Recognition of students’ behavior states in classroom based on improved MobileNetV2 algorithm

2021 ◽  
pp. 002072092199659
Author(s):  
Dan Cao ◽  
Jianfei Liu ◽  
Luguo Hao ◽  
Wenbin Zeng ◽  
Chen Wang ◽  
...  
Keyword(s):  
Network Model ◽  
Mobile Networks ◽  
Information Exchange ◽  
Classroom Behavior ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Computational Cost ◽  
Teaching Quality ◽  
Positive Role ◽  
Data Set

Analyzing and learning students' behavior states in classroom plays a positive role in understanding and improving the teaching effectiveness. Meanwhile, the application of lightweight network to pattern recognition has become a trend with the development of mobile networks. In order to improve the recognition accuracy of the lightweight network model MobileNetV2 and reduce the computational cost and delay caused by extracting rich features, an improved lightweight network model based on MobileNetV2 is proposed, in which an improved reverse residual module (C-Inverted residual block) is applied to replace the traditional module. In the improved reverse residual module, channel split operation is added to reduce MAC, and channel shuffle operations are used to promote information exchange and channel fusion. Experiments were carried out on Pascal VOC 2007 detection data set to test the general performance of the proposed improved model. Under the operation limits of 140 MFLOPS, 40 MFLOPS and 20 MFLOPS, mean average precision (mAP) of the improved MobileNetV2 algorithm increased by 1.2%, 2.2% and 4.3% compared with MobileNetV2. While the recognition accuracy of the proposed network model on self-made dataset of student classroom behavior states is 4.6% and 3.7% higher than that of MobileNetV1 and MobileNetV2 respectively, and the average recognition rate of students' classroom behavior states can be up to 92.7%. The results of this research combined with mobile networks would be expected to be used to evaluate teaching and learning effects and promote teaching quality improvement.

Analysis of the Teaching Quality of College Ideological and Political Education Based on Deep Learning

2021 ◽  
pp. 2143002
Author(s):  
Xiao Li ◽  
Ying Dong ◽  
Yanxia Jiang ◽  
Gabriel A. Ogunmola
Keyword(s):  
Deep Learning ◽  
Information Exchange ◽  
Teaching Quality ◽  
Knowledge Network ◽  
Ideological And Political Education ◽  
Political Education ◽  
Performance Ratio ◽  
Mechanism Analysis ◽  
Data Set ◽  
Interactive Experience

Education refers to ideologies, traditions, culture, and values that guide education to economics, politics, morals, religions, information, reality, comparative and historical aesthetic, and artistic school knowledge. The challenging characteristics in political education include lack in knowledge sharing, user’s interactive experience, and incentive mechanism has become an essential factor. In this paper, the Deep Learning-Based Innovative Ideological Behavior Education Model (DL-IIBEM ) has been proposed to strengthen the mechanism to promote information exchange, enhance the user’s interactive experience, and make the platform perform efficiently. Knowledge Network Mechanism Analysis is integrated with DL-IIBEM to strengthen user feedback probability, the average probability of completing social media tasks on a popular network, and the predicted utility degree for individual users. The entire platform is dramatically improved. The simulation analysis is performed based on the performance ratio based on data set 1 (98.2%) and 2 (95.3%), skill development ratio (95.3%), accuracy ratio, the teaching methods in ideological and political education, and Students Achievements ratio (98.2%) prove the proposed framework’s reliability.

An improved technique for face recognition applications

2018 ◽  
Vol 119 (9/10) ◽  
pp. 529-544 ◽  
Author(s):  
Ihab Zaqout ◽  
Mones Al-Hanjori
Keyword(s):  
Face Recognition ◽  
Computer Games ◽  
Recognition Accuracy ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Data Set ◽  
Content Type ◽  
Linear Discriminant ◽  
The Face ◽  
Feature Based

