Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Long, Shirong; Zhao, Xuekong

doi:10.3233/jifs-189048

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…To overcome the shortcomings of premature convergence of standard particle swarm optimization (PSO) algorithm, they proposed an improved multi PSO algorithm strategy. Moreover, to improve the premature problem of PSO in search performance, they combined the algorithm with the useful attributes of other algorithms to improve the diversity of particles in the algorithm, enhance the global search ability of particles, and achieve effective feature extraction [ 24 ]. To sum up, there are many research results on the application of deep learning in HCI, but few studies on the combination of the two for dance action extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

Jin

Wen

Xie

2022

Computational Intelligence and Neuroscience

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The paper aims to apply the deep learning-based image visualization technology to extract, recognize, and analyze human skeleton movements and evaluate the effect of the deep learning-based human-computer interaction (HCI) system. Dance education is researched. Firstly, the Visual Geometry Group Network (VGGNet) is optimized using Convolutional Neural Network (CNN). Then, the VGGNet extracts the human skeleton movements in the OpenPose database. Secondly, the Long Short-Term Memory (LSTM) network is optimized and recognizes human skeleton movements. Finally, an HCI system for dance education is designed based on the extraction and recognition methods of human skeleton movements. Results demonstrate that the highest extraction accuracy is 96%, and the average recognition accuracy of different dance movements is stable. The effectiveness of the proposed model is verified. The recognition accuracy of the optimized F-Multiple LSTMs is increased to 88.9%, suitable for recognizing human skeleton movements. The dance education HCI system’s interactive accuracy built by deep learning-based visualization technology reaches 92%; the overall response time is distributed between 5.1 s and 5.9 s. Hence, the proposed model has excellent instantaneity. Therefore, the deep learning-based image visualization technology has enormous potential in human movement recognition, and combining deep learning and HCI plays a significant role.

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Section: Literature Reviewmentioning

confidence: 99%

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

Jin

Wen

Xie

2022

Computational Intelligence and Neuroscience

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“…is increases the particle diversity of the algorithm and improves the overall particle search performance. As a result, efficient feature extraction is achieved [3]. Considering the analytical hierarchical process, the participating researchers have conducted the following studies.…”

Section: Related Workmentioning

confidence: 99%

Mathematics Deep Learning Teaching Based on Analytic Hierarchy Process

Duan

2022

Mathematical Problems in Engineering

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Deep learning is an important concept introduced into modern learning science. It is different from the surface learning of mechanically and passively acquiring knowledge and storing individual information but emphasizes learners’ active and critical learning. It wants them to understand the full meaning of what they have learned. By establishing a link between existing knowledge and new knowledge, it transfers existing knowledge to a new environment, makes decisions, and solves problems. Deep learning plays an important role in students’ learning. Deep learning ability is the key factor affecting the quality of learning and the development of students’ academic ability. The quality of in-depth teaching is difficult to guarantee, which requires a complete, comprehensive, and evaluation system to evaluate it. This paper introduces the analytic hierarchy process to weight the indexes in mathematics deep learning and puts forward some suggestions on creating an environment for deep learning. The experimental results show that teachers’ teaching accounts for the highest proportion of primary indicators, reaching 67%. Multimedia resources account for the highest proportion of secondary indicators, reaching 73.01%. This paper then puts forward some suggestions for indicators with large weights.

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“…Extension of subject area: as shown in Figure 9, the inward system of the architectural design major includes not only the content directly related to architectural design such as architectural structure and construction standards, but also the content related to urban planning such as urban layout, urban transportation, and regional planning. Moreover, the outward system of architectural design major involves the content of economics, environmental science, color science, ergonomics, material science and other disciplines [11]. Therefore, when setting up courses for the architectural design major, the curriculum content should be extended, subjects related to the architectural design major should be set as mandatory or elective courses according to their importance degree.…”

Section: Extension Of Teaching Content Scopementioning

confidence: 99%

Analysis of an Extensible Teaching Mode for Cultivating College Students into Innovative Talents

Hui

2021

Int. J. Emerg. Technol. Learn.

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The competition between countries is essentially the competition of innovative talents. However, China now faces multiple problems in the cultivation of innovative talents: the talents are weak in innovation ability; the high-level innovative talents are not sufficient; neither the quality or level of talents fully meet the demand of social and economic development. As a result, it is urgent for colleges to renovate the cultivation mode of innovative talents. Focusing on the cultivation of innovative talents majoring in architectural design, this paper thoroughly analyzed the extensible teaching mode for cultivating college students into innovative talents. Firstly, a questionnaire survey on relevant issues was carried out among employers and college graduates. The survey results revealed several problems with the innovative talent training in Chinese colleges, namely, the irrational curriculum design, the narrow scope of training objectives, and the lack of diverse teaching methods. Results verified, the employers are not highly satisfied with the innovative talents, especially the newly graduated ones. These problems are attributable to the weak professional development ability, poor teamwork ability, and insufficient innovation ability. To solve these problems, the authors analyzed and expounded the extensible teaching mode for the cultivation of innovative talents from four aspects: teaching content scope, teaching methods, teaching space, and appraisal. The research findings enrich the evidences of innovative talent training in Chinese colleges, providing a reference for improving the training quality of innovative talents.

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Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Cited by 15 publications

References 13 publications

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

Mathematics Deep Learning Teaching Based on Analytic Hierarchy Process

Analysis of an Extensible Teaching Mode for Cultivating College Students into Innovative Talents

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