Global Machine-learning Research: a scientometric assessment of global literature during 2009–18

2020 ◽  
Vol 13 (2) ◽  
pp. 105-120
Author(s):  
S. M. Dhawan ◽  
◽  
B. M. , Gupta ◽  
N. K. Singh ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Citation Index ◽  
National Level ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Relative Citation ◽  
The Subject ◽  
Learning Research ◽  
International Collaborative

The article provides a quantitative and qualitative analyses of global machine-learning research output (48,455 publications), using select bibliometric indicators, using Web of Science database for 2009–18 period. The various indicators used in this study are: average annual growth, citations per paper, international collaborative papers, relative citation index, activity index, top-productive countries, organizations, authors, journals, and highly cited papers. Machine learning (within the domain of artificial intelligence) as a subject of study has fast-emerged as a subject of intensive research. It registered average annual growth rate of 27.59% and averaged citation impact of 10.78 per paper. Among 138 participating countries, the USA and China were in top 10 most productive countries on the subject. Among top 10 countries, France and Canada were the leading countries in terms of average citation per paper and relative index. France and Australia were leading in terms of for their national-level share to international collaborative publications (64.95% and 63.95%, respectively). In terms of type of machine learning, supervised learning registered the largest publications’ share, followed by deep learning, semi-supervised learning and reinforced learning (0.89% share, 556 papers). Centre National De La Recherche Scientique, France (769 papers), Harvard University, USA (751 papers) and University of London, UK (729 papers) were the three most productive global research organizations. In contrast, University of Toronto, Canada, Nanyang Technological University, Singapore and University of Oxford, UK were the three leading organizations in terms of citation per paper and relative citation index. Y. Zhang (246 papers), Y. Liu (204 papers) and J. Wang (203 papers) were leading in publication productivity in contrast to J. Li (12.52 and 1.03). L. Zhang (12.42 and 1.02) and J. Zhang (11.23 and 0.92) scored high in citation per paper and relative citation index on the subject. Neurocomputing (1310 papers), PLOS One (917 papers) and Expert Systems with Applications (861 papers) were the leading journals on this subject.

scholarly journals Academic Publication of Anesthesiology From a Bibliographic Perspective From 1999 to 2018: Comparative Analysis Using Subject-Field Dataset and Department Dataset

2021 ◽  
Vol 8 ◽  
Author(s):  
Sy-Yuan Chen ◽  
Ling-Fang Wei ◽  
Mu-Hsuan Huang ◽  
Chiu-Ming Ho
Keyword(s):  
Growth Rate ◽  
Science Citation Index ◽  
Citation Index ◽  
Science Citation ◽  
Average Annual Growth Rate ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Anesthesia Department ◽  
The Subject ◽  
Science Citation Index Expanded

Background: Publication activity in the field of anesthesiology informs decisions that enhance academic advancement. Most previous bibliometric studies on anesthesiology examined data limited to journals focused on anesthesiology rather than data answerable to authors in anesthesia departments. This study comprehensively explored publication trends in the field of anesthesiology and their impact. We hypothesized that anesthesiology's bibliometric scene would differ based on whether articles in the same study period were published in anesthesiology-focused journals or were produced by authors in anesthesia departments but published in non-specialty journals.Methods: This cross-sectional study used bibliometric data from the Science Citation Index Expanded database between 1999 and 2018. Two datasets were assembled. The first dataset was a subject-dataset (articles published in 31 journals in the anesthesiology category of InCites Journal Citation Reports in 2018); the second dataset was the department-dataset (articles published in the Science Citation Index Expanded by authors in anesthesia departments). We captured the bibliographical record of each article in both datasets and noted each article's Institute for Scientific Information code, publication year, title, abstract, author addresses, subject category, and references for further study.Results: A total of 69,593 articles were published—cited 1,497,932 times—in the subject-dataset; a total of 167,501 articles were published—cited 3,731,540 times—in the department-dataset. The results demonstrate differences between the two datasets. First, the number of articles was stagnant, with little growth (average annual growth rate = 0.31%) in the subject-dataset; whereas there was stable growth (average annual growth rate = 4.50%) in articles in the department-dataset. Second, only 30.4% of anesthesia department articles were published in anesthesiology journals. Third, journals related to “pain” had the lowest department-subject ratio, which was attributable to a large portion of non-anesthesia department researchers' participation in related research.Conclusions: This study showed that articles published in anesthesiology-focused and non-specialty journals demonstrate fundamentally different trends. Thus, it not only helps researchers develop a more comprehensive understanding of the current publication status and trends in anesthesiology, but also provides a basis for national academic organizations to frame relevant anesthesiology development policies and rationalize resource allocation.

scholarly journals Global Publications Output in Quantum Computing Research: A Scientometric Assessment during 2007-16

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
S. M. Dhawan ◽  
B.M. Gupta ◽  
Sudhanshu Bhusan
Keyword(s):  
Quantum Computing ◽  
Computer Science ◽  
Citation Index ◽  
National Level ◽  
The Usa ◽  
The Subject ◽  
Scopus Database ◽  
Using Data ◽  
Scientometric Assessment ◽  
Highly Cited

The paper maps quantum computing research on various publication and citation indicators, using data from Scopus database covering 10-year period 2007-16. Quantum computing research cumulated 4703 publications in 10 years, registered a slow 3.39% growth per annum, and averaged 14.30 citations per paper during the period. Top 10 countries dominate the field with 93.15% global publications share. The USA accounted for the highest 29.98% during the period. Australia tops in relative citation index (2.0).  International collaboration has been a major driver of research in the subject; 14.10% to 62.64% of national level output of top 10 countries appeared as international collaborative publications. Computer Science is one of the most popular areas of research in quantum computing research. The study identifies top 30 most productive organizations and authors, top 20 journals reporting quantum computing research, and 124 highly cited papers with 100+ citations per paper.

