Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Computational Intelligence and Data Mining in Sports 2021
share announcement

Computational Intelligence and Data Mining in Sports 2021

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 11538

Special Issue Editors

Dr. Iztok Fister

E-Mail Website
Guest Editor
Faculty of electrical engineering and computer science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Interests: computational intelligence; data mining; multi-agent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Iztok Fister Jr.

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Interests: computational social science; data mining; sport science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sport can be viewed from two standpoints: professional and recreational. The first standpoint is connected to the industrial capitalist society, where the only ideal is to win at all costs. Usually, this ideal leads professional athletes to excessive behavior, like dealing with drugs, betting scandals, or gambling. The second standpoint is brighter, because it is devoted to mass sports. Indeed, the biggest problem of modern society is sedentary lifestyle, which is reflected in obesity and loss of fitness. This trend is especially present in youth generations.

Sport has a huge potential to eliminate these negative effects of modern society. Being involved in sport typically also demands sacrifice from potential athletes. This not only concerns the time wasted in training, but it is also connected with the cost of hiring the sports facilities in team sports or sports trainers, particularly in individual sports. However, the last concern can be reduced with the development of modern technologies. Nowadays, mobile wearable devices (e.g., Garmin, Polar) enable information needed to analyze the performance achieved by athletes in training. On the other hand, new algorithms and methods in computational intelligence and data mining allow for an intelligent mode of evaluating the progress of athletes in all phases of sports training.

This Special Issue focuses on computational intelligence and data mining in sports. The aim of this Special Issue is to compile the latest achievements in this area and to open a forum where people from academia and the sport industry can find solutions to the arising problems in sport. Potential topics include, but are not limited to, the following:

  • Computational social science;
  • Data mining of sport activities;
  • Theory of sport training;
  • Automatic generation of sport training sessions;
  • Injury prevention;
  • Food prediction and planning;
  • Mobile and pervasive computing;
  • Computational intelligence theory and/or applications to sports;
  • Visualization of sport activities.

Dr. Iztok Fister
Dr. Iztok Fister Jr.
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational intelligence in sports
  • data mining in sports
  • wrist-wearable devices
  • visualization
  • swarm intelligence and evolutionary algorithms

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 2818 KiB  
Article
Sports Information Needs in Chinese Online Q&A Community: Topic Mining Based on BERT
by Chuanlin Ning, Jian Xu, Hao Gao, Xi Yang and Tianyi Wang
Appl. Sci. 2022, 12(9), 4784; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094784 - 09 May 2022
Cited by 2 | Viewed by 3623
Abstract
The online Question and Answering (Q&A) community has grown globally, allowing users to ask, discuss, and answer questions based on shared interests. As a gathering place for people’s knowledge production, collaboration, and dissemination in the current Internet scene, the online Q&A community can [...] Read more.
The online Question and Answering (Q&A) community has grown globally, allowing users to ask, discuss, and answer questions based on shared interests. As a gathering place for people’s knowledge production, collaboration, and dissemination in the current Internet scene, the online Q&A community can intuitively reflect the public’s information needs and behavior. It also collects many sports-related data and becomes an effective vehicle for comprehending mass sports information needs and disseminating sports knowledge. However, sports-related studies on the online Q&A community have rarely been reported. This study took the sports information in Zhihu, the largest Q&A community in China, as the research object to explore the public needs for sports information in China. We introduced the BERT model through a self-compiled python program and collected 391,092 sports-topic answers in the online Q&A community of Zhihu. Then, we explored the topic content, evolution trend, and user attributes of these answers. We found that the overall trend of sports information needs in Zhihu can be divided into three cycles: the London 2012 Olympic period, the Rio 2016 Olympic period, and the Tokyo 2020 Olympic period in general. The diversified content of information needs included 40 second-level themes and eight first-level themes. Male and female users had similarities and differences in sports information needs. The male and female users had the same information needs for fitness-related information. However, men were more concerned with confrontational solid sports such as basketball and football; women were more likely to care about weight loss, shape effect, and self-protection while doing sports activities. In addition, compared with men, women preferred to emphasize their gender attributes when expressing their needs for sports information to obtain more practical knowledge. In conclusion, our finding reveals that the sports community formed by the current online Q&A community in China is still a male-dominated information field. Full article
(This article belongs to the Special Issue Computational Intelligence and Data Mining in Sports 2021)
►▼ Show Figures

Figure 1

14 pages, 403 KiB  
Article
New Perspectives in the Development of the Artificial Sport Trainer
by Iztok Fister, Jr., Sancho Salcedo-Sanz, Andrés Iglesias, Dušan Fister, Akemi Gálvez and Iztok Fister
Appl. Sci. 2021, 11(23), 11452; https://0-doi-org.brum.beds.ac.uk/10.3390/app112311452 - 03 Dec 2021
Cited by 1 | Viewed by 2119
Abstract
The rapid development of computer science and telecommunications has brought new ways and practices to sport training. The artificial sport trainer, founded on computational intelligence algorithms, has gained momentum in the last years. However, artificial sport trainer usually suffers from a lack of [...] Read more.
The rapid development of computer science and telecommunications has brought new ways and practices to sport training. The artificial sport trainer, founded on computational intelligence algorithms, has gained momentum in the last years. However, artificial sport trainer usually suffers from a lack of automatisation in realization and control phases of the training. In this study, the Digital Twin is proposed as a framework for helping athletes, during realization of training sessions, to make the proper decisions in situations they encounter. The digital twin for artificial sport trainer is based on the cognitive model of humans. This concept has been applied to cycling, where a version of the system on a Raspberry Pi already exists. The results of porting the digital twin on the mentioned platform shows promising potential for its extension to other sport disciplines. Full article
(This article belongs to the Special Issue Computational Intelligence and Data Mining in Sports 2021)
►▼ Show Figures

Figure 1

22 pages, 3821 KiB  
Article
Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
by Yu-Chia Hsu
Appl. Sci. 2021, 11(14), 6594; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146594 - 18 Jul 2021
Cited by 8 | Viewed by 4953
Abstract
The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the [...] Read more.
The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction. Full article
(This article belongs to the Special Issue Computational Intelligence and Data Mining in Sports 2021)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop