scholarly journals The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review

2020 ◽  
Author(s):  
Rory Bunker ◽  
Teo Sunsjak
Keyword(s):  
Machine Learning ◽  
Team Sports ◽  
Team Sport ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
The Past ◽  
Learning Techniques ◽  
Future Research Directions ◽  
Research Questions

Over the past two decades, Machine Learning (ML) techniques have been increasingly utilized for the purpose of predicting outcomes in sport. In this paper, we provide a review of studies that have used ML for predicting results in team sport, covering studies from 1996 to 2019. We sought to answer five key research questions while extensively surveying papers in this field. This paper offers insights into which ML algorithms have tended to be used in this field, as well as those that are beginning to emerge with successful outcomes. Our research highlights defining characteristics of successful studies and identifies robust strategies for evaluating accuracy results in this application domain. Our study considers accuracies that have been achieved across different sports and explores the notion that outcomes of some team sports could be inherently more difficult to predict than others. Finally, our study uncovers common themes of future research directions across all surveyed papers, looking for gaps and opportunities, while proposing recommendations for future researchers in this domain.

Machine Learning Techniques to Identify and Characterize Sleep Disorders Using Biosignals

2021 ◽  
pp. 136-160
Author(s):  
Mercedes Barrachina ◽  
Laura Valenzuela López
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Adult Population ◽  
The United States ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
Learning Techniques ◽  
Control And Prevention ◽  
Future Research Directions

Sleep disorders are related to many different diseases, and they could have a significant impact in patients' health, causing an economic impact to the society and to the national health systems. In the United States, according to information from the Center for Disease Control and Prevention, those disorders are affecting 50-70 million in the adult population. Sleep disorders are causing annually around 40,000 deaths due to cardiovascular problems, and they cost the health system more than 16 billion. In other countries, such as in Spain, those disorders affect up to 48% of the adult population. The main objective of this chapter is to review and evaluate the different machine learning techniques utilized by researchers and medical professionals to identify, assess, and characterize sleep disorders. Moreover, some future research directions are proposed considering the evaluated area.

scholarly journals Machine learning for energy-water nexus: challenges and opportunities

2018 ◽  
Vol 2 (3) ◽  
pp. 228-267 ◽  
Author(s):  
Zaidi ◽  
Chandola ◽  
Allen ◽  
Sanyal ◽  
Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Systems ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Technological Advancement ◽  
Research Directions ◽  
Learning Techniques ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
System Sustainability

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. To this end, recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors. We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability, transport, and use to energy generation, fuel supply, and customer demand, and in the interdependencies among these systems that can leave these systems vulnerable to cascading impacts from single disruptions. In this paper, we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves. We then survey machine learning techniques that have found application to date in energy-water nexus problems. We conclude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.

Machine Learning

2012 ◽  
pp. 13-22 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning

2011 ◽  
pp. 2462-2468 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

scholarly journals A Brief Review of Responsible AI and Socially Responsible Investment in Financial and Stock Trading

2021 ◽  
Author(s):  
A. K. M. Amanat Ullah ◽  
Samiha Sultana ◽  
Fahim Faisal ◽  
Md. Muzahidul Islam Rahi ◽  
Md. Ashraful Alam ◽  
...  
Keyword(s):  
Ethical Decision Making ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Socially Responsible ◽  
Research Directions ◽  
Responsible Investment ◽  
Learning Techniques ◽  
Socially Responsible Investments ◽  
Future Research Directions

Automated trading is used in most of the major markets of our world. In order to ensure sustainable development, incorporating ethical and socially responsible ideas while designing these Artificial Intelligence (AI) systems has become a necessity. Both the industry and the academia are working towards Responsible AI, which can make Socially Responsible Investments (SRI). This paper reviews the research on SRI investment in the financial sector and evaluates these methods, which can help find future research directions in Computational Finance. This survey looks at the machine learning techniques used for ethical decision-making while stock or forex trading, which will benefit any further research work on Responsible AI in Finance.<br>

scholarly journals Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

2021 ◽  
Vol 54 (5) ◽  
pp. 1-36
Author(s):  
Ishai Rosenberg ◽  
Asaf Shabtai ◽  
Yuval Elovici ◽  
Lior Rokach
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
Adversarial Attack ◽  
The Impact

In recent years, machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this limits the application of machine learning, especially in non-stationary, adversarial environments, such as the cyber security domain, where actual adversaries (e.g., malware developers) exist. This article comprehensively summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques and illuminates the risks they pose. First, the adversarial attack methods are characterized based on their stage of occurrence, and the attacker’ s goals and capabilities. Then, we categorize the applications of adversarial attack and defense methods in the cyber security domain. Finally, we highlight some characteristics identified in recent research and discuss the impact of recent advancements in other adversarial learning domains on future research directions in the cyber security domain. To the best of our knowledge, this work is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain, map them in a unified taxonomy, and use the taxonomy to highlight future research directions.

