scholarly journals Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 495 ◽  
Author(s):  
Jesús López Belmonte ◽  
Adrián Segura-Robles ◽  
Antonio-José Moreno-Guerrero ◽  
María Elena Parra-González
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
English Language ◽  
Web Of Science ◽  
Large Data ◽  
Explanatory Models ◽  
Scientific Production ◽  
Scientific Publications ◽  
The One ◽  
The Impact

Combined use of machine learning and large data allows us to analyze data and find explanatory models that would not be possible with traditional techniques, which is basic within the principles of symmetry. The present study focuses on the analysis of the scientific production and performance of the Machine Learning and Big Data (MLBD) concepts. A bibliometric methodology of scientific mapping has been used, based on processes of estimation, quantification, analytical tracking, and evaluation of scientific research. A total of 4240 scientific publications from the Web of Science (WoS) have been analyzed. Our results show a constant and ascending evolution of the scientific production on MLBD, 2018 and 2019 being the most productive years. The productions are mainly in English language. The topics are variable in the different periods analyzed, where “machine-learning” is the one that shows the greatest bibliometric indicators, it is found in most of motor topics and is the one that offers the greatest line of continuity between the different periods. It can be concluded that research on MLBD is of interest and relevance to the scientific community, which focuses its studies on the branch of machine-learning.

scholarly journals Aplicação da Lei de Bradford a pesquisas relacionadas a Open Government

2020 ◽  
Vol 23 (1) ◽  
Author(s):  
Marcio Sembay ◽  
Adilson Luiz Pinto ◽  
Douglas Dyllon Jeronimo De Macedo ◽  
José Antonio Moreiro-González
Keyword(s):  
Web Of Science ◽  
Scientific Production ◽  
Open Government ◽  
Quantitative Character ◽  
Scientific Publications ◽  
International Context ◽  
Search Filter ◽  
Bradford’S Law ◽  
The Impact ◽  
The Web

Se ha buscado analizar la producción científica sobre el término Open Government en la base de datos Web of Science durante el período de 2010 a 2016, usando como filtro de búsqueda los títulos de los artículos en todas las lenguas y considerando la dispersión de la producción intelectual internacional. La investigación seleccionó las revistas más relevantes del área de ciencias sociales aplicadas, observando el impacto de esas revistas en esta área de conocimiento. La investigación es de carácter cuantitativo, pues se analizó la producción científica a través del acceso en línea a la base de datos Web of Science que alcanzó hasta 3.165 registros totales. Para hacer el análisis de la dispersión de la literatura afectada se aplicó la ley bibliométrica de Bradford. Se empleó como herramienta de hoja de cálculo para analizar los datos obtenidos en la minería, así como para tabularlos y tratarlos. El trabajo revela que, a lo largo de los años, las publicaciones muestran una aparición no lineal, registrándose un aumento significativo en la producción relativa al contexto internacional. De acuerdo con el núcleo de la ley de Bradford, se demuestra que el término Open Government muestra calidad en las publicaciones científicas en las que se difunde, pero que aún se encuentra en una fase de lucha por su espacio en las publicaciones científicas dentro de una ciencia actual en transformación. The objective of this study was to analyze the scientific production of the term Open Government in the Web of Science database from 2010 to 2016, using as search filter the titles of articles in all languages, considering the distribution of international intellectual productions. The research retrieved the most relevant journals in the area of applied social sciences, observing the impact of these journals to this area of knowledge. The research has a quantitative character, as it was analyzed the scientific production with online access of the Web of Science database covering a total of 3,165 documents. One of the main laws of bibliometrics was applied, Bradford's law for this analysis. If you used a spreadsheet tool for mining analysis, tabulation and data processing. The work reveals non-linearity in publications over the years recording a significant increase in production in an international context. It is concluded that the term Open Government has quality in scientific productions according to the core of the law of Bradford demonstrated in that study, however, the term is still gaining space in scientific publications nowadays in a society in transformation. Objetivou-se analisar a produção cientifica sobre o termo Open Government na base de dados Web of Science no período de 2010 a 2016, usando como filtro de busca os títulos dos artigos em todas as linguagens, considerando a distribuição das produções intelectuais internacionais. A pesquisa recuperou os periódicos mais relevantes da área de ciências sociais aplicadas, observando seu impacto para esta área de conhecimento. A pesquisa tem caráter quantitativo, pois analisou-se a produção cientifica com acesso online da base de dados Web of Scienceabrangendo um total de 3.165 registros. Aplicou-se uma das principais leis da bibliometria, a lei de Bradford para essa análise. Utilizou-se uma ferramenta de planilha para análise da mineração, tabulação e tratamento dos dados. O trabalho revela a não linearidade nas publicações ao longo dos anos registrando um aumento significativo na produção em um contexto internacional. Conclui-se que o termo Open Government tem qualidade em produções científicas conforme o núcleo da lei de Bradford demonstrada nesse estudo, porém, o termo ainda está ganhando espaço em publicações cientificas na atualidade em uma ciência em transformação.

