scholarly journals Exploring machine learning: A bibliometric general approach using SciMAT

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1210
Author(s):  
Juan Rincon-Patino ◽  
Gustavo Ramirez-Gonzalez ◽  
Juan Carlos Corrales
Keyword(s):  
Machine Learning ◽  
Scientific Community ◽  
Scientific Work ◽  
Research Field ◽  
Classification Algorithms ◽  
Science Mapping ◽  
Future Developments ◽  
Regression Algorithms ◽  
Mapping Analysis ◽  
Machine Learning Applications

Background: Machine learning is becoming increasingly important for companies and the scientific community. In this study, we perform a bibliometric analysis on machine learning research, in order to provide an overview of the scientific work during the period 2007-2017 in this area and to show trends that could be the basis for future developments in the field. Methods: This study is carried out using the SciMAT tool based on results extracted from Scopus. This analysis shows the strategic diagrams of evolution and a set of thematic networks. The results provide information on broad tendencies of machine learning. Results: The results show that SciMAT is a useful tool to carry out a science mapping analysis, and emphasizes the premise that machine learning has boundless applications and will continue to be an interesting research field in the future. Conclusions: Some of the conclusions exposed in this study show that classification algorithms have been widely studied and represent a relevant tool for generating different machine learning applications. Nonetheless, regression algorithms are becoming increasingly important in the scientific community, allowing the generation of solutions to predict diseases, sales, and yields, for example.

scholarly journals Exploring machine learning: A bibliometric general approach using Citespace

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1240 ◽  
Author(s):  
Juan Rincon-Patino ◽  
Gustavo Ramirez-Gonzalez ◽  
Juan Carlos Corrales
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Web Of Science ◽  
Protein Molecule ◽  
Science Mapping ◽  
Mapping Analysis ◽  
Machine Learning Applications ◽  
The Usa ◽  
Learning Research ◽  
Near Future

Background: Machine learning researches algorithms that allow a machine to learn about resolving problems in different application domains. Due to the wide number of machine learning applications, it is necessary for newcomers to the field to have alternatives to explore this field faster. Methods: In this paper, we present a science mapping analysis on the machine learning research in the period 2007-2017. This study was develop using the CiteSpace tool based on results from Clarivate Web of Science. This analysis shows how the field has evolved, by highlighting the most notable authors, institutions, keywords, countries, categories, and journals. Results: The results provide information on trends and possibilities in the near future, particularly in areas such as health, biology and banking, where machine learning is a valuable tool to generate solutions. Conclusions: Machine learning is being widely studied, and several institutions in countries like the USA and China constantly generate machine learning based solutions. Diseases, such as cancer or Alzheimer’s disease, studies in biology, such as the protein molecule, virtual reality, commerce, smartphones, and ubiquitous computing, are all fields where machine learning contributes to resolving problems.

scholarly journals Opinion Mining and Sentiment Analysis in Marketing Communications: A Science Mapping Analysis in Web of Science (1998–2018)

2020 ◽  
Vol 9 (3) ◽  
pp. 23 ◽  
Author(s):  
Pablo Sánchez-Núñez ◽  
Carlos de las Heras-Pedrosa ◽  
José Ignacio Peláez
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Web Of Science ◽  
Scientific Work ◽  
Image Understanding ◽  
Marketing Communications ◽  
Science Mapping ◽  
Online Searching ◽  
Consumer Experiences ◽  
Mapping Analysis

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.

scholarly journals Menopause and Sustainable Career Outcomes: A Science Mapping Approach

2021 ◽  
Vol 18 (23) ◽  
pp. 12559
Author(s):  
Beatrice I. J. M. Van der Heijden ◽  
Karen Pak ◽  
Mónica Santana
Keyword(s):  
Postmenopausal Women ◽  
Older Workers ◽  
Web Of Science ◽  
Research Field ◽  
Menopausal Symptoms ◽  
Science Mapping ◽  
Women Health ◽  
The Third ◽  
Mapping Analysis ◽  
The Impact

This paper provides a systematic review of the phenomenon of menopause at the workplace from a sustainable career perspective, by highlighting its major themes along with the evolution and tendencies observed in this field. A conceptual science mapping analysis based on co-word bibliographic networks was developed, using the SciMAT tool. From 1992 to 2020, 185 documents were retrieved from the Web of Science. In the first analyzed time span (1992–2002), postmenopausal women, health, and risk factors appeared to be the motor themes (well-developed and important for the structure of the discipline under focus), and disorder was an emerging or disappearing theme in the phenomenon under research. In the second studied period (2003–2013), risk and health were motor themes, menopausal symptoms was a basic or transversal theme (important for the discipline but not well-developed), coronary heart disease was a specialized theme (well-developed but less important for the structure of the research field), and postmenopausal women was an emerging or disappearing theme (both weakly developed and marginal to the field). In the third studied period (2014–2020), menopause, breast cancer, and menopausal symptoms were motor themes, Anxiety was a specialized theme and risk and body mass index were emerging or disappearing themes. Sustainability of women’s careers in the second half of life is of increasing importance given the increasing equal representation of men and women in working organizations, and the impact of the changing nature of work in the 21st century on older workers.

