An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field

In recent years, athlete action recognition has become an important research field for showing and recognition of athlete actions. Generally speaking, movement recognition of athletes can be performed through a variety of modes, such as motion sensors, machine vision, and big data analysis. Among them, machine vision and big data analysis usually contain significant information which can be used for various purposes. Machine vision can be expressed as the recognition of the time sequence of a series of athlete actions captured through camera, so that it can intervene in the training of athletes by visual methods and approaches. Big data contains a large number of athletes’ historical training and competition data which need exploration. In-depth analysis and feature mining of big data will help coach teams to develop training plans and devise new suggestions. On the basis of the above observations, this paper proposes a novel spatiotemporal attention map convolutional network to identify athletes’ actions, and through the auxiliary analysis of big data, gives reasonable action intervention suggestions, and provides coaches and decision-making teams to formulate scientific training programs. Results of the study show the effectiveness of the proposed research.

Section: Experimental Results Of Ablation Studies On Spatiotemporal Attention Graph Convolutional Networkmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Machine Vision and Big Data-Driven Sports Athletes Action Training Intervention Model

Jiang

Wang

Lei

2021

“…is model can be summed up in two processes, namely, training process and recommendations, as shown in Figure 3. e training process includes learning platform data processing, such as algorithm design process, based on deep learning algorithm, optimizing the depth of the neural network [21][22][23][24], and more efficient and reasonable training process. e recommended process recommended models which are obtained by training process, obtaining the personalized learning resources.…”

Section: Recommended Modelmentioning

confidence: 99%

College English Flipped Classroom Teaching Model Based on Big Data and Deep Neural Networks

Chang

2021

With the rapid development of information technology, flipped classroom as a new type of mixed teaching mode relying on computer technology has changed the traditional teaching mode and formed a teaching process of “learning first and teaching later,” and it has been used in many fields of teaching. Flipped classroom reverses the sequence of traditional teaching knowledge transfer and knowledge internalization and improves students’ autonomy. However, it is still in the exploratory stage of the specific impact of the flipped classroom teaching model on college students’ English autonomous learning ability. Therefore, this article proposes a novel college English flipped classroom teaching model based on big data and deep neural networks. The study has selected a total of 230 students in two classes of the second-year English major of a university as the research objects. Data are utilized to investigate the changes of the two groups of students’ English autonomous learning ability and English academic performance, to explore the specific changes of college students’ English autonomous learning ability and its influencing factors through interviews, and to predict and effectively analyze the weight of influencing factors through the deep neural network. This research enriches the theoretical research results of college students’ English autonomous learning ability under the flipped classroom teaching model, provides reference for the cultivation of college students’ English autonomous learning ability, and has certain reference significance for the optimization of the flipped classroom teaching model. The proposed research will support researchers and practitioners at college and university level.

“…Due to its shallow structure, the classifier limits the learning of music features, and it is difficult to extract more effective features to represent music, which affects the accuracy of classification. In recent years, deep neural networks [9][10][11][12] have achieved good results in natural language processing, computer vision [13][14][15][16], and other research fields. e deep neural network model can automatically learn deeper features from the shallow features and can reflect the local relevance of the input data.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Classification Model of Music Genres and Chinese Traditional Musical Instruments Based on Deep Neural Networks

2021

The teaching of ideological and political theory courses and daily ideological and political education are two important parts of education for college students. With the iterative update of information technology, the individualized development of students, and the reform and innovation of ideological and political education, higher goals and requirements have been put forward for ideological and political education. Some universities have developed new paths in the teaching model, but they have not considered the evaluation module and paid little attention to their own development. They only paid attention to the fact that it injected fresh blood into the reform of education model and ideological education but ignored the improvement of their own quality. Therefore, with these limitations, the learning effect is not satisfactory. Keeping in view these issues, this article defines the concept of deep learning and ideological and political education of college students as the starting point and then analyzes the new precise and personalized concepts, new forms of intelligent teaching and evaluation, and new models of intelligent learning that deep learning brings to college students’ ideological and political education. This is a new path of intelligent linkage with the subject, object, and mediator. It can deepen the reform of the education and teaching mode of individualization, accuracy, interactivity, and vividness of college students’ ideological and political education and improve the evaluation and management of college students’ ideological and political education. The experimental results of the study showed the effectiveness of the proposed study.