scholarly journals EFFECTIVE MANAGEMENT OF THE INTERACTION OF SPORTS TEAM MEMBERS BY USING ARTIFICIAL INTELLIGENCE IDEAS

2019 ◽  
Vol 19 (1) ◽  
pp. 72-79
Author(s):  
M Koliada ◽  
T Bugayova ◽  
E Reviakina ◽  
S Belykh ◽  
G Kapranov
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Decision Making ◽  
Intelligent Systems ◽  
Quantitative Methods ◽  
Effective Interaction ◽  
Team Sports ◽  
Scientific Application ◽  
Team Members ◽  
Sports Team

Aim. The objective of the article is to explain both clearly and scientifically the theoretical and methodological foundations of decision-making based on the ideas of artificial intelligence. Materials and methods. We justified the necessity of taking into account the psychological factors connected with coach’s willingness to position players correctly and to achieve the best possible result in the conditions of the game’s unpredictability. The scientific application of the mechanisms for searching the effective interaction of sports team members was given with the help of a genetic algorithm. Results. We revealed the relevance of the issue of players positioning in terms of their better interaction for coaches and sports managers. Practical recommendations were given for a better understanding of decision-making based on the so-called ‘reserved algorithm’. The performance of Darwin’s algorithm in searching for optimal players positioning was demonstrated in details. The efficiency of such an algorithm was proved by making possible to find the best solution in a few steps. An example of the most popular software product for solving such problems in computer intelligent environments is given. Conclusion. We made a conclusion that by using intelligent systems it is possible to perform accurate and objective calculations in the management of sports team members. This also allows making both operational and final decisions regarding the interaction of own and opponent’s team members, which makes possible achieve high results. A coach or PE teacher can forecast precisely achievements in team sports. The application of genetic algorithm is a calculated guarantee of high achievements and the condition for improving quantitative methods in pedagogy.

scholarly journals Artificial intelligence for team sports: a survey

2019 ◽  
Vol 34 ◽  
Author(s):  
Ryan Beal ◽  
Timothy J. Norman ◽  
Sarvapali D. Ramchurn
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Outcome Prediction ◽  
Team Sports ◽  
Fantasy Sports ◽  
Sports Teams ◽  
Injury Prediction ◽  
Tactical Decision ◽  
Research Questions

Abstract The sports domain presents a number of significant computational challenges for artificial intelligence (AI) and machine learning (ML). In this paper, we explore the techniques that have been applied to the challenges within team sports thus far. We focus on a number of different areas, namely match outcome prediction, tactical decision making, player investments, fantasy sports, and injury prediction. By assessing the work in these areas, we explore how AI is used to predict match outcomes and to help sports teams improve their strategic and tactical decision making. In particular, we describe the main directions in which research efforts have been focused to date. This highlights not only a number of strengths but also weaknesses of the models and techniques that have been employed. Finally, we discuss the research questions that exist in order to further the use of AI and ML in team sports.

Diabetes Diagnosis System Based on Support Vector Machines Trained by Vortex Optimization Algorithm

2018 ◽  
pp. 203-218 ◽  
Author(s):  
Sadi Fuat Cankaya ◽  
Ibrahim Arda Cankaya ◽  
Tuncay Yigit ◽  
Arif Koyun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Support Vector Machines ◽  
Optimization Algorithm ◽  
Intelligent Systems ◽  
Support Vector ◽  
Diabetes Diagnosis ◽  
Diagnosis System ◽  
Vector Machines ◽  
Expert Support

Artificial intelligence is widely enrolled in different types of real-world problems. In this context, developing diagnosis-based systems is one of the most popular research interests. Considering medical service purposes, using such systems has enabled doctors and other individuals taking roles in medical services to take instant, efficient expert support from computers. One cannot deny that intelligent systems are able to make diagnosis over any type of disease. That just depends on decision-making infrastructure of the formed intelligent diagnosis system. In the context of the explanations, this chapter introduces a diagnosis system formed by support vector machines (SVM) trained by vortex optimization algorithm (VOA). As a continuation of previously done works, the research considered here aims to diagnose diabetes. The chapter briefly gives information about details of the system and findings reached after using the developed system.

Assessment of Team Cohesion: A Russian Approach

1995 ◽  
Vol 81 (2) ◽  
pp. 443-450 ◽  
Author(s):  
Barry W. Copeland ◽  
William F. Straub
Keyword(s):  
Symbolic Interaction ◽  
Team Sports ◽  
Team Leadership ◽  
Team Cohesion ◽  
Interaction Theory ◽  
Practical Application ◽  
Scientific Application ◽  
Team Members ◽  
Symbolic Interaction Theory ◽  
Computerized System

