scholarly journals Sports Policy and Training Decision Support Method Based on Wireless Sensor Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhi Li
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Real Time ◽  
Average Error ◽  
Scientific Management ◽  
Sports Training ◽  
Scientific Decision ◽  
Decision Support Method ◽  
Sports Data

In recent years, the awareness of sports departments at all levels of society to promote sports through science and has been increasing, and the scientific decision-making and management of sports have been improved to a great extent. With the application of scientific decision-making combined with a real-time sports data monitoring network, the opponent’s advance information can be effectively observed during the game and reasonable decisions can be made to deal with the opponent’s offense. Therefore, high-level athletes appear to be more relaxed and calm in the game. It first requires the application of advanced information collection methods to obtain sports data quickly, in real time and at low cost, and extract information about athletes’ scientific management decision-making from massive data and then make scientific management decisions for sports training. The modern sports method is highly open, and big data mining also profoundly affects the relevant decision-making of sports training. How to design appropriate decision support tools to grasp the key points of the problem in sports information data and make reasonable and correct decisions is a problem that is closely watched by macro decision-makers and coaches at all levels. This article mainly introduces the training decision support method derived from data mining and intends to provide some technical directions for making scientific decisions in sports training. This paper proposes related algorithms of a training decision support method derived from data mining, including training effectiveness prediction model and decision tree algorithm, for the design of the training decision support method derived from data mining. Experimental data shows that the average error between the prediction of the effectiveness of the training method and the actual situation of the training decision support method in this paper is 0.913%, which is helpful for the management or coach to make decisions.

scholarly journals Auxiliary Decision Support Model of Sports Training Based on Association Rules

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Changnian Zhang ◽  
MeiJie Li ◽  
Hui Wang ◽  
Ning Wang
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Association Rules ◽  
Human Body ◽  
Movement Trajectory ◽  
Classification Rules ◽  
Sports Training ◽  
Decision Support Model ◽  
Discriminative Ability ◽  
Support Model

In sports or fitness training, nonstandard movements will affect the training effect and even lead to sports injuries. However, the standard movements of various sports activities need professional guidance, so it is difficult to find out whether the movements are standard or not. In recent years, body pose estimation has become a hot topic in computer vision research. A deep learning model can effectively identify the human nodes and movement trajectory in pictures or videos and evaluate the movements of the target human body. However, the movement process is generally covered by others or the situation of nearby personnel, which leads to the deviation of the movement recognition of the human body and affects the evaluation of the movement. Thus, it is unable to effectively correct the wrong movement, but will mislead the training personnel. Therefore, this paper proposes a novel decision support model for sports training based on association rules. We use posterior probability settings to reveal the weights of the discriminative ability of attribute items and set the classification performance to reflect the weights of three measures to evaluate credit contribution. Thus, the learning threshold setting reflects the weight of the decision-making ability of sports training. Furthermore, compared with traditional association rules, attribute items, frequent item sets, and classification rules that can improve the decision-making performance of sports training are discovered, which complement the deficiencies of different measures. Finally, using the weighted voting strategy to fuse the decision-making information of the classification rules, we can effectively assist in sports training so that the coach can work out corresponding countermeasures and realize scientific management.

THE RESEARCH TREND OF INFORMATION TECHNOLOGY AND DECISION MAKING IN 2009

2010 ◽  
Vol 09 (01) ◽  
pp. 1-8 ◽  
Author(s):  
YONG SHI
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Risk Analysis ◽  
Multiple Criteria Decision Making ◽  
Advisory Board ◽  
Research Trend ◽  
Research Topics

The research topics of the 39 papers published in the International Journal of Information Technology and Decision Making (IT&DM) in 2009 can be classified into three major directions: decision support, multiple criteria decision making, and data mining and risk analysis. The Editor-in-Chief, on behalf of the editorial board and advisory board, highlights the key ideas of these contributions. The seven papers in first issue of 2010 IT&DM are also introduced.

Data Science

2018 ◽  
pp. 141-151
Author(s):  
Sabitha Rajagopal
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Research And Development ◽  
Real Time ◽  
Data Science ◽  
Data Warehousing ◽  
Holistic Approach ◽  
Digital Data ◽  
Data Application ◽  
Development Analysis

Data Science employs techniques and theories to create data products. Data product is merely a data application that acquires its value from the data itself, and creates more data as a result; it's not just an application with data. Data science involves the methodical study of digital data employing techniques of observation, development, analysis, testing and validation. It tackles the real time challenges by adopting a holistic approach. It ‘creates' knowledge about large and dynamic bases, ‘develops' methods to manage data and ‘optimizes' processes to improve its performance. The goal includes vital investigation and innovation in conjunction with functional exploration intended to notify decision-making for individuals, businesses, and governments. This paper discusses the emergence of Data Science and its subsequent developments in the fields of Data Mining and Data Warehousing. The research focuses on need, challenges, impact, ethics and progress of Data Science. Finally the insights of the subsequent phases in research and development of Data Science is provided.

