Intelligent medical heterogeneous big data set balanced clustering using deep learning

Li, Xiaofeng; Jiao, Hongshuang; Dong, Li

doi:10.1016/j.patrec.2020.08.027

Cited by 22 publications

(18 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Big data was first proposed in the 1990s, and the definition of big data has been controversial since it was proposed. It is difficult to process it effectively using current data management tools and processing methods [10][11][12]. Big data is the collection of large quantities, high speed, and diverse information that aids efficient decision-making, discovery of insights, and optimal processing through new processing mechanisms that differ from traditional methods.…”

Section: Biomechanics In Big Data Table Tennis Trainingmentioning

confidence: 99%

[Retracted] Biomechanics and Neuromuscular Control Training in Table Tennis Training Based on Big Data

Zhang

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

Thinking of big data as a collection of huge and sophisticated data sets, it is hard to process it effectively with current data management tools and processing methods. Big data is reflected in that the scale of data exceeds the scope of traditional volume measurement, and it is difficult to collect, store, manage, and analyze through traditional methods. Analyzing the biomechanics of table tennis training through big data is conducive to improving the training effect of table tennis, so as to formulate corresponding neuromuscular control training. This paper mainly analyzes various indicators in biomechanics and kinematics in table tennis training under big data. Under these metrics, an improved decision tree method was then used to analyze the differences between athletes trained for neuromuscular control and those who did not. It analyzed the effect of neuromuscular control training on the human body through different experimental control groups. Experiments showed that after nonathletes undergo neuromuscular control training, the standard rate of table tennis hitting action increases by 10% to 20%, reaching 80%. The improvement of athletes is not very obvious.

show abstract

Section: Biomechanics In Big Data Table Tennis Trainingmentioning

confidence: 99%

[Retracted] Biomechanics and Neuromuscular Control Training in Table Tennis Training Based on Big Data

Zhang

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

show abstract

“…It has the characteristics of self-organization and self-learning. Although the construction of a single neuron is simple and its function is limited, it can bring together large numbers of neurons to form a kind of complex network [ 18 , 19 ]. This is a learning algorithm that trains according to certain rules and applies them in practice.…”

Section: Influence Algorithm Of Exercise Biomechanics In Martial Arts...mentioning

confidence: 99%

[Retracted] Influence of Sports Biomechanics on Martial Arts Sports and Comprehensive Neuromuscular Control under the Background of Artificial Intelligence

Zhang

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

Neuromuscular control refers to the reflexes of nerves that affect muscle balance and function. In addition, there are interactions between joint structure, muscle function, and the central nervous system. In the integration with other intelligent control methods and optimization algorithms, such as fuzzy control/expert verification and genetic algorithm, it provides nonparametric object models, optimization parameters, reasoning models, and fault diagnosis. The central nervous system is the main research object of neuromuscular control. Martial arts often cause injuries or affect the progress of martial arts because of some irregular movements. Chinese traditional martial arts is another name for “martial arts” in the late Qing Dynasty in China. It is mainly reflected in the individual’s application and attainments in martial arts traditional teaching methods and personal cultivation. Therefore, this paper proposes an analysis of the influence of sports biomechanics on martial arts sports and comprehensive neuromuscular control in the context of artificial intelligence. In this paper, the specific research of Wushu sports is carried out mainly in two aspects: sports biomechanics and neuromuscular control. It uses a variety of algorithms, successively using particle swarm algorithm, neural network structure, fitness function, and so on. This paper compares and analyzes their accuracy and then selects the optimal algorithm. It then conducts experimental research on the martial arts movements of professional martial arts Sanda players. The final experimental conclusion shows that, regarding lower limb selective response time and the middle left lower limb prereaction time (L-PMT) of the elite athlete group and the ordinary athlete group, the average movement value of the elite group of 2.336 is significantly greater than that of the ordinary group of 1.938. This shows that, within a certain range, the larger the knee angle and the smaller the hip angle, the stronger the ability to buffer the impact of the ground, without causing greater damage to the muscles and joints.

show abstract

“…In the edge segmentation of the computed tomography sequence image, each pixel has corresponding case identification number information and semantic category information such as department and disease, the lesion feature information is more important, which can be predicted by actual branches and semantic branches [ 31 , 32 ]. Taking instance branching as an example, the existing network framework is designed according to the evaluation index of instance edge segmentation, that is, only the prediction accuracy of each instance is considered, and the relationship between different types of instances is not processed.…”

Section: Methodsmentioning

confidence: 99%

Optimization algorithm of CT image edge segmentation using improved convolution neural network

Wang

Wei

2022

PLoS ONE

View full text Add to dashboard Cite

To address the problem of high failure rate and low accuracy in computed tomography (CT) image edge segmentation, we proposed a CT sequence image edge segmentation optimization algorithm using improved convolution neural network. Firstly, the pattern clustering algorithm is applied to cluster the pixels with relationship in the CT sequence image space to extract the edge information of the real CT image; secondly, Euclidean distance is used to calculate similarity and measure similarity, according to the measurement results, convolution neural network (CNN) hierarchical optimization is carried out to improve the convergence ability of CNN; finally, the pixel classification of CT sequence images is carried out, and the edge segmentation of CT sequence images is optimized according to the classification results. The results show that the overall recognition rate of this method is at a high level. The training time is obviously reduced when the training times exceed 12 times, the recall rate is always about 90%, and the accuracy of image segmentation is high, which solves the problem of large failure rate and low accuracy.

show abstract

Intelligent medical heterogeneous big data set balanced clustering using deep learning

Cited by 22 publications

References 15 publications

[Retracted] Biomechanics and Neuromuscular Control Training in Table Tennis Training Based on Big Data

[Retracted] Biomechanics and Neuromuscular Control Training in Table Tennis Training Based on Big Data

[Retracted] Influence of Sports Biomechanics on Martial Arts Sports and Comprehensive Neuromuscular Control under the Background of Artificial Intelligence

Optimization algorithm of CT image edge segmentation using improved convolution neural network

Contact Info

Product

Resources

About