scholarly journals Deep Learning Research: Scientometric Assessment of Global Publications Output during 2004 -17

2019 ◽  
Vol 3 (1) ◽  
pp. 23 ◽  
Author(s):  
B. M. Gupta ◽  
S. M. Dhawan
Keyword(s):  
Deep Learning ◽  
National Level ◽  
High Growth ◽  
Research Field ◽  
Qualitative Description ◽  
The Subject ◽  
Learning Research ◽  
Scientometric Assessment ◽  
Productive Authors ◽  
Highly Cited

The paper provides a quantitative and qualitative description of deep learning research using bibliometric indicators covering global research publications published during 14-year period 2004-17. Global deep learning research registered 106.76% high growth per annum, and averaged 7.99 citations per paper. Top 10 countries world- over dominate the research field with their 99.74% global publications share and more than 100% global citations share. China ranks the top with the highest (29.25%) global publications share, followed by USA (26.46%), U.K. (6.40%), etc. during the period. Canada tops in relative citation index (5.30). International collaboration has been a major driver of research in the subject with 14.96% to 53.76% of national-level share of top 10 countries output appeared as international collaborative publications. Computer Science is one of the most popular areas of research in deep learning research (76.85% share). The study identifies top 50 most productive organizations and 50 most productive authors and top 20 most productive journals reporting deep learning research and 118 highly cited papers with 100+ citations per paper.

Predicting the citation and impact factor of terms for scientific publications using machine learning algorithms

2020 ◽  
Author(s):  
Aleksey Klokov ◽  
Evgenii Slobodyuk ◽  
Michael Charnine
Keyword(s):  
Machine Learning ◽  
Semantic Processing ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Scientific Publications ◽  
Text Data ◽  
Semantic Relationships ◽  
Subject Areas ◽  
The Subject ◽  
Scientific Environment

The object of the research when writing the work was the body of text data collected together with the scientific advisor and the algorithms for processing the natural language of analysis. The stream of hypotheses has been tested against computer science scientific publications through a series of simulation experiments described in this dissertation. The subject of the research is algorithms and the results of the algorithms, aimed at predicting promising topics and terms that appear in the course of time in the scientific environment. The result of this work is a set of machine learning models, with the help of which experiments were carried out to identify promising terms and semantic relationships in the text corpus. The resulting models can be used for semantic processing and analysis of other subject areas.

Ageing Saudi Population in the Facebook Era

2018 ◽  
Author(s):  
Asharaf Abdul Salam
Keyword(s):  
Growth Rate ◽  
Saudi Arabia ◽  
Population Ageing ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Saudi Population ◽  
Aged Population ◽  
Facebook Usage

<p>Data pertaining to 1974, 1992, 2004 and 2010 Censuses in Saudi Arabia was collected. Some reviews and literature on population ageing in Saudi Arabia as well as Facebook usage obtained. Statistics pertaining to Saudi population was utilized.</p> <p>Aged population in 2010 estimated by assuming the annual growth rate of 1974-2004.</p>

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

Classification and Recognition of Electronic Components Based on Improved Cooperative Semi-supervised Learning Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
Dan Luo
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Supervised Learning ◽  
Image Recognition ◽  
Production Efficiency ◽  
Learning Algorithm ◽  
Electronic Components ◽  
Electron Device ◽  
Actual Recognition ◽  
The Subject

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

scholarly journals Association between triglyceride-glucose index and risk of arterial stiffness: a cohort study

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Shouling Wu ◽  
Luli Xu ◽  
Mingyang Wu ◽  
Shuohua Chen ◽  
Youjie Wang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Linear Regression ◽  
Arterial Stiffness ◽  
Multivariate Linear Regression ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Cross Sectional ◽  
Tyg Index ◽  
Longitudinal Association ◽  
The Cross

Abstract Background Triglyceride–glucose (TyG) index, a simple surrogate marker of insulin resistance, has been reported to be associated with arterial stiffness. However, previous studies were limited by the cross-sectional design. The purpose of this study was to explore the longitudinal association between TyG index and progression of arterial stiffness. Methods A total of 6028 participants were derived from the Kailuan study. TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Arterial stiffness was measured using brachial-ankle pulse wave velocity (baPWV). Arterial stiffness progression was assessed by the annual growth rate of repeatedly measured baPWV. Multivariate linear regression models were used to estimate the cross-sectional association of TyG index with baPWV, and Cox proportional hazard models were used to investigate the longitudinal association between TyG index and the risk of arterial stiffness. Results Multivariate linear regression analyses showed that each one unit increase in the TyG index was associated with a 39 cm/s increment (95%CI, 29–48 cm/s, P < 0.001) in baseline baPWV and a 0.29 percent/year increment (95%CI, 0.17–0.42 percent/year, P < 0.001) in the annual growth rate of baPWV. During 26,839 person-years of follow-up, there were 883 incident cases with arterial stiffness. Participants in the highest quartile of TyG index had a 58% higher risk of arterial stiffness (HR, 1.58; 95%CI, 1.25–2.01, P < 0.001), as compared with those in the lowest quartile of TyG index. Additionally, restricted cubic spline analysis showed a significant dose–response relationship between TyG index and the risk of arterial stiffness (P non-linearity = 0.005). Conclusion Participants with a higher TyG index were more likely to have a higher risk of arterial stiffness. Subjects with a higher TyG index should be aware of the following risk of arterial stiffness progression, so as to establish lifestyle changes at an early stage.

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