Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation

In recent years, with the rapid development of a new generation of artificial intelligence technology, how to deeply apply artificial intelligence technology to physical education and break through the limitations of time-space scenarios and knowledge transfer methods in traditional models has become a key issue in intelligent physical education in the era of artificial intelligence. In order to realize the online monitoring of wearable devices with artificial intelligence in sports and overcome the problem of low recognition accuracy of electrocardiogram, blood oxygen, and respiratory signals in many cases, this paper proposes a combination of variational modal decomposition based on the maximum envelope kurtosis method. Long-short-term neural network (VMD-LSTM) monitoring method for wearable sports equipment. Through experimental analysis and verification, the current signal of the VMD model shows a trend of fluctuating from large to stable and then to large with motion, while the training accuracy of LSTM after the 150th iteration is 94.09%, which shows that the coupling model VMD LSTM can better predict the direction of sports artificial intelligence. In addition, although the training time of the BP neural network is shorter than that of the LSTM model, there is a large gap between the recognition effect and the LSTM, and there are also large differences between different neural network structures. This shows that the VMD-LSTM model has broad application prospects in such models.

Section: Normalizationsupporting

confidence: 79%

Application of Improved VMD-LSTM Model in Sports Artificial Intelligence

Zhang

2022

“…As a commonly used convolutional neural network for deep learning [ 12 , 13 ], AlexNet is improved based on the classic network LeNet, and on this basis, it can obtain more deep image features by extending the depth of the network. AlexNet is deeper than LeNet in terms of network depth and uses the combined structure of the convolution layer and pooling layer to obtain the features of graphic images [ 14 ]. At the same time, AlexNet also uses dropout regularization to suppress overfitting and uses the ReLU function instead.…”

Section: Model Introductionmentioning

confidence: 99%

Decision-Making Application of the Cloud-Fog Hybrid Model Based on the Improved Convolutional Neural Network in Financial Services in Smart Medical Care

2022

In order to alleviate the “difficulty in seeing a doctor” for the masses, continuously optimize the service process, and explore new financial service processes for admission and discharge, this study proposes a cloud-fog hybrid model UCNN-BN based on an improved convolutional neural network and applies it to financial services in smart medical care. Decision-making applications: this research improves and designs the UCNN network based on AlexNet and introduces small convolution layers to form convolution groups, making the network more adjustable. The network structure is simpler and more flexible, and it is easy to adjust the algorithm. The number of parameters is small, and it can be directly superimposed without having to add new network hidden layers. The experimental results show that the recognition rate of the UCNN network on the FER2013 and CK+ datasets is higher than that of other recognition methods, and the recognition rates on the FER2013 and CK+ datasets are 98% and 68.01%, due to other methods. This shows that the improved convolutional neural network used in this study for financial services in smart medical care has certain applicability, and small convolution kernels help to extract more subtle features, so as to identify more accurately.

“… To find the inner connection between fuzzy cluster analysis and children's geometric graphic education activities, and to dig, filter, and refine the mathematical elements in the resources that are suitable for children's geometric graphic education activities. To address the shortcomings that the fuzzy C-mean clustering image segmentation [ 15 ] algorithm does not consider the different degrees of the contribution of each dimensional feature to the clustering and easily falls into local optimum, an improved fuzzy clustering algorithm based on kernel function is proposed. …”

Section: Introductionmentioning

confidence: 99%

“…(3) To address the shortcomings that the fuzzy C-mean clustering image segmentation [15] algorithm does not consider the different degrees of the contribution of each dimensional feature to the clustering and easily falls into local optimum, an improved fuzzy clustering algorithm based on kernel function is proposed.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Graphic Perception Education for Young Children Based on Fuzzy Clustering Analysis

Gui

Suh

2022

Geometric ability includes elements of identification, conceptualization, combination, drawing, and reasoning, and graphic perception is an important part of it. Kindergarten science education includes geometry instruction. Children are guided to perceive the relationship between shapes and figures through direct perception, first-hand experience, and practical operation through concentrated educational activities, and form image-concrete thinking over time, enhancing their perception and experience of the relationship between shapes in the objective world, and accumulating certain mathematical perceptual experience. Clustering is a branch of unsupervised pattern recognition that is very useful. Fuzzy clustering, which establishes the uncertainty description of samples to categories and can objectively reflect the real world, has become the mainstream of cluster analysis research. The graphics perception education of children is investigated using fuzzy clustering analysis. The main topic of this paper is how to apply children’s graphics to the design of children’s educational institutions and open up new creative perspectives for the design of children’s educational institutions. The method of graphic perception education: perception education for preschool children is proposed based on the multichannel characteristics of preschool children’s aesthetic perception and with reference to the theory of perception. The experimental results show that the improved algorithm reduces segmentation time by 171.48 s when compared to the traditional FCM algorithm for both noisy and high-quality images and that it is significantly faster than the FCM algorithm in terms of segmentation speed. As a result, the model construction of a set of children’s graphic perception education for the cognitive characteristics of the age group children can provide corresponding references and references for related topic research.