scholarly journals Data Science Methods for Psychology

Psychology ◽  
2020 ◽  
Author(s):  
Jeffrey Stanton
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Data Science ◽  
Large Data ◽  
Large Data Sets ◽  
Predictive Analysis ◽  
Data Sets ◽  
The Impact

The term “data science” refers to an emerging field of research and practice that focuses on obtaining, processing, visualizing, analyzing, preserving, and re-using large collections of information. A related term, “big data,” has been used to refer to one of the important challenges faced by data scientists in many applied environments: the need to analyze large data sources, in certain cases using high-speed, real-time data analysis techniques. Data science encompasses much more than big data, however, as a result of many advancements in cognate fields such as computer science and statistics. Data science has also benefited from the widespread availability of inexpensive computing hardware—a development that has enabled “cloud-based” services for the storage and analysis of large data sets. The techniques and tools of data science have broad applicability in the sciences. Within the field of psychology, data science offers new opportunities for data collection and data analysis that have begun to streamline and augment efforts to investigate the brain and behavior. The tools of data science also enable new areas of research, such as computational neuroscience. As an example of the impact of data science, psychologists frequently use predictive analysis as an investigative tool to probe the relationships between a set of independent variables and one or more dependent variables. While predictive analysis has traditionally been accomplished with techniques such as multiple regression, recent developments in the area of machine learning have put new predictive tools in the hands of psychologists. These machine learning tools relax distributional assumptions and facilitate exploration of non-linear relationships among variables. These tools also enable the analysis of large data sets by opening options for parallel processing. In this article, a range of relevant areas from data science is reviewed for applicability to key research problems in psychology including large-scale data collection, exploratory data analysis, confirmatory data analysis, and visualization. This bibliography covers data mining, machine learning, deep learning, natural language processing, Bayesian data analysis, visualization, crowdsourcing, web scraping, open source software, application programming interfaces, and research resources such as journals and textbooks.

The impact of machine learning and big data on credit markets

2021 ◽  
Author(s):  
Peter Eccles ◽  
Paul Grout ◽  
Paolo Siciliani ◽  
Anna Zalewska
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Credit Markets ◽  
The Impact

Bibliometric Analysis of Scientific Production Related to Tick Control

2017 ◽  
Vol 5 (8) ◽  
pp. 20-28
Author(s):  
Luana Brito Oliveira ◽  
Suzana Leitão Russo
Keyword(s):  
Bibliometric Analysis ◽  
Web Of Science ◽  
Animal Health ◽  
Tick Control ◽  
Scientific Activity ◽  
Scientific Production ◽  
Health Concern ◽  
Public Health Concern ◽  
Scientific Publications ◽  
Great Relevance

Ticks are distributed all over the world and significantly affect human and animal health. Increasing public health concern with tick borne diseases requires the strategic control of ticks in animals that transmit diseases to humans. The aim of this article is to present a bibliometric analysis of the scientific production related to tick control, using bibliometrics as an instrument of analysis to measure scientific activity. To identify the studies , a search was made on four Scopus databases, Web of Science, Medline / Pubmed and Science Direct. Of 1764 publications, only 480 were analyzed after the exclusion of certain productions according to previously defined criteria. It was pointed out that the identified studies have great relevance for the control of ticks, considering that scientific publications are important markers of the activity of production and development of the field of knowledge.

scholarly journals Attributes Reduction in Big Data

2020 ◽  
Vol 10 (14) ◽  
pp. 4901
Author(s):  
Waleed Albattah ◽  
Rehan Ullah Khan ◽  
Khalil Khan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Processing ◽  
Data Analytics ◽  
Detection Efficiency ◽  
Large Data ◽  
Model Learning ◽  
Big Data Processing ◽  
Performance Loss ◽  
Points Of View

