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 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 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

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 Machine Learning for Sustainable Energy Systems

2021 ◽  
Vol 46 (1) ◽  
Author(s):  
Priya L. Donti ◽  
J. Zico Kolter
Keyword(s):  
Machine Learning ◽  
Sustainable Energy ◽  
Energy Systems ◽  
Future Research ◽  
Annual Review ◽  
Publication Date ◽  
Research Directions ◽  
Sustainable Energy Systems ◽  
Future Research Directions ◽  
And Storage

In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy systems. We first provide a taxonomy of machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage. Finally, we identify gaps in this literature, propose future research directions, and discuss important considerations for deployment. Expected final online publication date for the Annual Review of Environment and Resources, Volume 46 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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 Challenges and Opportunities in Robotic Food Handling: A Review

2022 ◽  
Vol 8 ◽  
Author(s):  
Zhongkui Wang ◽  
Shinichi Hirai ◽  
Sadao Kawamura
Keyword(s):  
Low Cost ◽  
Food Products ◽  
Future Research ◽  
Food Handling ◽  
Research Directions ◽  
Fundamental Information ◽  
Food Recognition ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
End Effectors

Despite developments in robotics and automation technologies, several challenges need to be addressed to fulfill the high demand for automating various manufacturing processes in the food industry. In our opinion, these challenges can be classified as: the development of robotic end-effectors to cope with large variations of food products with high practicality and low cost, recognition of food products and materials in 3D scenario, better understanding of fundamental information of food products including food categorization and physical properties from the viewpoint of robotic handling. In this review, we first introduce the challenges in robotic food handling and then highlight the advances in robotic end-effectors, food recognition, and fundamental information of food products related to robotic food handling. Finally, future research directions and opportunities are discussed based on an analysis of the challenges and state-of-the-art developments.

scholarly journals A Heuristic Model for Predicting Human Fall Detection using Machine Learning Techniques

2020 ◽  
Vol 9 (2) ◽  
pp. 119-121
Keyword(s):  
Machine Learning ◽  
Health Problem ◽  
Fall Detection ◽  
Machine Learning Techniques ◽  
Technological Advancement ◽  
Heuristic Model ◽  
Human Beings ◽  
Machine Learning Classifiers ◽  
Learning Techniques ◽  
Smart Health

It is very obvious that human fall due to unconsciousness is a very common health problem in every human being. With the evolution of many smart health devices, we should contribute the technological advancement of machine learning into it. Different techniques are already used in order to detect human fall detection in human beings. In this paper we have studied the patterns of falling of human through the fall detection dataset while this human was performing various motions. By understanding all these we have generated the prediction protocol which estimates the fall of a person using fall detection dataset. Machine Learning classifiers were used to predict the human fall and a comparative study of various algorithms used was developed to find out the best classifier.

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