Purpose The face recognition problem has a long history and a significant practical perspective and one of the practical applications of the theory of pattern recognition, to automatically localize the face in the image and, if necessary, identify the person in the face. Interests in the procedures underlying the process of localization and individual’s recognition are quite significant in connection with the variety of their practical application in such areas as security systems, verification, forensic expertise, teleconferences, computer games, etc. This paper aims to recognize facial images efficiently. An averaged-feature based technique is proposed to reduce the dimensions of the multi-expression facial features. The classifier model is generated using a supervised learning algorithm called a back-propagation neural network (BPNN), implemented on a MatLab R2017. The recognition rate and accuracy of the proposed methodology is comparable with other methods such as the principle component analysis and linear discriminant analysis with the same data set. In total, 150 faces subjects are selected from the Olivetti Research Laboratory (ORL) data set, resulting 95.6 and 85 per cent recognition rate and accuracy, respectively, and 165 faces subjects from the Yale data set, resulting 95.5 and 84.4 per cent recognition rate and accuracy, respectively. Design/methodology/approach Averaged-feature based approach (dimension reduction) and BPNN (generate supervised classifier). Findings The recognition rate is 95.6 per cent and recognition accuracy is 85 per cent for the ORL data set, whereas the recognition rate is 95.5 per cent and recognition accuracy is 84.4 per cent for the Yale data set. Originality/value Averaged-feature based method.

scholarly journals Imperfect Wheat Grain Recognition Combined with an Attention Mechanism and Residual Network

2021 ◽  
Vol 11 (11) ◽  
pp. 5139
Author(s):  
Weiwei Zhang ◽  
Huimin Ma ◽  
Xiaohong Li ◽  
Xiaoli Liu ◽  
Jun Jiao ◽  
...  
Keyword(s):  
Machine Vision ◽  
Network Model ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Attention Mechanism ◽  
Residual Network ◽  
Wheat Grains ◽  
Key Factor ◽  
Different Depths ◽  
Deep Learning Network

Intelligent detection of imperfect wheat grains based on machine vision is of great significance to correctly and rapidly evaluate wheat quality. There is little difference between the partial characteristics of imperfect and perfect wheat grains, which is a key factor limiting the classification and recognition accuracy of imperfect wheat based on a deep learning network model. In this paper, we propose a method for imperfect wheat grains recognition combined with an attention mechanism and residual network (ResNet), and verify its recognition accuracy by adding an attention mechanism module into different depths of residual network. Five residual networks with different depths (18, 34, 50, 101, and 152) were selected for the experiment, it was found that the recognition accuracy of each network model was improved with the attention mechanism, and the average recognition rate of ResNet-50 with the addition of the attention mechanism reached 96.5%. For ResNet-50 with the attention mechanism, the optimal learning rate was further screened as 0.0003. The average recognition accuracy reached 97.5%, among which the recognition rates of scab wheat grains, insect-damaged wheat grains, sprouted wheat grains, mildew wheat grains, broken wheat grains, and perfect wheat grains reached 97%, 99%, 99%, 95%, 96%, and 99% respectively. This work can provide guidance for the detection and recognition of imperfect wheat grains using machine vision.

scholarly journals A Violation Information Recognition Method of Live-Broadcasting Platform Based on Machine Learning Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoying Shen ◽  
Chao Yuan
Keyword(s):  
Machine Learning ◽  
Skin Color ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Learning Technology ◽  
Recognition Method ◽  
Data Set ◽  
Color Recognition ◽  
Live Broadcast ◽  
Information Recognition