The CS-1/4, a computerized system for assessing cohesion and conflict, was designed by Kalinin and Nilopets for application with Russian Olympic team sports. The instrument combines Symbolic Interaction Theory with sociometry (Task I), assessment of behavior (Task II), and assessment of personality (Tasks III/IV) to examine the behavior of the group from several perspectives. An overview of the system is presented and its practical application for western research in sports is examined. A composite of team members’ ratings on all four tasks is analyzed through a computer algorithm. The analysis results in a 12-page printout of data pertaining to team leadership tendencies, role status, and compatibility and a summary of the team's dominating drives in terms of achievement, independence, and support characteristics. Suggestions for practical and scientific application with teams in western sports are mentioned.

scholarly journals ARTIFICIAL INTELLIGENCE TECHNOLOGY AND LAW: CHALLENGES OF OUR TIME

2020 ◽  
pp. 524-549
Author(s):  
Y. S. Kharitonova ◽  
◽  
V. S. Savina ◽  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Intelligent Systems ◽  
Legal Regulation ◽  
Original Result ◽  
Legal Personality ◽  
Blockchain Technology ◽  
Legal Science ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Systems

Introduction: the article deals with the issues concerning the protection of the rights to digital content created with the use of artificial intelligence technology and neural networks. This topic is becoming increasingly important with the development of the technologies and the expansion of their application in various areas of life. The problems of protecting the rights and legitimate interests of developers have come to the fore in intellectual property law. With the help of intelligent systems, there can be created not only legally protectable content but also other data, relations about which are also subject to protection. In this regard, of particular importance are the issues concerning the standardization of requirements for procedures and means of storing big data used in the development, testing and operation of artificial intelligence systems, as well as the use of blockchain technology. Purpose: based on an analysis of Russian and foreign scientific sources, to form an idea of the areas of legal regulation and the prospects for the application of artificial intelligence technology from a legal perspective. Methods: empirical methods of comparison, description, interpretation; theoretical methods of formal and dialectical logic; special scientific methods (legal-dogmatic and the method of interpretation of legal norms). Results: analysis of the practice of using artificial intelligence systems has shown that today intelligent algorithms include a variety of technologies that are based on or related to intelligent systems, but not always fall under the concept of classical artificial intelligence. Strictly speaking, classic artificial intelligence is only one of the intelligent system technologies. The results created by autonomous artificial intelligence have features of works. At the same time, there are some issues of a public law nature that require resolution: obtaining consent to data processing from the subjects of this data, determining the legal personality of these persons, establishing legal liability in connection with the unfair use of data obtained for decision-making. Standardization in the sphere and application of blockchain technology could help in resolving these issues. Conclusions: in connection with the identified and constantly changing composition of high technologies that fall under the definition of artificial intelligence, there arise various issues, which can be divided into some groups. A number of issues of legal regulation in this area have already been resolved and are no longer of relevance for advanced legal science (legal personality of artificial intelligence technology); some issues can be resolved using existing legal mechanisms (analysis of personal data and other information in course of applying computational intelligence technology for decision-making); some other issues require new approaches from legal science (development of a sui generis legal regime for the results of artificial intelligence technology, provided that the original result is obtained).

Coaches’ Beliefs About Shy Children and Adolescents in the Context of Team Sports

2020 ◽  
pp. 1-10
Author(s):  
Kim Nguyen ◽  
Robert J. Coplan ◽  
Kristen A. Archbell ◽  
Linda Rose-Krasnor
Keyword(s):  
Children And Adolescents ◽  
Age Differences ◽  
Social Inclusion ◽  
Team Sports ◽  
Sports Participation ◽  
Team Sport ◽  
Sport Organizations ◽  
Team Members ◽  
Sports Team

The goal of this study was to explore coaches’ beliefs about the role of child and adolescent shyness in team sports. Participants were (N = 496) coaches of children and adolescents from team sport organizations across Canada. Coaches responded to open-ended questions asking about the benefits of team sports participation for shy children and adolescents, as well as the special contributions that shy team members may make to a sports team. Among the results, coaches cited improvements in social skills most often as the primary benefits of engaging in team sports for shy team members. Coaches most frequently listed promoting social inclusion, quiet leadership, and being coachable as specific contributions of shy team members. Some age differences were also noted. Results are discussed in terms of implications of shyness for children and adolescents who participate in organized team sports.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2018 ◽  
Vol 3 (2) ◽  
pp. 31-47 ◽  
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Intelligent Systems ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

scholarly journals The Use of Artificial Intelligence Methods to Assess the Effectiveness of Lean Maintenance Concept Implementation in Manufacturing Enterprises

2020 ◽  
Vol 10 (21) ◽  
pp. 7922
Author(s):  
Katarzyna Antosz ◽  
Lukasz Pasko ◽  
Arkadiusz Gola
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Trees ◽  
Set Theory ◽  
Rough Set ◽  
Intelligent Systems ◽  
High Efficiency ◽  
Rough Set Theory ◽  
Conservation Strategy ◽  
Artificial Intelligence Methods