Managing Information for Real-Time Decision Support at Community Level

2013 ◽  
Vol 5 (1) ◽  
pp. 60-76
Author(s):  
Hakikur Rahman
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Life Support ◽  
Human Life ◽  
Community Level ◽  
Information Support ◽  
Space Technology

Management of real time information systems is gaining importance in all sectors and facets of human life. Varying from their applications in aviation, military, government, space technology, earth science, robotics, human cognitive, life support systems, disasters and emergencies, they emerge in diversified forms and natures. This paper identifies various contexts in the decision making processes of community livelihood; contextualizes the relevant information for taking time-critical decision, conceptualizes appropriate decision making methods, tools, and technologies for proper implementation, and manages an appropriate decision support system focusing knowledge acquisition and learning. Along this perspective, the paper establishes a decision support system framework in the aspect of early warning system in reaching out to grass roots community people at their own language, sign, and interpretation; provides knowledge support during disaster management, especially during post-disaster; provides information support in agriculture related matters, focusing pest control, and pre and post harvesting issues; provides emergency health assistance support during road accidents, or emergency health cases, or epidemic breakouts; and finally provides collaborative learning to improve e-governance at community level.

A proactive decision making framework for condition-based maintenance

2015 ◽  
Vol 115 (7) ◽  
pp. 1225-1250 ◽  
Author(s):  
Alexandros Bousdekis ◽  
Babis Magoutas ◽  
Dimitris Apostolou ◽  
Gregoris Mentzas
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Real Time ◽  
Condition Based Maintenance ◽  
Sensor Data ◽  
Extensive Literature ◽  
Decision Space ◽  
Content Type ◽  
Extensive Literature Review

Purpose – The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the context of CBM. Design/methodology/approach – Starting with the manufacturing challenges and the main principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision making are analysed and their potential relevance to CBM is identified. Then, an extensive literature review of methods and techniques for the various steps of CBM is provided, especially for prognosis and decision support. Based on these, limitations and gaps are identified and a framework for proactive decision making in the context of CBM is proposed. Findings – In the proposed framework for proactive decision making, the CBM concept is enriched in the sense that it is structured into two components: the information space and the decision space. Moreover, it is extended in a way that decision space is further analyzed according to the types of recommendations that can be provided. Moreover, possible inputs and outputs of each step are identified. Practical implications – The paper provides a framework for CBM representing the steps that need to be followed for proactive recommendations as well as the types of recommendations that can be given. The framework can be used by maintenance management of a company in order to conduct CBM by utilizing real-time sensor data depending on the type of decision required. Originality/value – The results of the work presented in this paper form the basis for the development and implementation of proactive Decision Support System (DSS) in the context of maintenance.

scholarly journals Analysis of Characteristics of Tennis Singles Matches Based on 5G and Data Mining Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ming Li ◽  
Qinsheng Li ◽  
Yuening Li ◽  
Yunkun Cui ◽  
Xiufeng Zhao ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Statistical Methods ◽  
Analysis Data ◽  
Scientific Basis ◽  
Experimental Simulation ◽  
Mining Technology ◽  
Offensive Tactics ◽  
Analysis System ◽  
Scientific Decision

The level of technical and tactical decision-making in a tennis game has a very important impact on the outcome of the game. How to discover the characteristics and rules of the game from a large amount of technical and tactical data, how to overcome the shortcomings of traditional statistical methods, and how to provide a scientific basis for correct decision-making are a top priority. Based on 5G and association analysis data mining theory, we established a data mining model for tennis technical offensive tactics and association rules and conducted specific case studies. It can calculate the maximization and distribution rate of certain technologies, also distinguish between the athlete’s gain and loss rate and the spatial position on the track, and use artificial statistical methods to cause errors and subjective participation. This solution provides objective and scientific decision support for this problem and is used in the decision-making of the landing point in tennis match technology and tactics. Experimental simulation shows that the data mining technology analysis system used for regional tennis matches is more concise, efficient, and accurate than traditional movie analysis methods.

scholarly journals Using Multi-Criteria Optimization in Decision Support under Risk

2020 ◽  
Author(s):  
Andrzej Łodziński
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Maker ◽  
Previous Method ◽  
Decision Making Process ◽  
Decision Making Under Risk ◽  
Evaluation Functions ◽  
Interactive Decision Making ◽  
The Individual ◽  
Decision Support Method

The chapter presents an extension of a previous method for decision support under risk. The decision-making process is modeled by a multi-criteria optimization problem, in which the individual evaluation functions represent the results of decisions in several possible scenarios with associated risks. The decision support method is an interactive decision-making process. The choice is made by solving the problem depending on the control parameters that define the aspirations of the decision maker as well as on an evaluation of the obtained solutions. The decision maker selects a set of parameters representing various risks’ impacts that influences a solution, and then he/she evaluates the obtained solution by accepting or rejecting it. In another case, the decision maker selects a new value and the problem is solved again for the new parameter. In this chapter, an example of supporting decision-making under risk is presented.

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