“…e movements of ice hockey and football players are analyzed and studied. e movement direction of players is identified by using the histogram of oriented gradient (HOG) and hidden Markov model (HMM) [11][12][13]. e player's gestures in the lowresolution tennis game video are recognized [14].…”
This paper examines the problem of athletes’ training in sports, exploring the methods and means by which athletes can perform difficult movements in which they normally make minor training errors in order to achieve better competition results and placements. To this end, we test the explanatory and predictive effects of a theoretical model starting with planned behaviour and then use exercise planning, self-efficacy, and support as variables to develop a partial least squares regression model of sports to improve the explanation and prediction of sporting athletes’ intentions and behaviour. An improved RBF network-based method for player behaviour prediction is proposed. On the basis of the RBF analysis, the number of layers and the number of neurons in the hidden layer of the network are adjusted and optimised, respectively, to improve its generalisation and learning abilities, and the athlete behaviour prediction model is given. The results demonstrate the advantages of the improved algorithm, which in turn provides a more scientific approach to the current basketball training.
“…e movements of ice hockey and football players are analyzed and studied. e movement direction of players is identified by using the histogram of oriented gradient (HOG) and hidden Markov model (HMM) [11][12][13]. e player's gestures in the lowresolution tennis game video are recognized [14].…”
This paper examines the problem of athletes’ training in sports, exploring the methods and means by which athletes can perform difficult movements in which they normally make minor training errors in order to achieve better competition results and placements. To this end, we test the explanatory and predictive effects of a theoretical model starting with planned behaviour and then use exercise planning, self-efficacy, and support as variables to develop a partial least squares regression model of sports to improve the explanation and prediction of sporting athletes’ intentions and behaviour. An improved RBF network-based method for player behaviour prediction is proposed. On the basis of the RBF analysis, the number of layers and the number of neurons in the hidden layer of the network are adjusted and optimised, respectively, to improve its generalisation and learning abilities, and the athlete behaviour prediction model is given. The results demonstrate the advantages of the improved algorithm, which in turn provides a more scientific approach to the current basketball training.
“…rough the above phrase filtering, we finally obtained 199,075 Meituan phrases that only appeared once in the text and contained only two words [31].…”
In the Web 2.0 era, the problem of uneven quality and overload of online reviews is very serious, and the cognitive cost of obtaining valuable content from them is getting higher and higher. This paper explores an effective solution to address comment overload by means of information recommendation in order to improve the utilization of online information and information service quality. This paper proposes a review ranking recommendation scheme that focuses on the information quality of reviews and places more emphasis on satisfying users’ personal information need. The paper’s approach is used to extract and rank low-frequency keywords that appear only once in the comment set. The more useful the extracted phrases are, the more useful this review will be and the higher the usefulness votes will be, which can reflect the actual situation of this product more objectively and accurately and facilitate better consumption decisions for consumers. The experimental results show that users’ satisfaction with the perceived usefulness of the reviews is jointly influenced by the information quality of Meituan’s reviews and users’ individual information needs; the recommendation strategy achieves the organic integration of the two, and the evaluation results under three different recommendation modes show that compared with “interest recommendation” and “utility recommendation,” the satisfaction score of “fusion recommendation” is the highest
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