Deep Learning for Dance Teaching System

2022 ◽  
pp. 1577-1581
Author(s):  
Yingyi Xu
Keyword(s):  
Deep Learning ◽  
Teaching System

scholarly journals Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuan Fang ◽  
Jingning Li
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Online Teaching ◽  
Calculation Model ◽  
Cosine Similarity ◽  
Personalized Recommendation ◽  
Functional Modules ◽  
Recommendation Algorithm ◽  
Teaching System ◽  
Similarity Calculation

This study provides an in-depth study and analysis of English course recommendation techniques through a combination of bee colony algorithm and neural network algorithm. In this study, the acquired text is trained with a document vector by a deep learning model and combined with a collaborative filtering method to recommend suitable courses for users. Based on the analysis of the current research status and development of the technology related to course resource recommendation, the deep learning technology is applied to the course resource recommendation based on the current problems of sparse data and low accuracy of the course recommendation. For the problem that the importance of learning resources to users changes with time, this study proposes to fuse the time information into the neural collaborative filtering algorithm through the clustering classification algorithm and proposes a deep learning-based course resource recommendation algorithm to better recommend the course that users want to learn at this stage promptly. Secondly, the course cosine similarity calculation model is improved for the course recommendation algorithm. Considering the impact of the number of times users rate courses and the time interval between users rating different courses on the course similarity calculation, the contribution of active users to the cosine similarity is reduced and a time decay penalty is given to users rating courses at different periods. By improving the hybrid recommendation algorithm and similarity calculation model, the error value, recall, and accuracy of course recommendation results outperform other algorithmic models. The requirements analysis identifies the personalized online teaching system with rural primary and secondary school students as the main service target and then designs the overall architecture and functional modules of the recommendation system and the database table structure to implement the user registration, login, and personal center functional modules, course publishing, popular recommendation, personalized recommendation, Q&A, and rating functional modules.

scholarly journals Teaching System of Embedded Mechanical Manufacturing Specialty Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Honghua Liu ◽  
WenPing Tan ◽  
Jingzhong Gong ◽  
Pinghui Hu
Keyword(s):  
Deep Learning ◽  
Vocational Education ◽  
Professional Practice ◽  
Control Group ◽  
Teaching System ◽  
Practice Teaching ◽  
Professional Teaching ◽  
Experimental Group ◽  
Practical Teaching

With the increasing development of electronic computers and the popularization of computer technology, management systems have been widely used in the society. With the continuous improvement of the teaching system, the methods of education and teaching are also quietly changing, especially in the field of higher vocational education. Due to the characteristics of vocational education, it makes “training project progress control” and “teaching resource integration” the teaching requirements of “distribution,” “strictly restricting daily work habits,” “improving professional quality,” and “teachers’ effective control of the quality of students’ personal projects” that have become increasingly urgent needs in vocational colleges. There is a certain gap between the standards and quality of talent training in our country’s applied undergraduate colleges, and the society’s requirements for high-skilled talents and practical teaching in applied undergraduate colleges are a necessary condition for cultivating high-skilled talent. This subject research takes the mechanical manufacturing professional practice teaching system as the research object, conducts in-depth investigation and research in applied undergraduate colleges, explores the status quo of the mechanical manufacturing professional practice teaching system of applied undergraduate colleges, and analyzes the reasons for the problems. In this study, six classes of mechanical manufacturing majors in six applied undergraduate colleges were selected as the experimental group and the control group for comparison. The experimental group adopts an embedded mechanical manufacturing professional teaching system based on deep learning. In daily teaching, the control group used traditional teaching methods to learn. The questionnaire understands the role of the embedded machinery manufacturing professional teaching system in teaching from four aspects: practical teaching curriculum, practical teaching content, practical teaching equipment, and practical teaching teachers. Experiments have proved that more than half of the people think that the teaching effect of the current mechanical manufacturing professional practice teaching courses is average; only 17.18% think that the classroom is good, and 24.36% think that the classroom is poor. This shows that the construction of similar engineering professional practice is in line with the major itself, and the teaching system has theoretical guiding significance, which is conducive to the seamless connection of mechanical manufacturing majors and industrial positions in applied undergraduate colleges; it is conducive to continuously improving the core competitiveness of mechanical manufacturing majors in industrial and applied undergraduate colleges.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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