Processing big data requires serious computing resources. Because of this challenge, big data processing is an issue not only for algorithms but also for computing resources. This article analyzes a large amount of data from different points of view. One perspective is the processing of reduced collections of big data with less computing resources. Therefore, the study analyzed 40 GB data to test various strategies to reduce data processing. Thus, the goal is to reduce this data, but not to compromise on the detection and model learning in machine learning. Several alternatives were analyzed, and it is found that in many cases and types of settings, data can be reduced to some extent without compromising detection efficiency. Tests of 200 attributes showed that with a performance loss of only 4%, more than 80% of the data could be ignored. The results found in the study, thus provide useful insights into large data analytics.

scholarly journals Method for a cloud based remaining-service-life-prediction for vehicle-gearboxes based on big-data-analysis and machine learning

2020 ◽  
Vol 84 (4) ◽  
pp. 305-314
Author(s):  
Daniel Vietze ◽  
Michael Hein ◽  
Karsten Stahl
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Service Life ◽  
Operating Time ◽  
The Other ◽  
Learning Approaches ◽  
State Of Health ◽  
Remaining Service Life ◽  
Other Hand ◽  
The One

AbstractMost vehicle-gearboxes operating today are designed for a limited service-life. On the one hand, this creates significant potential for decreasing cost and mass as well as reduction of the carbon-footprint. On the other hand, this causes a rising risk of failure with increasing operating time of the machine. Especially if a failure can result in a high economic loss, this fact creates a conflict of goals. On the one hand, the machine should only be maintained or replaced when necessary and, on the other hand, the probability of a failure increases with longer operating times. Therefore, a method is desirable, making it possible to predict the remaining service-life and state of health with as little effort as possible.Centerpiece of gearboxes are the gears. A failure of these components usually causes the whole gearbox to fail. The fatigue life analysis deals with the dimensioning of gears according to the expected loads and the required service-life. Unfortunately, there is very little possibility to validate the technical design during operation, today. Hence, the goal of this paper is to present a method, enabling the prediction of the remaining-service-life and state-of-health of gears during operation. Within this method big-data and machine-learning approaches are used. The method is designed in a way, enabling an easy transfer to other machine elements and kinds of machinery.

A Method of Recommendation the Most Used XML Tags

2014 ◽  
Vol 931-932 ◽  
pp. 1353-1359
Author(s):  
Sutheetutt Vacharaskunee ◽  
Sarun Intakosum
Keyword(s):  
Big Data ◽  
Large Data ◽  
Problem Domain ◽  
Data Set ◽  
Xml Document ◽  
Extensible Markup ◽  
Standard Set ◽  
A New Technique ◽  
The One ◽  
Case Base Reasoning

Processing of a large data set which is known for today as big data processing is still a problem that has not yet a well-defined solution. The data can be both structured and unstructured. For the structured part, eXtensible Markup Language (XML) is a major tool that freely allows document owners to describe and organize their data using their markup tags. One major problem, however, behind this freedom lies in the big data retrieving process. The same or similar information that are described using the different tags or different structures may not be retrieved if the query statements contains different keywords to the one used in the markup tags. The best way to solve this problem is to specify a standard set of the markup tags for each problem domain. The creation of such a standard set if done manually requires a lot of hard work and is a time consuming process. In addition, it may be hard to define terms that are acceptable by all people. This research proposes a model for a new technique, XML Tag Recommendation (XTR) that aims to solve this problem. This technique applies the idea of Case Base Reasoning (CBR) by collecting the most used tags in each domain as a case. These tags come from the collection of related words in WordNet. The WordCount that is the web site to find the frequency of words is applied to choose the most used one. The input (problem) to the XTR system is an XML document contains the tags specified by the document owners. The solution is a set of the recommended tags, which is the most used tags, for the problem domain of the document. Document owners have a freedom to change or not change the tags in their documents and can provide feedback to the XTR system.

scholarly journals Pesquisa em psicologia do esporte e o cenário de periódicos científicos específicos

2019 ◽  
Vol 17 (2) ◽  
pp. 93-99
Author(s):  
Fábio Hech Dominski
Keyword(s):  
United States ◽  
English Language ◽  
Web Of Science ◽  
The United States ◽  
Google Scholar ◽  
Scientific Journals ◽  
Isi Web Of Science ◽  
Current Scenario ◽  
Publication Language ◽  
The Impact

