognition algorithm based on multifeature fusion can effectively improve the recognition accuracy and perform well in practical applications.

An investigation into machine pattern recognition based on time-frequency image feature extraction using a support vector machine

2010 ◽  
Vol 224 (4) ◽  
pp. 981-994 ◽  
Author(s):  
H Li ◽  
P Zhou ◽  
Z Zhang
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Working Conditions ◽  
Signal Analysis ◽  
Feature Vector ◽  
Rolling Bearing ◽  
Image Feature ◽  
Small Samples ◽  
Support Vector ◽  
Time Frequency

In this article, a new method of pattern recognition for machine working conditions is presented that is based on time-frequency image (TFI) feature extraction and support vector machines (SVMs). In this study, the Hilbert time-frequency spectrum (HTFS) is used to construct TFIs because of its good performance in non-stationary and non-linear signal analysis. Cyclostationarity signal analysis is a pre-processing method for improving the performance of the HTFS in the construction of TFIs. Feature extraction for TFIs is investigated in detail to construct a feature vector for pattern recognition. Gravity centre and information entropy of TFIs are used to construct the feature vector for pattern recognition. SVMs are used for different working conditions classification by the constructed feature vector because of its powerful performance even for small samples. In the end, rolling bearing pattern recognition is used as an example to testify the effectiveness of this method. According to the result analysis, it can be concluded that this method will contribute to the development of preventative maintenance.

scholarly journals Optimized Variational Mode Decomposition and Permutation Entropy with Their Application in Feature Extraction of Ship-Radiated Noise

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 503
Author(s):  
Dongri Xie ◽  
Shaohua Hong ◽  
Chaojun Yao
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Marine Animals ◽  
Practical Applications ◽  
Radiated Noise ◽  
Mode Decomposition

The complex and changeable marine environment surrounded by a variety of noise, including sounds of marine animals, industrial noise, traffic noise and the noise formed by molecular movement, not only interferes with the normal life of residents near the port, but also exerts a significant influence on feature extraction of ship-radiated noise (S-RN). In this paper, a novel feature extraction technique for S-RN signals based on optimized variational mode decomposition (OVMD), permutation entropy (PE), and normalized Spearman correlation coefficient (NSCC) is proposed. Firstly, with the mode number determined by reverse weighted permutation entropy (RWPE), OVMD decomposes the target signal into a set of intrinsic mode functions (IMFs). The PE of all the IMFs and SCC between each IMF with the raw signal are then calculated, respectively. Subsequently, feature parameters are extracted through the sum of PE weighted by NSCC for the IMFs. Lastly, the obtained feature vectors are input into the support vector machine multi-class classifier (SVM) to discriminate various types of ships. Experimental results indicate that five kinds of S-RN samples can be accurately identified with a recognition rate of 94% by the proposed scheme, which is higher than other previously published methods. Hence, the proposed method is more advantageous in practical applications.

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 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.

A Performance Comparison of EMG Classification Methods for Hand and Finger Motion

2014 ◽  
Author(s):  
Sungtae Shin ◽  
Reza Langari ◽  
Reza Tafreshi
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Dimension Reduction ◽  
Time Domain ◽  
Classification Accuracy ◽  
Human Motion ◽  
Support Vector ◽  
Classification Methods ◽  
Data Set

For recognizing human motion intent, electromyogram (EMG) based pattern recognition approaches have been studied for many years. A number of methods for classifying EMG patterns have been introduced in the literature. On the purpose of selecting the best performing method for the practical application, this paper compares EMG pattern recognition methods in terms of motion type, feature extraction, dimension reduction, and classification algorithm. Also, for more usability of this research, hand and finger EMG motion data set which had been published online was used. Time-domain, empirical mode decomposition, discrete wavelet transform, and wavelet packet transform were adopted as the feature extraction. Three cases, such as no dimension reduction, principal component analysis (PCA), and linear discriminant analysis (LDA), were compared. Six classification algorithms were also compared: naïve Bayes, k-nearest neighbor, quadratic discriminant analysis, support vector machine, multi-layer perceptron, and extreme machine learning. The performance of each case was estimated by three perspectives: classification accuracy, train time, and test (prediction) time. From the experimental results, the time-domain feature set and LDA were required for the highest classification accuracy. Fast train time and test time are dependent on the classification methods.

scholarly journals Method of Analyzing and Managing Volleyball Action by Using Action Sensor of Mobile Device

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xu Sun ◽  
Kai Zhao ◽  
Wei Jiang ◽  
Xinlong Jin
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Human Motion ◽  
Classification Algorithm ◽  
Support Vector ◽  
Motion Sensors ◽  
Support Vector Machine Algorithm ◽  
Motion Information ◽  
Motion Recognition

With the development of electronic technology and sensor technology, more and more intelligent electronic devices integrate micro inertial sensors, which makes the research of human action recognition based on action sensing data have great application value. Data-based action recognition is a new research direction in the field of pattern recognition, which is essentially a process of action data acquisition, feature extraction, feature extraction, and recognition, the process of classification and recognition. Inertial motion information includes acceleration and angular velocity information, which is ubiquitous in daily life. Compared with motion recognition based on visual information, it can more directly reflect the meaning of action. This study mainly discusses the method of analyzing and managing volleyball action by using the action sensor of mobile device. Based on the motion recognition algorithm of support vector machine, the motion recognition process of support vector machine is constructed. When the data terminal and gateway of volleyball players are not in the same LAN, the classification algorithm classifies the samples to be tested through the characteristic data, which directly affects the recognition results. In this paper, the support vector machine algorithm is selected as the data classification algorithm, and the calculation of the classification process is reduced by designing an appropriate kernel function. For multiclass problems, the hierarchical structure of directed acyclic graph is optimized to improve the recognition rate. We need to bind motion sensors to human joints. In order to realize real-time recognition of human motion, mobile devices need to add windows to the motion capture data, that is, divide the data into a small sequence of specified length, and provide more application scenarios for the device. This method of embedding motion sensors into devices to read motion information is widely used, which provides a convenient data acquisition method for human motion pattern recognition based on motion information. The multiclassification support vector machine algorithm is used to train the classification algorithm model with action data. When the signal strength of the sensor is 90 t and the speed is 2.0 m/s and 0.5 m/s, the detection accuracy of the adaptive threshold is 93% and 95%, respectively. The results show that the SVM method based on hybrid kernel function can greatly improve the recognition accuracy of volleyball stroke, and the recognition time is short.

scholarly journals Speech/music classification using PLP and SVM

2019 ◽  
Vol 8 (02) ◽  
pp. 24469-24472
Author(s):  
Thiruven Gatanadhan R
Keyword(s):  
Feature Extraction ◽  
Linear Prediction ◽  
Classification Problem ◽  
Vector Model ◽  
Support Vector ◽  
Audio Classification ◽  
Music Classification ◽  
Audio Retrieval ◽  
Audio Indexing ◽  
Perceptual Linear Prediction

Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. This paper deals with the Speech/Music classification problem, starting from a set of features extracted directly from audio data. Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. The accuracy of the classification relies on the strength of the features and classification scheme. In this work Perceptual Linear Prediction (PLP) features are extracted from the input signal. After feature extraction, classification is carried out, using Support Vector Model (SVM) model. The proposed feature extraction and classification models results in better accuracy in speech/music classification.

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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