Evaluation of Match Results of Five Successful Football Clubs With Ensemble Learning Algorithms

Filiz, Enes

doi:10.1080/02701367.2022.2053647

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…Grandes conjuntos de dados implicam em uma falha fora da memória. De acordo com Filiz, 23 eles ofereciam métodos líderes mundiais para educação em saúde mental, identidade setorial e treinamento físico. As estatísticas naïve bayesianas sustentam essa divisão de treinamento esportivo.…”

Section: Resultsunclassified

Educação De Saúde Dos Estudantes Universitários Baseado Em Aprendizado Profundo

Chai

2023

Rev Bras Med Esporte

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Introdução: A capacidade do aluno de reconhecer doenças mentais bem como sua capacidade de combater essas doenças lidando com dificuldades psicológicas pode ser aprimorada com uma compreensão global da saúde mental. Palestras e panfletos em papel mostraram-se pouco eficazes para esse aprendizado, surgindo a necessidade de melhorar os meios de educação em saúde pois essa educação aliada aos serviços de saúde mental pode melhorar a qualidade de vida dos alunos, a felicidade familiar e a sociedade em geral. Objetivo: Este estudo visa examinar a saúde física mental dos atletas universitários, com tratamento de dados num sistema de treinamento esportivo inteligente baseado em aprendizado profundo. Métodos: Os dados dos alunos foram coletados e divididos em duas categorias. O grupo de pesquisa recebeu aprendizagem profunda, enquanto o grupo de controle participou do treinamento atlético tradicional. Utilizou-se a Rede Neural Profunda Genética Agrupada para treinamento esportivo (CG-DNN). Os testes ANOVA e t de Student foram aplicados para avaliar a saúde mental dos alunos. Resultados: A atividade física proposta pode reduzir o estresse, regular o humor, promover a saúde mental, prevenir e tratar doenças mentais. Conclusão: O exercício físico desenvolvido com a tecnologia de aprendizado profundo é indicado para melhorar a saúde física e regular a saúde mental dos estudantes universitários. Nível de Evidência II; Estudos Terapêuticos - Investigando os Resultados do Tratamento.

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Section: Resultsunclassified

Educação De Saúde Dos Estudantes Universitários Baseado Em Aprendizado Profundo

Chai

2023

Rev Bras Med Esporte

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“…Large datasets imply an out-of-memory fault. According to Filiz, 23 they offered world-leading methods for mental health education, sector identity, and physical training. Naïve Bayesian statistics underpin this sports training divide.…”

Section: Student T-testmentioning

confidence: 99%

Health Education of University Students Based on Deep Learning

Chai

2023

Rev Bras Med Esporte

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Introduction: Students’ ability to recognize mental illness as well as their ability to combat it by dealing with psychological difficulties can be improved with a global understanding of mental health. Lectures and paper flyers have proved to be ineffective for this learning, giving rise to the need to improve the means of health education as this education combined with mental health services can improve students’ quality of life, family happiness and general society. Objective: This study aimed to examine the physical mental health of university athletes, with data processing in an intelligent sports training system based on deep learning. Methods: Student data were collected and divided into two categories. The research group received deep learning, while the control group participated in traditional athletic training. The Clustered Genetic Deep Neural Network for sports training (CG-DNN) was used. ANOVA and Student’s t-tests were applied to assess the students’ mental health. Results: The proposed physical activity can reduce stress, regulate mood, promote mental health, prevent and treat mental illness. Conclusion: Physical exercise developed with deep learning technology is indicated to improve the physical health and regulate the mental health of university students. Level of Evidence II; Therapeutic Studies - Investigating Treatment Outcomes.

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“…Common sources of inconsistencies between actual and predicted values in machine learning models include noise, variability, and bias [ 23 ]. Bagging, boosting, stacking, and voting, are among the notable approaches in this domain, offering improved predictive performance by combining the outputs of multiple base learners [ 24 , 25 ]. One of the most frequently used ensembled algorithms is voting [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data

Naderian,

Nikniaz,

Farhangi

et al. 2024

BMC Public Health

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Background Dyslipidemia, characterized by variations in plasma lipid profiles, poses a global health threat linked to millions of deaths annually. Objectives This study focuses on predicting dyslipidemia incidence using machine learning methods, addressing the crucial need for early identification and intervention. Methods The dataset, derived from the Lifestyle Promotion Project (LPP) in East Azerbaijan Province, Iran, undergoes a comprehensive preprocessing, merging, and null handling process. Target selection involves five distinct dyslipidemia-related variables. Normalization techniques and three feature selection algorithms are applied to enhance predictive modeling. Result The study results underscore the potential of different machine learning algorithms, specifically multi-layer perceptron neural network (MLP), in reaching higher performance metrics such as accuracy, F1 score, sensitivity and specificity, among other machine learning methods. Among other algorithms, Random Forest also showed remarkable accuracies and outperformed K-Nearest Neighbors (KNN) in metrics like precision, recall, and F1 score. The study’s emphasis on feature selection detected meaningful patterns among five target variables related to dyslipidemia, indicating fundamental shared unities among dyslipidemia-related factors. Features such as waist circumference, serum vitamin D, blood pressure, sex, age, diabetes, and physical activity related to dyslipidemia. Conclusion These results cooperatively highlight the complex nature of dyslipidemia and its connections with numerous factors, strengthening the importance of applying machine learning methods to understand and predict its incidence precisely.

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Evaluation of Match Results of Five Successful Football Clubs With Ensemble Learning Algorithms

Cited by 5 publications

References 33 publications

Educação De Saúde Dos Estudantes Universitários Baseado Em Aprendizado Profundo

Educação De Saúde Dos Estudantes Universitários Baseado Em Aprendizado Profundo

Health Education of University Students Based on Deep Learning

Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data

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