scholarly journals Optimization of Hybrid Multimedia Art and Design Teaching Mode in the Era of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hua Tian
Keyword(s):  
Neural Network ◽  
Big Data ◽  
New Media ◽  
Cognitive Load ◽  
Convolutional Neural Network ◽  
Information Overload ◽  
Art Teachers ◽  
Digital Learning ◽  
Art Curriculum ◽  
New Media Art

In this paper, a 9-layer convolutional neural network with 4 convolutional layers, 4 pooling layers, and 1 fully connected layer is designed to recognize the emotions of digital learning images in the era of big data. The convolutional neural network is trained using digital learning images that have been labeled with emotions, and the final test shows that the network has good recognition results. This in turn causes the information overload problem to arise. And combined with the questionnaire results and interviews, it was found that there are problems of technology for technology’s sake, teaching for teaching’s sake, and in multimedia teaching, and these will add to the psychological and visual sensory burden of students and easily cause the information overload problem. The types of information overload problems in multimedia-assisted teaching are summarized as follows: unreasonable presentation of information, which causes audiovisual redundancy; too much teaching irrelevant information, which increases the external cognitive load; and an uncoordinated audiovisual environment, which increases the external cognitive load. Starting from the perspective of the integration of preservice to in-service art teachers’ new media art curriculum design and teaching ability development, three representative teacher education cases were studied using a combination of teaching practice and case tracking methods to summarize the successful experiences and effective ways of art teachers’ new media art curriculum development and teaching ability development, which will provide future art teacher training and in-service teachers’ professional development. Both are below 5%. The types of funny emotions are mainly distributed in animation teaching methods. Animation resources are generally well designed in color and layout and can convey good visual emotional characteristics. In other types of images, the emotional distribution level of funny is less than 10%. It is worthwhile to learn from this experience.

Analysis of Radar Technology Identification Model for Potential Geologic Hazard based on Convolutional Neural Network and Big Data

2021 ◽  
Author(s):  
Feng He ◽  
Hongjiang Liu ◽  
Chunxue Liu ◽  
Guangjing Bao
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Convolutional Neural Network ◽  
New Technologies ◽  
Hazard Identification ◽  
Identification Accuracy ◽  
Radar Image ◽  
Geologic Hazard ◽  
Continuous Increase ◽  
Identification Model

Abstract To ensure the proper adoption of new technologies in identifying the potential geologic hazard on tourist routes, convolutional neural network (CNN) technology is applied in the radar image geologic hazard information extraction. A scientific and practical geologic hazard radar identification model is built, which is based on CNN’s image identification and big data algorithm calculation, and it can effectively improve the geologic hazard identification accuracy. By designing experiments, the geologic hazard radar image data are verified, and the practicality of radar image intelligent Identification under CNN and big data technology is also verified. The results show that the images of different resolution sizes all play a significant role in identification of geologic hazard performed by CNN. However, there are differences in the performance of different CNN models. With the continuous increase of training samples, the identification accuracy of various network models is also improved. By means of radar image test, the identification capability of CNN model is the best, the highest precision is 93.61%, and the geologic hazard recall rate is 98.27%. Apriori algorithm is introduced into data processing, and the running speed and efficiency of identification models are improved, with favorable identification effect in variable data sets. This research can provide theoretical ideas and practical value for the development of potential geologic hazard identification on tourist routes.

scholarly journals Design of the Piano Score Recommendation Image Analysis System Based on the Big Data and Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuanyuan Zhang
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Big Data ◽  
Convolutional Neural Network ◽  
Recommendation System ◽  
Piano Music ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Data Environment ◽  
Music Recommendation System

In the era of big data, the problem of information overload is becoming more and more obvious. A piano music image analysis and recommendation system based on the CNN classifier and user preference is designed by using the convolutional neural network (CNN), which can realize accurate piano music recommendation for users in the big data environment. The piano music recommendation system based on the CNN is mainly composed of user modeling, music feature extraction, recommendation algorithm, and so on. In the recommendation algorithm module, the potential characteristics of music are predicted by the regression model, and the matching degree between users and music is calculated according to user preferences. Then, music that users may be interested in is generated and sorted in order to recommend new piano music to relevant users. The image analysis model contains four “convolution + pooling” layers. The classification accuracy and gradient change law of the CNN under RMSProp and Adam optimal controllers are compared. The image analysis results show that the Adam optimal controller can quickly find the direction, and the gradient decreases greatly. In addition, the accuracy of the recommendation system is 55.84%. Compared with the traditional CNN algorithm, this paper uses the convolutional neural network (CNN) to analyze and recommend piano music images according to users’ preferences, which can realize more accurate piano music recommendation for users in the big data environment. Therefore, the piano music recommendation system based on the CNN has strong feature learning ability and good prediction and recommendation ability.

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