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 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>

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.

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.

scholarly journals The Demography of Socially Responsible Investors across Countries and Time: A Systematic Review

2018 ◽  
Vol 7 (2) ◽  
pp. 7-15
Author(s):  
Anjum Ubaid Siddiqui
Keyword(s):  
Financial Risk ◽  
Socially Responsible Investment ◽  
Future Research ◽  
Socially Responsible ◽  
Responsible Investment ◽  
The Past ◽  
Socially Responsible Investments ◽  
Gender Education ◽  
Future Research Directions ◽  
Socio Demographic Factors

The field of investment has received considerable changes in recent years. Over the past decade one of the major trends in the finance domain is the emergence of Socially Responsible Investments, reflecting the increasing awareness of investors to social and environmental and corporate governance issues. Over the past three decades many surveys have been conducted to understand and segment the demographic profile of socially responsible investors. This article aims to provide an overview of the existing literature on the demographic characteristics of socially responsible investors that has been studied across countries and time .We have limited our focus to insights generated by the articles specifically, dealing with relative influence of socio-demographic factors on the attitude towards Socially Responsible Investment, as opposed to broadening the scope of our inquiry to a larger set of studies related to the SRI such as fund performance, financial risk/return characteristics etc. The articles published have been classified into four broad demographic categories viz. Age, Gender, Education and Income on the basis of the importance of each category in context of Socially Responsible Investment. Finally the conclusion and future research directions are suggested which would be of importance for both academicians and investment world.

scholarly journals Carpooling in Connected and Autonomous Vehicles: Current Solutions and Future Directions

2022 ◽  
Author(s):  
Farkhanda Zafar ◽  
Hasan Ali Khattak ◽  
Moayad Aloqaily ◽  
Rasheed Hussain
Keyword(s):  
Business Model ◽  
Autonomous Vehicles ◽  
Research Work ◽  
Relevant Information ◽  
Security And Privacy ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Survey ◽  
One Stop ◽  
Future Research Directions

Owing to the advancements in communication and computation technologies, the dream of commercialized connected and autonomous cars is becoming a reality. However, among other challenges such as environmental pollution, cost, maintenance, security, and privacy, the ownership of vehicles (especially for Autonomous Vehicles (AV)) is the major obstacle in the realization of this technology at the commercial level. Furthermore, the business model of pay-as-you-go type services further attracts the consumer because there is no need for upfront investment. In this vein, the idea of car-sharing ( aka carpooling) is getting ground due to, at least in part, its simplicity, cost-effectiveness, and affordable choice of transportation. Carpooling systems are still in their infancy and face challenges such as scheduling, matching passengers interests, business model, security, privacy, and communication. To date, a plethora of research work has already been done covering different aspects of carpooling services (ranging from applications to communication and technologies); however, there is still a lack of a holistic, comprehensive survey that can be a one-stop-shop for the researchers in this area to, i) find all the relevant information, and ii) identify the future research directions. To fill these research challenges, this paper provides a comprehensive survey on carpooling in autonomous and connected vehicles and covers architecture, components, and solutions, including scheduling, matching, mobility, pricing models of carpooling. We also discuss the current challenges in carpooling and identify future research directions. This survey is aimed to spur further discussion among the research community for the effective realization of carpooling.

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.

A Survey of Recent Approaches Integrating Blogs in School Education

2021 ◽  
pp. 229-245
Author(s):  
Stavros Tsetsos ◽  
Jim Prentzas
Keyword(s):  
Secondary Education ◽  
Web 2.0 ◽  
Research Work ◽  
School Education ◽  
Future Research ◽  
Research Directions ◽  
Web 2.0 Tools ◽  
Primary And Secondary Education ◽  
Future Research Directions ◽  
Enhanced Learning

Web 2.0 tools are frequently integrated in education. The main goal of this integration is to provide enhanced learning experiences to students. Among other Web 2.0 tools, blogs are often used. Many approaches have been presented that successfully exploited blogs in all levels of education. An aspect of interest is to outline main directions of the corresponding research work that will provide insight to researchers, teachers, students, developers, and policymakers. This chapter provides a brief survey of approaches integrating blogs in primary and secondary education. Initially, main concepts regarding blogs as Web 2.0 tools and educational blogs are briefly discussed. Then, 16 approaches concerning the use of blogs in primary and secondary education are surveyed. The results derived from these approaches are analyzed. The analysis shows that the results are positive, and blogs turn out to be useful tools for school education. It is likely that more such approaches will be presented in the future. The chapter also outlines future research directions.

scholarly journals A Review of Facial Landmark Extraction in 2D Images and Videos Using Deep Learning

2019 ◽  
Vol 3 (1) ◽  
pp. 14 ◽  
Author(s):  
Matteo Bodini
Keyword(s):  
Deep Learning ◽  
Future Research ◽  
3D Face Reconstruction ◽  
Research Directions ◽  
Facial Expression Analysis ◽  
Facial Landmark ◽  
Learning Techniques ◽  
In The Wild ◽  
Future Research Directions ◽  
2D Images

The task of facial landmark extraction is fundamental in several applications which involve facial analysis, such as facial expression analysis, identity and face recognition, facial animation, and 3D face reconstruction. Taking into account the most recent advances resulting from deep-learning techniques, the performance of methods for facial landmark extraction have been substantially improved, even on in-the-wild datasets. Thus, this article presents an updated survey on facial landmark extraction on 2D images and video, focusing on methods that make use of deep-learning techniques. An analysis of many approaches comparing the performances is provided. In summary, an analysis of common datasets, challenges, and future research directions are provided.

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