With the development of the live broadcast industry, security issues in the live broadcast process have become increasingly apparent. At present, the supervision of various live broadcast platforms is basically in a state of human supervision. Manpower supervision is mainly through user reporting and platform supervision measures. However, there are a large number of live broadcast rooms at the same time, and only relying on human supervision can no longer meet the monitoring needs of live broadcasts. Based on this situation, this study proposes a violation information recognition method of a live-broadcasting platform based on machine learning technology. By analyzing the similarities and differences between normal live broadcasts and violation live broadcasts, combined with the characteristics of violation image data, this study mainly detects human skin color and sensitive parts. A prominent feature of violation images is that they contain a large area of naked skin, and the ratio of the area of naked skin to the overall image area of the violation image will exceed the threshold. Skin color recognition plays a role in initial target positioning. The accuracy of skin color recognition is directly related to the recognition accuracy of the entire system, so skin color recognition is the most important part of violation information recognition. Although there are many effective skin color recognition technologies, the accuracy and stability of skin color recognition still need to be improved due to the influence of various external factors, such as light intensity, light source color, and physical equipment. When it is detected that the area of the skin color in the live screen exceeds the threshold, it is preliminarily determined to be a suspected violation video. In order to improve the recognition accuracy, it is necessary to detect sensitive parts of the suspected video. Naked female breasts are a very obvious feature in violation images. This study uses a chest feature extraction method to detect the chest in the image. When the recognition result is a violation image, it is determined that the live broadcast involves violation content. The machine learning algorithm is simple to implement, and the parameters are easy to adjust. The classifier training requires a short time and is suitable for live violation information recognition scenarios. The experimental results on the adopted data set show that the method used in this article can effectively detect videos with violation content. The recognition rate is as high as 85.98%, which is suitable for a real-life environment and has good practical significance.

scholarly journals An Unconstrained Face Recognition Method Based on Siamese Networks

2021 ◽  
Author(s):  
Song CunLi ◽  
Shouyong Ji
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Network Model ◽  
High Frequency ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Low Frequency ◽  
Image Features ◽  
Frequency Features ◽  
Frequency Feature

Abstract It is aimed at the low accuracy and low efficiency of face recognition under unlimited conditions.In this paper, a Siamese neural Network model SN-LF (Siamese Network based on LBP and Frequency Feature perception) is designed based on the Local Binary Pattern (LBP) and the Frequency sensing model.Based on Siamese neural networks, the network adopts circular LBP algorithm and frequency feature perception to realize face recognition under unrestricted conditions.The LBP algorithm can eliminate the influence of light on the image and provide directional input to the network model at the same time.Frequency feature sensing divides the image features into low frequency features and high frequency features. The low frequency features are compressed in the Siamese neural network to increase the recognition efficiency of the network. At the same time, information is exchanged with the high frequency features, so that the target noise data can be eliminated while the feature data is retained.In this way, the recognition rate of the network is maintained, and the computing speed of the network is improved.Simulation experiments are carried out on standard face dataset CASIA-Webface and Yale-B, and compared with other network models. The experimental results show that the proposed SN-LF network structure can improve the recognition accuracy of the algorithm, and achieve a good recognition accuracy.

scholarly journals Research on Real-Time Face Key Point Detection Algorithm Based on Attention Mechanism

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Jiangjin Gao ◽  
Tao Yang
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Network Model ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Point Detection ◽  
Detection Speed

The existing face detection methods were affected by the network model structure used. Most of the face recognition methods had low recognition rate of face key point features due to many parameters and large amount of calculation. In order to improve the recognition accuracy and detection speed of face key points, a real-time face key point detection algorithm based on attention mechanism was proposed in this paper. Due to the multiscale characteristics of face key point features, the deep convolution network model was adopted, the attention module was added to the VGG network structure, the feature enhancement module and feature fusion module were combined to improve the shallow feature representation ability of VGG, and the cascade attention mechanism was used to improve the deep feature representation ability. Experiments showed that the proposed algorithm not only can effectively realize face key point recognition but also has better recognition accuracy and detection speed than other similar methods. This method can provide some theoretical basis and technical support for face detection in complex environment.

An Enhancement of Feature Selection Algorithm for EDM: A Review

2018 ◽  
Vol 8 (5) ◽  
pp. 29
Author(s):  
Manpreet Kaur ◽  
Chamkaur Singh
Keyword(s):  
Feature Selection ◽  
Educational Data Mining ◽  
Problem Formulation ◽  
Research Area ◽  
Education Quality ◽  
Educational Institutions ◽  
Selection Algorithm ◽  
Positive Role ◽  
Data Set ◽  
Selection Algorithms

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

scholarly journals A Computational Protocol Combining DFT and Cheminformatics for Prediction of pH-Dependent Redox Potentials