The increase in the performance and effectiveness of maintenance processes is a continuous aim of production enterprises. The elimination of unexpected failures, which generate excessive costs and production losses, is emphasized. The elements that influence the efficiency of maintenance are not only the choice of an appropriate conservation strategy but also the use of appropriate methods and tools to support the decision-making process in this area. The research problem, which was considered in the paper, is an insufficient means of assessing the degree of the implementation of lean maintenance. This problem results in not only the possibility of achieving high efficiency of the exploited machines, but, foremost, it influences a decision process and the formulation of maintenance policy of an enterprise. The purpose of this paper is to present the possibility of using intelligent systems to support decision-making processes in the implementation of the lean maintenance concept, which allows the increase in the operational efficiency of the company’s technical infrastructure. In particular, artificial intelligence methods were used to search for relationships between specific activities carried out under the implementation of lean maintenance and the results obtained. Decision trees and rough set theory were used for the analysis. The decision trees were made for the average value of the overall equipment effectiveness (OEE) indicator. The rough set theory was used to assess the degree of utilization of the lean maintenance strategy. Decision rules were generated based on the proposed algorithms, using RSES software, and their correctness was assessed.

scholarly journals Towards understanding of artificial intelligence in accounting profession

2021 ◽  
pp. 1-5
Author(s):  
Aditya Prasad Sahoo Sahoo ◽  
Dr Yajnya Dutta Nayak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Intelligent Systems ◽  
Feature Detection ◽  
Expert Knowledge ◽  
Object Identification ◽  
Extensive Experience ◽  
Efficiency And Effectiveness ◽  
Repetitive Activities

Accountants have embraced the emission of automation over many years to get better the efficiency and effectiveness of their work. But technology has not been able to replace the need for expert knowledge and decision-making. Earlier generations of ‘intelligent systems have usually demonstrated the progressing power of human expertise and the restrictions of machines. In the upcoming decades, intelligent systems must take over more and better decision-making tasks from humans. While accountant has been using technology for a lot of years to improve what they do and deliver more value to businesses, this is an opportunity to reimagine and radically improve the quality of business and investment decisions which is the ultimate purpose of the profession. Accountants, as expert decision-makers, use both ways of thinking they apply their knowledge to specific situations to make reasoned decisions, although also make quick intuitive decisions based on extensive experience in their field. Today, AI is being used for image recognition, object identification, detection, classification, and automated geophysical feature detection. These are underlying tasks that once required the input of a human. Focusing on how artificial intelligence will impact accountants, AI will very soon help the organization to automate much of the routine and repetitive activities that are undertaken on a daily, weekly or annual basis. It will also help the organization to empower quick decision-making to create smart insights examine huge quantities of data with ease.

Engineering Decision Making in an Open Age

2013 ◽  
Author(s):  
Shuichi Fukuda
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Dynamic Decision Making ◽  
Trial And Error ◽  
Lazy Evaluation ◽  
Team Members ◽  
New Framework ◽  
Previous Step

In an age of diversification and changes, a new framework for decision making for a team is required. As growing complexity and diversification call for team members from a wide variety of areas, a decision cannot be made one-time as it used to be and it must be reached by trials and errors step by step. Such dynamic decision making has to convince members at each step by providing different perspectives for each member to understand the line of reasoning, and must allow lazy evaluation, because some members cannot understand what pieces of knowledge and experience are called for until later step, when clearer perspective is available. Steps proceed by satisfying at least one member. If it fails, then it backtracks to the previous step until it satisfies one more member. This process is repeated until all members are satisfied. Artificial Intelligence allows such trial and error decision making to make all members feel satisfied. The usefulness of this approach is demonstrated by applying developed WPS producing tool to many applications in industry. And it is believed this DDM tool will be very useful for decision making in other areas, too, where systems are very complex and diverse.

scholarly journals Social impact assessment process for industry 4.0 to achieve sustainable artificial intelligence systems

2020 ◽  
Vol 10 (2) ◽  
pp. 27-47
Author(s):  
Vijayan Gurumurthy Iyer
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Impact Assessment ◽  
Intelligent Systems ◽  
Social Impact ◽  
Project Planning ◽  
Assessment Process ◽  
Decision Making Process ◽  
Business Economics ◽  
Artificial Intelligence Systems

The strategic environmental assessment (SEA) process can be broadly defined as a study of the social impacts of a proposed project, plan, policy or legislative action of intelligence systems on the society, environment and sustainability. The SEA process for sustainable intelligent systems has been aimed to incorporate society, environment and sustainability factors into the project planning and decision-making process for sustainable intelligent systems. Artificial intelligence systems (AIS) should consider the titled ‘environmental impact assessment (EIA)’ process that can be defined as the systematic identification and evaluation of the potential impacts (effects) of proposed projects, plans, programmes, policies or legislative actions relative to the biological physical, physico-chemical, biological, cultural, socio-economic and anthropological components of the total environment. The SEA process protocol is important as it has been proposed for studying and checking the productivity and quality of AIS. This treaty and official government procedures of SEA were helpful in the decision-making process much earlier than the EIA process.   Keywords: Artificial intelligence, business, economics, environment, industry.

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