scholarly journals A science mapping analysis of ‘Communication’ WoS subject category (1980-2013)

Comunicar ◽  
2018 ◽  
Vol 26 (55) ◽  
pp. 81-91 ◽  
Author(s):  
Julio Montero-Díaz ◽  
Manuel-Jesús Cobo ◽  
María Gutiérrez-Salcedo ◽  
Francisco Segado-Boj ◽  
Enrique Herrera-Viedma
Keyword(s):  
Science Communication ◽  
Web Of Science ◽  
Research Field ◽  
Communication Research ◽  
Science Mapping ◽  
Subject Category ◽  
Journal Citation Reports ◽  
Mapping Analysis ◽  
The Web ◽  
Journal Citation

Communication Research field has an extraordinary growth pattern, indeed bigger than other research fields. In order to extract knowledge from such amount, intelligent techniques are needed. In such a way, using bibliometric techniques, the evolution of the conceptual, social and intellectual aspects of this research field could be analysed, and hence, understood. Although the communication research field has been widely analysed using bibliometric techniques and science mapping tools, a conceptual analysis of the whole communication research field is still needed. Therefore, this article introduces the first science mapping analysis in the communication research field based on the Web of Science Subject Category "Communication," showing its conceptual structure and scientific evolution. SciMAT, a bibliometric science mapping software tool based on co-word analysis and h-index, is applied using a sample of 33.627 research documents from 1980 to 2013 published in 74 main communication journals indexed in the Journal Citation Reports of the Web of Science. The results show that research conducted in the communication research is concentrated on the following sixteen disconnected thematic areas: “children”, “psychological aspects”, “news”, “audience”, “surveys”, “advertising”, “health”, “relationship”, “gender”, “discourse”, “telephone communication”, “public relation”, “telecommunications”, “public opinion”, “activism” and “internet”. These areas have progressively disconnected among them, which drives to a Communication field relatively fragmented. El campo científico de la comunicación ha experimentado un enorme crecimiento a lo largo de los años, superando incluso a algunas áreas científicas consagradas. Mediante el uso de técnicas bibliométricas, podemos analizar la evolución conceptual, social e intelectual de esta área, así como comprenderla. En particular, el área de «Comunicación» ha sido ampliamente estudiada desde un punto de vista bibliométrico, pero no se ha realizado un análisis conceptual global del área englobado en un marco longitudinal. En este sentido, este artículo muestra el primer análisis de mapas científicos del área de investigación de la comunicación basándose en la Categoría de la Web of Science «Communication», centrándose en la estructura conceptual y cómo esta ha evolucionado. El estudio se ha realizado mediante la herramienta de análisis de mapas científicos SciMAT, basada en los mapas de co-palabras y en el índice-h. Un conjunto de 33.627 artículos científicos, publicados entre 1980 y 2013 en las 74 principales revistas del Journal Citation Reports de la Web of Science, han sido estudiados. Analizando los resultados, podemos destacar que la investigación llevada a cabo en el área de la comunicación se ha centrado en dieciséis áreas temáticas: «infancia», «aspectos psicológicos», «noticias», «audiencias», «sondeos», «publicidad», «salud», «relaciones», «género», «discurso», «comunicación telefónica», «relaciones públicas», «telecomunicaciones», «opinión pública», «activismo» e «Internet». Estas áreas se han desconectado entre ellas progresivamente, lo que conduce a un campo relativamente fragmentado.

scholarly journals Private Equity focused on Family Firms & Small and Medium Sized Companies: Review and Science Mapping Analysis of the Recent Scientific Field

2019 ◽  
Vol 9 (2) ◽  
pp. 146-158 ◽  
Author(s):  
Laura Arteche Bueno ◽  
Camilo Prado Román ◽  
Antonio Fernández Portillo
Keyword(s):  
Private Equity ◽  
Family Firms ◽  
Innovation Management ◽  
Research Field ◽  
Scientific Field ◽  
Science Mapping ◽  
Dominant Form ◽  
Mapping Analysis ◽  
Established Firms ◽  
Published Research

Private equity is mostly invested in established firms, of which family firms are the dominant form. This article reports the recent evolution of the scientific research on the PE focused on family firms and small and medium-sized enterprises. The purpose is to identify the main themes related to the field between 1992 and 2018 and to identify and analyze the major thematic areas throughout the period. The methodology applied is the science mapping analysis, which shows that: (i) published research on the field is concentrated in two main thematic areas: corporate governance-entrepreneurship and innovation-management, and; (ii) there has been an atomization of the research field during the last six years. Throughout this article, the authors develop a more complete understanding of the PE scientific field focused on family owned SMEs and provide suggestions for those looking for alternatives to traditional bank financing.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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