Introdução: É notável o crescimento na produção de conhecimento na área da psicologia do esporte (PE). O conhecimento produzido pelos pesquisadores ocorre através da publicação de seus trabalhos no formato de artigos em periódicos científicos. Não existe na literatura análises considerando os periódicos específicos da área e suas características. Objetivo: Discutir acerca do cenário atual de periódicos específicos relacionados à PE. Métodos: Trata-se de uma pesquisa documental a respeito dos periódicos de PE. Foram extraídos e analisados dados como país, instituição, editora, língua de publicação, as métricas (JCR - ISI Web of Science, SJR, Citescore e SNIP – Scopus, e índice h5 – Google Scholar), periodicidade, período de publicações e número de artigos publicados em 2018. Resultados: Foram observados 14 periódicos na literatura relacionados diretamente a temática da PE. A maioria dos periódicos (5) é dos Estados Unidos, três da Espanha e três do Reino Unido. Brasil, Itália e Holanda apresentaram um periódico cada. A maioria dos periódicos publica na língua inglesa (13 dos 14). O fator de impacto (JCR) dos periódicos variou de 0,64 a 6,90, cinco periódicos não apresentaram essa métrica em 2018. Neste ano, os periódicos publicaram de 11 até 144 artigos. Conclusão: A partir da análise dos periódicos científicos específicos da PE, verificou-se que os de maior qualidade considerando as métricas analisadas, são dos Estados Unidos e da Europa. No Brasil ressalta-se a necessidade de fortalecimento do periódico específico existente na área, que pode ser realizado a partir da unificação das organizações que atuam na prática profissional e na pesquisa científica em PE. ABSTRACT. Sport psychology research and the specific journals scenario. Background: There is a remarkable growth in the production of knowledge in the field of sports psychology (SP). The knowledge produced by researchers occurs through the publication of their work in the format of articles in scientific journals. There are no analyses in the literature considering the specific journals of the area and their characteristics. Objective: To discuss about the current scenario of specific journals related to SP. Methods: This is a documentary research about the SP journals. The following data were extracted and analyzed: country, institution, publisher, publication language, metrics (JCR - ISI Web of Science, SJR, Citescore and SNIP – Scopus, and index h5 – Google Scholar), periodicity, publication period, and number of articles published in 2018. Results: It was observed 14 journals related to SP. Most of them are from United States, three from Spain and three from United Kingdom. Brazil, Italy and Netherlands showed one journal each. Most of the journals publish in English language (13 of 14). The impact factor ranged from 0.64 to 6.90, and five journals do not show this metric in 2018. In this year, the journals published from 11 to 144 articles. Conclusion: From the analysis of the specific scientific journals of the SP, it was found that the journals with highest quality are from the United States and Europe. In Brazil, there is a need to strengthen the existing specific journal in the area, which can be done by unifying the organizations that work in professional practice and scientific research of SP.

scholarly journals The Bibliometric Landscape of Infectious Disease Research in Panama (1990 - 2019): An Evaluation of the Country’s Capability on One Health

2020 ◽  
Author(s):  
Jose Loaiza ◽  
Robinson Zapata ◽  
Rao Kosagisharaf ◽  
Rolando A. Gittens ◽  
Enrique Mendoza ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
One Health ◽  
Scientific Publications ◽  
Disease Research ◽  
Infectious Disease Research ◽  
Health Concept ◽  
Health Related ◽  
The One ◽  
The Impact

Abstract Background: This work aims to analyze the landscape of scientific publications on subjects related to One Health and infectious diseases in Panama. We asked the following specific questions: How does the One Health research landscape look like in Panama? Are historical research efforts aligned with the One Health concept? What infectious diseases have received more attention from the local scientific community since 1990?Methods: Boolean searches on the Web of Science, SCOPUS and PubMed were undertaken to evaluate the main trends of publications related to One Health and infectious disease research in the country of Panama, between 1990 and 2019. Results: 4,547 publications were identified since 1990, including 3,564 peer-reviewed articles interconnected with One Health related descriptors, and 211 articles focused particularly on infectious diseases. There was a pattern of exponential growth in the number of publications with various contributions from Panamanian institutions. The rates of multidisciplinary, inter-institutional and inter-sectoral research ranged from moderate to low, to very low, respectively. Research efforts have centered largely on protozoan, neglected and arthropod-borne diseases with a strong emphasis on malaria, Chagas and leishmaniasis. Conclusion: Panama has scientific capabilities on One Health to tackle future infectious disease threats, but the official collaboration schemes and strategic investment to develop further competencies need to be considered. Through future collaborative efforts, Panama can reduce the risk of pandemics by developing surveillance strategies to improve the prediction of disease spillover, spread and persistence while helping to mitigate the impact on public health and the economy, regionally.

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