The Application of RBF Neural Network Model Based on Deep Learning for Flower Pattern Design in Art Teaching

Xiao, Lijun; Luo, Yan

doi:10.1155/2022/4206857

Cited by 3 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Everyone may examine the exhibits at the e-museum and interact with them at their own speed, which encourages greater openness to learning. It is a great strategy for involving, educating, and entertaining students, as well as giving older and more seasoned art aficionados fresh information, viewpoints, and freedom (Bello & Mohamed, 2018;Xiao & Luo, 2022).…”

Section: Findings and Discussionmentioning

confidence: 99%

The Use of the North Sumatra E-Museum on Resource Reading Skills in Anthropology

Maswita

Lubis

2023

AIJP

View full text Add to dashboard Cite

The purpose of this research is to prove the improvement of reading skills of Al-Azhar University law students through the e-Museum. A post-test research design with only a control design is the quantitative experimental approach used in this method. Primary school students in the second grade served as the study population; they were split evenly into two groups (20 in the experimental group and 18 in the control group). The normality test, homogeneity test, and paired sample t-test are the data analysis methods used to determine the value of Sig. (2-tailed), where a value of 0.000 0.05 indicates that Ha is accepted and Ho is rejected. There were also notable differences between the control and experimental groups. It concluded that the e-Museum could help law students in their anthropological classes become better readers. This means that everyone accessing the E-museum needs to pay close attention to its digitally recorded and easily accessible archive of historically significant, scientifically significant, and culturally significant visual and aural materials.

show abstract

Section: Findings and Discussionmentioning

confidence: 99%

The Use of the North Sumatra E-Museum on Resource Reading Skills in Anthropology

Maswita

Lubis

2023

AIJP

View full text Add to dashboard Cite

show abstract

“…It estimates positive result count while evaluating system of art education. Then the formula is derived in equation (17).…”

Section: ) Precisionmentioning

confidence: 99%

“…The investigation outcomes of DL demonising auto encoders were enhanced through the application of machine learning algorithms, which also provided references for data processing, stability, security of digital art education platforms [16]. Lastly, even though industrial hazardous gas tracking process suggested by has some relevance to topic of art education platform, findings of the parallel optimization framework, energy usage reduction research provide particular references for platform's optimization, on-going development [17]. The research mentioned above, which covered techniques and technologies from numerous industries, produced significant improvements and optimizations for this platform [18].…”

Section: Introductionmentioning

confidence: 99%

Optimized Hamiltonian Deep Neural Network-Based Art Education Teaching Content and Evaluation System

Qiuyu Long

2024

jes

View full text Add to dashboard Cite

The organized educational processes and experiences created to promote a comprehension and appreciation of the visual arts are referred to as art education. It includes a variety of exercises designed to foster artistic abilities, the ability to express oneself creatively, and a greater understanding of artistic ideas and background. There are several places where art instruction can take place, such as private studios, museums, community centers, and schools. In this manuscript, Optimized Hamiltonian Deep Neural Network-Based Art Education Teaching Content and Evaluation System (HDNN-AETC-ES) is proposed. Initially the data is collect from MADS dataset. To execute this, input data is pre-processed using Adaptive Distorted Gaussian Matched Filter (ADGMF) and it removes the noise from collected data. Then, data is fed to Hamiltonian Deep Neural Network (HDNN)for effectively categorized Technology-Based Art Teaching Platform. Generally, HDNN doesn’t express adapting optimization approaches to determine optimal parameters to ensure accurate art teaching. Hence, the Tasmanian Devil Optimization Algorithm (TDOA) to optimize Hamiltonian Deep Neural Network which accurately evaluated the art teaching. Then the proposed HDNN-AETC-ES is implemented and the performance metrics likes accuracy, precision, recall, F1-score, computation time, ROC are analyzed. Performance of the HDNN-AETC-ES approach attains 18.41%, 24.08% and 32.57% higher accuracy, 20.31%, 21.08% and 22.57% higher Precision and 21.41%, 22.08% and 23.55% higher recall when analyzed through existing techniques like art education teaching content using optimization of digital art teaching platform depend on information technology with deep learning (DAT-IT-DL), building arts education policy utilizing tools of out-of-school time youth arts organizations (BAEP-ST-YAO) and investigating arts education effects on school engagement with climate (IAE-ES-EC) methods respectively.

show abstract

“…Input the number of items into the classroom teaching quality evaluation system, evaluate the student scoring situation, and analyze the quality of art teaching courses (Cheng et al, 2018). For the expected results, this article evaluated curriculum (Xiao & Luo, 2022). This article selects ten courses based on the ratings of various supervising experts and distributes a questionnaire survey to students.…”

Section: Analysis Of Neural Network Evaluation Modelmentioning

confidence: 99%

Improving the Efficiency of College Art Teaching Based on Neural Networks

Jin

2024

International Journal of Web-Based Learning and Teaching Technologies

View full text Add to dashboard Cite

How to develop a teaching management system to improve the teaching efficiency of art courses has become an important challenge at present. This article takes university art teaching courses as the research object, uses dynamic L-M algorithm to optimize a large number of parameters, proposes an improved neural networks evaluation model, comprehensively analyzes the main influencing factors of art course teaching effectiveness, and establishes a teaching efficiency index evaluation system. The research results indicate that equating the number of hidden layer nodes to the number of samples can improve the performance of neural networks. The improved L-M algorithm was used to train the neural networks, and the maximum error of all test samples was only 0.04, verifying the feasibility and rationality of the improved neural networks model for evaluating course teaching effectiveness. The research results provide theoretical data support for neural networks to improve the efficiency of university art education.

show abstract

The Application of RBF Neural Network Model Based on Deep Learning for Flower Pattern Design in Art Teaching

Cited by 3 publications

References 24 publications

The Use of the North Sumatra E-Museum on Resource Reading Skills in Anthropology

The Use of the North Sumatra E-Museum on Resource Reading Skills in Anthropology

Optimized Hamiltonian Deep Neural Network-Based Art Education Teaching Content and Evaluation System

Improving the Efficiency of College Art Teaching Based on Neural Networks

Contact Info

Product

Resources

About