scholarly journals An Intelligent Image Feature Recognition Algorithm With Hierarchical Attribute Constraints Based on Weak Supervision and Label Correlation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 105744-105753
Author(s):  
Songshang Zou ◽  
Hao Chen ◽  
Haoyu Zhou ◽  
Jianguo Chen
Keyword(s):  
Feature Recognition ◽  
Recognition Algorithm ◽  
Image Feature ◽  
Weak Supervision ◽  
Label Correlation

scholarly journals Research on School Classroom Teaching Model Based on Clustering Algorithm and Fuzzy Control

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fujun Zhang ◽  
Aichuan Li ◽  
Jianfei Shi ◽  
Dongxin Wang
Keyword(s):  
Fuzzy Control ◽  
Clustering Algorithm ◽  
Feature Recognition ◽  
Computing Time ◽  
Recognition Algorithm ◽  
Image Feature ◽  
Operating Efficiency ◽  
Computational Techniques ◽  
Research Model ◽  
Adaboost Algorithm

The method of computational intelligence to monitor and evaluate the concentration of students in the teaching process can promptly and effectively adjust the learning plan and improve the learning effect. In this article, clustering algorithm and fuzzy control methods are used to construct a research model of students’ attention in class. In addition, this article uses the existing MATLAB-based image feature recognition algorithm to detect and obtain facial features and analyze the main features of facial expressions through computational techniques to realize the judgment of attention. In addition, this article optimizes the traditional AdaBoost algorithm to save computing time and improve operating efficiency and system performance stability. Finally, this article constructs the functional modules of the research model according to actual needs and designs experiments to verify the performance of the model. Experimental research results show that the model constructed in this article has a certain effect.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

scholarly journals Analysis Technology of Tennis Sports Match Based on Data Mining and Image Feature Retrieval

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Hong Huang ◽  
Risheng Deng
Keyword(s):  
Data Mining ◽  
Feature Recognition ◽  
Image Data ◽  
Technical Analysis ◽  
Machine Learning Algorithms ◽  
Technical Training ◽  
Image Feature ◽  
Mining Technology ◽  
Movement Characteristics ◽  
Real Time Tracking

Tennis game technical analysis is affected by factors such as complex background and on-site noise, which will lead to certain deviations in the results, and it is difficult to obtain scientific and effective tennis technical training strategies through a few game videos. In order to improve the performance of tennis game technical analysis, based on machine learning algorithms, this paper combines image analysis to identify athletes’ movement characteristics and image feature recognition processing with image recognition technology, realizes real-time tracking of athletes’ dynamic characteristics, and records technical characteristics. Moreover, this paper combines data mining technology to obtain effective data from massive video and image data, uses mathematical statistics and data mining technology for data processing, and scientifically analyzes tennis game technology with the support of ergonomics. In addition, this paper designs a controlled experiment to verify the technical analysis effect of the tennis match and the performance of the model itself. The research results show that the model constructed in this paper has certain practical effects and can be applied to actual competitions.

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.

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