scholarly journals Basketball Sports Injury Prediction Model Based on the Grey Theory Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Fengyan Zhang ◽  
Ying Huang ◽  
Wengang Ren
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Sports Injuries ◽  
Sports Injury ◽  
Time Interval ◽  
Single Model ◽  
Interval Model ◽  
Injury Prediction ◽  
Combination Model ◽  
Gray Theory

Sports injuries will have an impact on the consistency and systemicity of the training process, as well as athlete training and performance improvement. Many talented athletes have had their careers cut short due to sports injuries. Preventing sports injuries is the best way for basketball players to reduce sports injuries. Many coaches and athletes on sports teams, on the other hand, are unaware of the importance of sports injury prevention. They only realize that the body’s sports functions are abnormal when it suffers from sports injuries. As a result, this paper proposes a gray theory neural network-based athlete injury prediction model. First, from the standpoint of a single model, the improved unequal interval model is used to predict sports injury by optimizing the unequal interval model in gray theory. The findings show that it is a good predictor of sports injuries, but it is a poor predictor of the average number of injuries. Following that, in order to overcome the shortcomings of a single model, a gray neural network combination model was used. A combination model of the unequal time interval model and BP neural network was determined and established. The prediction effect is significantly improved by combining the gray neural network mapping model and the coupling model to predict the two characteristics of sports injuries. Finally, simulation experiments show that the proposed method is effective.

Weight Loss Prediction Model for Pig Carcass Based on a Genetic Algorithm Back- Propagation Neural Network

2020 ◽  
Vol 63 (4) ◽  
pp. 1071-1077
Author(s):  
Chenyang Sun ◽  
Lusheng Chen ◽  
Yinian Li ◽  
Hao Yao ◽  
Nan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Weight Loss ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Time Interval ◽  
Spraying Parameters ◽  
Pig Carcasses

HighlightsWe propose five spraying parameters according to the characteristics of pig carcasses in the spray-chilling process.A prediction model for pig carcass weight loss, based on a genetic algorithm back-propagation neural network, is proposed to reveal the relationship between weight loss and spraying parameters.To study the effects of various spraying parameters on weight loss, an automatic spray-chilling device was designed, which can modify up to five spraying parameters.Abstract. Because the weight loss of a pig carcass in the spray-chilling process is easily affected by the spraying frequency and duration, a prediction model for weight loss based on a genetic algorithm (GA) back-propagation (BP) neural network is proposed in this article. With three-way crossbred pig carcasses selected as the test materials, the duration and time interval of high-frequency spraying, the duration and time interval of low-frequency spraying, and the duration of a single spray were selected as inputs to the network model. The weight and threshold of the network were then optimized by the GA. The prediction model for pig carcass weight loss established by the GA BP neural network yielded a correlation coefficient of R = 0.99747 between the network output value of the test samples and the target value. Weight loss prediction by the model is feasible and allows better expression of the nonlinear relationship between weight loss and the main controlling factors. The results can be a reference for chilled meat production. Keywords: BP neural network, Genetic algorithm, Pig carcass, Predictive model, Weight loss

scholarly journals Early Warning System of Tennis Sports Injury Risk Based on Mobile Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yupeng Li ◽  
Kitak Kim ◽  
Yu Ding
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Injury Risk ◽  
Sports Injuries ◽  
Rbf Neural Network ◽  
Warning System ◽  
Sports Injury ◽  
Analysis Method ◽  
Tennis Players

In recent years, sports injuries in professional tennis players have gradually increased and sports injuries will break the sports training system and affect the long-term growth of new tennis players. Avoiding athlete injuries has become an important factor in improving training quality and game performance and ensuring the sustainable development of young tennis players’ competitiveness. Therefore, this article will use the RBF neural network algorithm and cluster analysis method to establish a tennis sports injury risk early warning model and finally establish a tennis sports injury risk early warning system so that tennis players can reduce their injuries. In this article, we use the questionnaire survey method, expert interview method, mathematical statistics method, and logical analysis method to investigate and analyze the results of training injuries of Chinese tennis players and coaches. The experimental results in this article show that among 48 tennis players of different ages, who are participating in formal training and tennis competitions, 15 young tennis players have been injured more than 6 times, accounting for 31.2% of the total; 20 have been injured 3 to 6 times, accounting for 41.7% of the total; 9 of them have been injured several times, accounting for 18.8% of the total; and 4 have been injured, accounting for 8.3% of the total. After using the tennis sports injury risk warning system based on the algorithm of RBF neural network in mobile computing, the tennis sports injury rate has dropped to 5%. It can be seen that the system has high feasibility and practicability.

scholarly journals Prediction Model of Juvenile Football Players’ Sports Injury Based on Text Classification Technology of Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Kai He
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Text Classification ◽  
Sports Injury ◽  
Ligament Injury ◽  
Football Players ◽  
Training Methods ◽  
Joint Injury ◽  
Injury Prediction ◽  
Exercise Load

As the level of soccer in our country has improved rapidly, the level of skill has gradually improved, and the requirements for training of athletes have increased. Due to changes in athlete training methods, it has been decided that athletes must bear a great risk of sports injuries. Accurate prediction of injuries is very important for the development of youth soccer. Based on this, this paper proposes a text classification algorithm based on machine learning and builds a sports injury prediction model that can accurately predict athlete injuries, reduce athlete injuries during training, and be effective. We put forward various sports suitable for young athletes, and put forward some measures to prevent and alleviate athletes’ injuries. This article selects 48 football players from a college of physical education of a university for testing. The athletes participating in the experiment use professional equipment to collect exercise volume and exercise load data, and real-time records of each athlete's physical fitness data within half a year, through the athlete's exercise volume, exercise load, body metabolism, and physical indicators to predict their sports injury. Experiments show that from the degree of injury, it can be seen that the severe injury is the least, with 5 cases of muscle injury, 2 cases of fascia ligament injury, and 1 case of joint injury. There were 25 cases of mild injuries, accounting for 41.0% of the total. This is because the athlete’s sports injury prediction model has better prediction capabilities, allowing athlete coaches and therapists to optimize training courses, ultimately preventing injuries, improving training levels, and reducing rehabilitation costs.

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

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