scholarly journals A Brief Review of Responsible AI and Socially Responsible Investment in Financial and Stock Trading

2021 ◽  
Author(s):  
A. K. M. Amanat Ullah ◽  
Samiha Sultana ◽  
Fahim Faisal ◽  
Md. Muzahidul Islam Rahi ◽  
Md. Ashraful Alam ◽  
...  
Keyword(s):  
Ethical Decision Making ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Socially Responsible ◽  
Research Directions ◽  
Responsible Investment ◽  
Learning Techniques ◽  
Socially Responsible Investments ◽  
Future Research Directions

Automated trading is used in most of the major markets of our world. In order to ensure sustainable development, incorporating ethical and socially responsible ideas while designing these Artificial Intelligence (AI) systems has become a necessity. Both the industry and the academia are working towards Responsible AI, which can make Socially Responsible Investments (SRI). This paper reviews the research on SRI investment in the financial sector and evaluates these methods, which can help find future research directions in Computational Finance. This survey looks at the machine learning techniques used for ethical decision-making while stock or forex trading, which will benefit any further research work on Responsible AI in Finance.<br>

Understanding of social behaviour in human collective motions with non-trivial rule of control

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 84-86
Author(s):  
Keisuke Fujii
Keyword(s):  
Machine Learning ◽  
Team Sports ◽  
Machine Learning Techniques ◽  
Theoretical Modelling ◽  
Video Footage ◽  
Learning Techniques ◽  
Collective Motions ◽  
Tracking Movements ◽  
Or Team ◽  
Computational Ability

The coordination and movement of people in large crowds, during sports games or when socialising, seems readily explicable. Sometimes this occurs according to specific rules or instructions such as in a sport or game, at other times the motivations for movement may be more focused around an individual's needs or fears. Over the last decade, the computational ability to identify and track a given individual in video footage has increased. The conventional methods of how data is gathered and interpreted in biology rely on fitting statistical results to particular models or hypotheses. However, data from tracking movements in social groups or team sports are so complex as they cannot easily analyse the vast amounts of information and highly varied patterns. The author is an expert in human behaviour and machine learning who is based at the Graduate School of Informatics at Nagoya University. His challenge is to bridge the gap between rule-based theoretical modelling and data-driven modelling. He is employing machine learning techniques to attempt to solve this problem, as a visiting scientist in RIKEN Center for Advanced Intelligence Project.

scholarly journals A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7567
Author(s):  
Xiaochen Zhang ◽  
Lanxin Hui ◽  
Linchao Wei ◽  
Fuchuan Song ◽  
Fei Hu
Keyword(s):  
The Elderly ◽  
Research Field ◽  
Future Research ◽  
Human Machine Interaction ◽  
Research Directions ◽  
The Past ◽  
Power Wheelchairs ◽  
The Disabled ◽  
Future Research Directions ◽  
Machine Interaction

Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.

scholarly journals Approaches to the rationalization of surgical instrument trays: scoping review and research agenda

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bruno Miranda dos Santos ◽  
Flavio Sanson Fogliatto ◽  
Carolina Melecardi Zani ◽  
Fernanda Araujo Pimentel Peres
Keyword(s):  
Scoping Review ◽  
Surgical Instruments ◽  
Future Research ◽  
Healthcare Organizations ◽  
Research Directions ◽  
Lean Practices ◽  
Study Selection ◽  
Expert Analysis ◽  
Future Research Directions ◽  
Research Questions

Abstract Background Surgical Tray Rationalization (STR) consists of a systematic reduction in the number of surgical instruments to perform specific procedures without compromising patient safety while reducing losses in the sterilization and assembly of trays. STR is one example of initiatives to improve process performance that have been widely reported in industrial settings but only recently have gained popularity in healthcare organizations. Methods We conduct a scoping review of the literature to identify and map available evidence on surgical tray management. Five methodological stages are implemented and reported; they are: identifying research questions, identifying relevant studies, study selection, charting the data, and collating, summarizing and reporting the results. Results We reviewed forty-eight articles on STR, which were grouped according to their main proposed approaches: expert analysis, lean practices, and mathematical programming. We identify the most frequently used techniques within each approach and point to their potential contributions to operational and economic dimensions of STR. We also consolidate our findings, proposing a roadmap to STR with four generic steps (prepare, rationalize, implement, and consolidate) and recommended associated techniques. Conclusions To the best of our knowledge, ours is the first study that reviews and systematizes the existing literature on the subject of STR. Our study closes with the proposition of future research directions, which are presented as nine research questions associated with the four generic steps proposed in the STR roadmap.

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