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3978
Author(s):  
Rocco Peter Fornari ◽  
Piotr de Silva
Keyword(s):  
Organic Molecules ◽  
Ph Value ◽  
Computational Cost ◽  
Legendre Transform ◽  
Good Prediction ◽  
Redox Potentials ◽  
Free Energies ◽  
Hybrid Functional ◽  
Data Set ◽  
State Changes

Discovering new materials for energy storage requires reliable and efficient protocols for predicting key properties of unknown compounds. In the context of the search for new organic electrolytes for redox flow batteries, we present and validate a robust procedure to calculate the redox potentials of organic molecules at any pH value, using widely available quantum chemistry and cheminformatics methods. Using a consistent experimental data set for validation, we explore and compare a few different methods for calculating reaction free energies, the treatment of solvation, and the effect of pH on redox potentials. We find that the B3LYP hybrid functional with the COSMO solvation method, in conjunction with thermal contributions evaluated from BLYP gas-phase harmonic frequencies, yields a good prediction of pH = 0 redox potentials at a moderate computational cost. To predict how the potentials are affected by pH, we propose an improved version of the Alberty-Legendre transform that allows the construction of a more realistic Pourbaix diagram by taking into account how the protonation state changes with pH.

scholarly journals Application of portrait recognition system for emergency evacuation in mass emergencies

2021 ◽  
Vol 30 (1) ◽  
pp. 893-902
Author(s):  
Ke Xu
Keyword(s):  
Detection Rate ◽  
Characteristic Curve ◽  
Recognition Rate ◽  
Recognition System ◽  
Emergency Evacuation ◽  
Shopping Mall ◽  
Single Shot ◽  
Data Set ◽  
Linear Discriminant ◽  
Adaboost Algorithm

Abstract A portrait recognition system can play an important role in emergency evacuation in mass emergencies. This paper designed a portrait recognition system, analyzed the overall structure of the system and the method of image preprocessing, and used the Single Shot MultiBox Detector (SSD) algorithm for portrait detection. It also designed an improved algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA) for portrait recognition and tested the system by applying it in a shopping mall to collect and monitor the portrait and establish a data set. The results showed that the missing detection rate and false detection rate of the SSD algorithm were 0.78 and 2.89%, respectively, which were lower than those of the AdaBoost algorithm. Comparisons with PCA, LDA, and PCA + LDA algorithms demonstrated that the recognition rate of the improved PCA + LDA algorithm was the highest, which was 95.8%, the area under the receiver operating characteristic curve was the largest, and the recognition time was the shortest, which was 465 ms. The experimental results show that the improved PCA + LDA algorithm is reliable in portrait recognition and can be used for emergency evacuation in mass emergencies.

scholarly journals ResCaps: an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma

2021 ◽  
Author(s):  
Xiongzhi Ai ◽  
Jiawei Zhuang ◽  
Yonghua Wang ◽  
Pin Wan ◽  
Yu Fu
Keyword(s):  
Papillary Carcinoma ◽  
Network Model ◽  
Reliability And Validity ◽  
Thyroid Papillary Carcinoma ◽  
Data Set ◽  
Ultrasonic Image ◽  
First Choice ◽  
Thyroid Papillary ◽  
Ultrasonic Images

AbstractUltrasonic image examination is the first choice for the diagnosis of thyroid papillary carcinoma. However, there are some problems in the ultrasonic image of thyroid papillary carcinoma, such as poor definition, tissue overlap and low resolution, which make the ultrasonic image difficult to be diagnosed. Capsule network (CapsNet) can effectively address tissue overlap and other problems. This paper investigates a new network model based on capsule network, which is named as ResCaps network. ResCaps network uses residual modules and enhances the abstract expression of the model. The experimental results reveal that the characteristic classification accuracy of ResCaps3 network model for self-made data set of thyroid papillary carcinoma was $$81.06\%$$ 81.06 % . Furthermore, Fashion-MNIST data set is also tested to show the reliability and validity of ResCaps network model. Notably, the ResCaps network model not only improves the accuracy of CapsNet significantly, but also provides an effective method for the classification of lesion characteristics of thyroid papillary carcinoma ultrasonic images.

Sign in / Sign up

Export Citation Format

Share Document