scholarly journals Volleyball Data Analysis System and Method Based on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xianyan Dai ◽  
Shangbin Li
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Rapid Development ◽  
Gluteus Maximus ◽  
Economic Benefits ◽  
Practical Significance ◽  
Beijing Olympics ◽  
Type Group ◽  
Volleyball Players ◽  
Sports Data

After the reform and the opening up, the economy of my country has grown rapidly and people’s lives have become better and better. As a result, there is a lot of time to pay attention to their health, which has promoted the rapid development of my country’s sports industry. Since the 2008 Beijing Olympics, the successful hosting of the Beijing Olympics has been further strengthened. With the rise of the development of sports in our country, the use of machine learning in a large amount of information can process this data and analyze it well. Based on this, this article is aimed at making volleyball players and coaches better understand the technical structure of hiking and the technique of hiking. The paper understands the characteristics of muscle activity over time and uses machine learning methods to analyze a large number of volleyball sports data. In this experiment, 12 volleyball players from a college of physical education were selected. According to the actual situation of the students’ physical fitness and skills, it is more reasonable to divide them into two arms with preswing technology (A type) group and two-arms without preswing technology (B type) group. Mainly study the volleyball spiking action, select the representative front-row 4th position strong attack and the back-row 6th position for comparison and analysis, and analyze the process from the take-off stage to the aerial shot stage in the four stages of the smash through the kinematics, dynamics, and surface electromyography parameters. Experiments have shown that for type A, the left gluteus maximus integral EMG sum value is significantly different between the front and rear rows ( P < 0.05 ). The discharge volume of the left gluteus maximus during the front-row spiking process is greater than that of the back-row spiking. This difference is mainly reflected in the kicking stage and the air attack stage. It shows that volleyball data analysis has a very broad prospect of exploration and application, which can create huge social and economic benefits. How to analyze kinematics is also a very demanding research project and is also part of the future analysis of sports data. Academic value and broad practical significance are important.

scholarly journals PMData: A sports logging dataset

2020 ◽  
Author(s):  
Vajira Thambawita ◽  
Steven Hicks ◽  
Hanna Borgli ◽  
Svein Arne Pettersen ◽  
Håkon Kvale Stensland ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Smartphone Application ◽  
Sleep Patterns ◽  
Sports Activity ◽  
Activity Data ◽  
Machine Learning Applications ◽  
Sports Data

In this paper, we present PMData: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for instance, additional sports data is used to predict and analyze everyday developments, like a person's weight and sleep patterns; and applications where traditional lifelog data is used in a sports context to predict athletes' performance. \datasetname combines input from Fitbit Versa 2 smartwatch wristbands, the PMSys sports logging smartphone application, and Google forms. Logging data has been collected from 16 persons for five months. Our initial experiments show that novel analyses are possible, but there is still room for improvement.

Machine Learning for Mass Spectrometry Data Analysis in Proteomics

2020 ◽  
Vol 17 ◽  
Author(s):  
Juntao Li ◽  
Kanglei Zhou ◽  
Bingyu Mu
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Support Vector Machine ◽  
Data Analysis ◽  
Rapid Development ◽  
Point Of View ◽  
Mass Spectrometry Data ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

: With the rapid development of high-throughput techniques, mass spectrometry has been widely used for largescale protein analysis. To search for the existing proteins, discover biomarkers, and diagnose and prognose diseases, machine learning methods are applied in mass spectrometry data analysis. This paper reviews the applications of five kinds of machine learning methods to mass spectrometry data analysis from an algorithmic point of view, including support vector machine, decision tree, random forest, naive Bayesian classifier and deep learning.

scholarly journals Prediction of Volleyball Competition Using Machine Learning and Edge Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qiang Liu ◽  
Qiannan Liu
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Science ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Data Mining Algorithm ◽  
Human Society ◽  
Apriori Algorithm ◽  
Proposed Model ◽  
The Way

Data analysis and machine learning are the backbones of the current era. Human society has entered machine learning and data science that increases the data capacity. It has been widely acknowledged that not only does the number of information increase exponentially, but also the way of human information management and processing is completed to be changed from manual to computer, mainly depending on the transformation of information technology including a computer, network, and communication. This paper is aimed at a solution to the lag of the methods and means of volleyball technique prediction in China. Through field visits, it is found that the way of analysis and research of techniques and tactics in Chinese volleyball practice is relatively backward, which to a certain extent affected the rapid development of Chinese volleyball. Therefore, it is a necessary and urgent task to realize the reform of the methods and means of volleyball technical and tactical analysis in China. The data analysis and prediction are based on the machine learning and data mining algorithm applied to volleyball in this paper is an inevitable trend. The proposed model is applied to the data produced at the edges of the systems and thoroughly analyzed. The Apriori algorithm of the machine learning algorithm is utilized to process the data and provide a prediction about the strategies of a volleyball match. The Apriori algorithm of machine learning is also optimized to perform better data analysis. The effectiveness of the proposed model is also highlighted.

scholarly journals Smart Medical Prediction for Guidance: A Mechanism Study of Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiangming Wang ◽  
Baobao Dong
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Prediction Model ◽  
Learning Technologies ◽  
Practical Significance ◽  
Learning Technology ◽  
Enterprise Strategy ◽  
Mechanism Study ◽  
Smart Healthcare ◽  
Medical Analysis

Data analysis and prediction have gradually attracted more and more attention in the smart healthcare industry. The smart medical prediction system is of great importance to the enterprise strategy and business development, and it is also of great value to provide medical advices for patients and assist patient guidance. The research theme is the use of machine learning technologies with the application in the areas of smart medical analysis. In this paper, the actual data of the smart medical industry were statistically analysed and visualized according to the features, and the most influential feature combinations were selected for the establishment of the prediction model. Based on machine learning technology, namely, random forest, the guidance prediction model is established, and the combination of features is repeatedly adjusted to improve its accuracy. The practical significance of this paper is to provide a high-precision solution for smart medical data analysis and to realize the proposed data analysis and prediction on the cloud platform based on the Spark environment.

Machine Learning Based Predictive Action on Categorical Non-Sequential Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1020-1030
Author(s):  
Pradeep S. ◽  
Jagadish S. Kallimani
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Categorical Data ◽  
Numerical Data ◽  
Processing Technique ◽  
Machine Learning Algorithms ◽  
Sequential Data ◽  
Industry Standard ◽  
Robust Model ◽  
Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

Machine learning-assisted production data analysis in liquid-rich Duvernay Formation

2021 ◽  
Vol 200 ◽  
pp. 108377
Author(s):  
Bing Kong ◽  
Zhuoheng Chen ◽  
Shengnan Chen ◽  
Tianjie Qin
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Production Data
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