Background The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sport performance and injury risk and to find out which AI techniques each sport has been using. Methods Systematic searches through the PubMed, Scopus, and Web of Science online databases were conducted for articles reporting AI techniques or methods applied to team sports athletes. Results Fifty-eight studies were included in the review with 11 AI techniques or methods being applied in 12 team sports. Pooled sample consisted of 6456 participants (97% male, 25 ± 8 years old; 3% female, 21 ± 10 years old) with 76% of them being professional athletes. The AI techniques or methods most frequently used were artificial neural networks, decision tree classifier, support vector machine, and Markov process with good performance metrics for all of them. Soccer, basketball, handball, and volleyball were the team sports with more applications of AI. Conclusions The results of this review suggest a prevalent application of AI methods in team sports based on the number of published studies. The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods. Electronic supplementary material The online version of this article (10.1186/s40798-019-0202-3) contains supplementary material, which is available to authorized users.
No abstract
Atualmente grandes volumes de dados são gerados e coletados por meio de sensores, dispositivos e redes sociais. A capacidade de lidar com grandes massas de dados tornou-se um importante fator para o sucesso de muitas organizações, exigindo, cada vez mais, a utilização de processamento paralelo e distribuído. Para auxiliar os desenvolvedores a projetar programas distribuídos, existem várias ferramentas (frameworks), como Apache Hadoop e Spark. Esses frameworks fornecem diversos parâmetros de configuração (por exemplo, o Hadoop tem mais de 200) e atribuir valores otimizados a todos eles não é uma tarefa simples. Este trabalho investiga a influência desses parâmetros no desempenho do Apache Hadoop, utilizando o algoritmo HEDA, um algoritmo iterativo que calcula métricas de centralidade em grandes grafos. A execução do HEDA em uma rede complexa é extremamente importante, pois existem várias medidas de centralidade que determinam a importância de um vértice dentro do grafo. Observou-se que, em alguns casos, a melhoria no tempo de execução atingiu aproximadamente 80% aplicando os valores propostos por este trabalho aos parâmetros de configuração do Hadoop. Além disso, foi possível aumentar em cinco vezes o uso dos processadores e melhorar consideravelmente a escalabilidade. O trabalho também apresenta os métodos aplicados para preparar, executar e analisar os experimentos, o que poderá auxiliar em novos estudos.
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