Underwater Acoustic Research Trends with Machine Learning: General Background

PurposeThe purpose of this article is to track the emergence of topics and research trends in environmental accounting research by using a machine learning method for literature reviews. The article shows how the method can track the emergence of topics and research trends over time.Design/methodology/approachThe analysis of the emergence of topics and shifts in research trends was based on a machine learning approach that allowed the authors to identify “topic bursts” in publication data. The data set of this study contained, 2,502 records published between 1972 and 2019, both within and outside of accounting journals. The data set was assembled through a systematic keyword search of the literature.FindingsFindings indicated that research studies within accounting journals have addressed sustainability concerns in a general fashion, with a recent focus on broad topics such as corporate social responsibility (CSR) and stakeholder theory. Research studies published outside of accounting journals have focussed on more specific topics (e.g. the shift to a low-carbon or circular economy, the attainment of the sustainable development goals [SDGs], etc.) and new methodologies (e.g. accounting for ecosystem services).Research limitations/implicationsThe method provides an approach for identifying “trending” topics within accounting and non-accounting journals and allows to identify topics and areas that could benefit from a greater exchange of ideas between accounting and non-accounting journals.Originality/valueThe authors provide a much needed review of research on the vitally important topic of environmental accounting not only in accounting journals but also in the broader research community.

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The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks

Advances in Wireless Technologies and Telecommunication - Next-Generation Wireless Networks Meet Advanced Machine Learning Applications ◽

10.4018/978-1-5225-7458-3.ch001 ◽

2019 ◽

pp. 1-23

Author(s):

Paulo Valente Klaine ◽

Oluwakayode Onireti ◽

Richard Demo Souza ◽

Muhammad Ali Imran

Keyword(s):

Machine Learning ◽

Cellular Networks ◽

Research Trends ◽

Future Research ◽

Wireless Cellular Networks ◽

Intelligent Algorithms ◽

Current State ◽

Future Networks ◽

Applications Of Machine Learning ◽

Self Organizing

In this chapter, a brief overview of the role and applications of machine learning (ML) algorithms in future wireless cellular networks is presented, more specifically, in the context of self-organizing networks (SONs). SON is a promising and innovative concept, in which future networks are expected to analyze and use historical data in order to improve and adapt themselves to the network conditions. For this to be possible, however, algorithms that are capable of extracting patterns from data and learn from previous actions are necessary. This chapter highlights the utilization and possible applications of ML algorithms in future cellular networks. A brief introduction of ML and SON is presented, followed by an analysis of current state of the art solutions involving ML in SON. Lastly, guidelines on the utilization of intelligent algorithms in SON and future research trends in the area are highlighted and conclusions are drawn.

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A Survey of Big Data Analytics Using Machine Learning Algorithms

Advances in Human and Social Aspects of Technology - HCI Challenges and Privacy Preservation in Big Data Security ◽

10.4018/978-1-5225-2863-0.ch005 ◽

2018 ◽

pp. 95-123 ◽

Cited By ~ 2

Author(s):

Usha Moorthy ◽

Usha Devi Gandhi

Keyword(s):

Machine Learning ◽

Big Data ◽

Deep Learning ◽

Information Management ◽

Big Data Analytics ◽

High Volume ◽

Information Management System ◽

Research Trends ◽

Machine Learning Algorithms ◽

Security Issues

Big data is information management system through the integration of various traditional data techniques. Big data usually contains high volume of personal and authenticated information which makes privacy as a major concern. To provide security and effective processing of collected data various techniques are evolved. Machine Learning (ML) is considered as one of the data technology which handles one of the central and hidden parts of collected data. Same like ML algorithm Deep Learning (DL) algorithm learn program automatically from the data it is considered to enhance the performance and security of the collected massive data. This paper reviewed security issues in big data and evaluated the performance of ML and DL in a critical environment. At first, this paper reviewed about the ML and DL algorithm. Next, the study focuses towards issues and challenges of ML and their remedies. Following, the study continues to investigate DL concepts in big data. At last, the study figures out methods adopted in recent research trends and conclude with a future scope.

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Recent Research Trends of Machine Learning-Based Radar Techniques

The Journal of Korean Institute of Communications and Information Sciences ◽

10.7840/kics.2018.43.8.1382 ◽

2018 ◽

Vol 43 (8) ◽

pp. 1382-1385

Author(s):

Dong-Hwan Kim ◽

Bang Chul Jung ◽

Choul-Young Kim ◽

Sang-Woon Jeon ◽

Jin-Woong Kim

Keyword(s):

Machine Learning ◽

Research Trends

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