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.

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 Global Research Studies on Electronic Journals A Scientometric Study during 1990 2017

2019 ◽  
Vol 39 (3) ◽  
pp. 116-124
Author(s):  
B M Gupta ◽  
S M Dhawan ◽  
Shankar Reddy Kolle
Keyword(s):  
Activity Index ◽  
Research Impact ◽  
Qualitative Description ◽  
Electronic Journals ◽  
Long Span ◽  
The Usa ◽  
The Subject ◽  
Scientometric Study ◽  
Highly Cited ◽  
Global Research

This paper presents a quantitative and qualitative description of global research in the field “electronic journals”. The study is based on global publications data (1747 publications) on the subject sourced from SCOPUS database covering the period 1990-2017. The study analyzes the data on a series of measures, like average annual growth, citations per paper, international collaborative papers, relative citation index, and activity index. Global research in the subject registered 18.46% growth, research impact of 5.28 citations per paper, and contributed barely 26 highly cited papers within a long span of 28 years. The USA has emerged as the world leader in electronic journals research accounting for 45.28% global publications share, followed by U.K. (12.18%), India (5.49%), etc. Top 20 organizations and authors in the subject contributed 16.58% and 11.15% global publications share respectively and 37.72% and 43.92% global citations share respectively during the period.

scholarly journals Machine Translation Research A Scientometric Assessment of Global Publications Output during 2007 16

2019 ◽  
Vol 39 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Brij Mohan Gupta ◽  
S.M. Dhawan
Keyword(s):  
Growth Rate ◽  
Machine Translation ◽  
Citation Impact ◽  
Qualitative Description ◽  
Bibliometric Indicators ◽  
Translation Research ◽  
Highly Cited Papers ◽  
Scopus Database ◽  
Scientometric Assessment ◽  
Highly Cited

The present study provides a quantitative and qualitative description of global machine translation research published during 2007-16 and as indexed in Scopus database. The study profiles research in the field on a series of measures, such as publications growth rate, global share, citation impact, share of international collaborative papers and distribution of publications by sub-areas. The study also profiles top contributing countries, organisations and authors in machine translation research on a series of bibliometric indicators. The study further reports characteristics of highly cited papers in the field.

scholarly journals 3D Printing: A Scientometric Assessment of Global Publications Output during 2007-16

2018 ◽  
Vol 38 (4) ◽  
pp. 238 ◽  
Author(s):  
Brij Mohan Gupta ◽  
Surinder Mohan Dhawan
Keyword(s):  
3D Printing ◽  
Three Dimensional ◽  
Qualitative Description ◽  
Research Teams ◽  
Industrial Manufacturing ◽  
Funding Mechanisms ◽  
Qualitative Performance ◽  
Scientometric Assessment ◽  
Insight Into ◽  
Highly Cited

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>The papers provides quantitative and qualitative description of three dimensional (3D) printing research based </span><span>on global publications output (7309 publications) in the field as indexed in Scopus database covering the 10-year </span><span>period 2007-2016. 3D printing research registered 54.61 per cent growth per annum and averaged 10.59 citations per </span><span>papers in 10 years, and bulk of global output (93.79 %) in the field emanates from just top 16 countries. The papers further provides an insight into qualitative performance of 3D printing research in terms of relative citation index, citations per papers, highly cited papers, top 25 global organisations and authors in the field, most favoured subjects in the field, and most favoured materials, technologies, and applications used in the field. The study concludes that 3D printing is the next revolution in industrial manufacturing led by USA and China. Asian and Pacific countries </span><span>should take initiatives to support 3D printing research through appropriate policy and funding mechanisms as well as encourage research teams to collaborate with leading international hubs in 3D printing research. </span></p></div></div></div>

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 A Scientometric Assessment of Global Publications in E Learning Research during 2003 to 18

2020 ◽  
Vol 40 (06) ◽  
pp. 382-389
Author(s):  
B M Gupta ◽  
SM Dhawan
Keyword(s):  
Qualitative Assessment ◽  
Prolific Author ◽  
Average Growth ◽  
The World ◽  
Publication Share ◽  
E Learning ◽  
The Subject ◽  
Learning Research ◽  
Scientometric Assessment ◽  
Leading Journals

The paper provides quantitative and qualitative assessment of global publications output in the domain of e-learning research (1809 publications). The data was sourced from Scopus database during 2003-18. The study finds that global e-learning research registered 18.92 per cent annual average growth, averaged 6.90 citations per paper in a 16-year window. The distribution of global research in the subject is highly skewed as 10 out of 94 participating countries account for 62.58 per cent global publications share. A total of 449 authors from 387 organisations contributed to global e-learning research. The top 15 organisations collectively contributed 14.81 per cent global publication share and 24.52 per cent global citation share respectively. The top 15 authors contributed 7.89 per cent global publication share and and 33.45 per cent global citation share respectively during the period. Carnegie Mellon University, USA (49 papers) is the most productive organisations in the world, National Cheng Kung University, Taiwan (23.29 and 3.37) is the most cited organisation. M. Vivou (24 papers) is most prolific author in the world and C.M. Chen (103.0 and 14.93) the most cited author in the subject. Computers and Education and Computers in Human Behavior (20 papers) were the leading journals publishing on this theme.

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 Energy Storage Ceramics: A Bibliometric Review of Literature

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3605
Author(s):  
Haiyan Hu ◽  
Aiping Liu ◽  
Yuehua Wan ◽  
Yuan Jing
Keyword(s):  
Energy Storage ◽  
Materials Science ◽  
Research Field ◽  
Highly Cited Papers ◽  
Research Areas ◽  
Productive Author ◽  
The Usa ◽  
Bibliometric Review ◽  
Leading Position ◽  
Highly Cited

Energy storage ceramics is among the most discussed topics in the field of energy research. A bibliometric analysis was carried out to evaluate energy storage ceramic publications between 2000 and 2020, based on the Web of Science (WOS) databases. This paper presents a detailed overview of energy storage ceramics research from aspects of document types, paper citations, h-indices, publish time, publications, institutions, countries/regions, research areas, highly cited papers, and keywords. A total of 3177 publications were identified after retrieval in WOS. The results show that China takes the leading position in this research field, followed by the USA and India. Xi An Jiao Tong Univ has the most publications, with the highest h-index. J.W. Zhai is the most productive author in energy storage ceramics research. Ceramics International, Journal of Materials Science-Materials in Electronics, and the Journal of Alloys and Compounds are the most productive journals in this field, and materials science—multidisciplinary is the most frequently used subject category. Keywords, highly cited papers, and the analysis of popular papers indicate that, in recent years, lead-free ceramics are prevalent, and researchers focus on fields such as the microstructure, thin films, and phase transition of ceramics.

Deep Learning for seasonality modelling in Inflation-Indexed Swap pricing

2021 ◽  
Vol 16 (3) ◽  
pp. 54-69
Author(s):  
Pier Giuseppe Giribone ◽  
◽  
Duccio Martelli ◽  
◽  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Consumer Price Index ◽  
Fair Value ◽  
Cash Flows ◽  
Future Trend ◽  
Research Field ◽  
Fixed Rate ◽  
Real Market ◽  
Finance Theory

An Inflation-Indexed Swap (IIS) is a derivative in which, at every payment date, the counterparties swap an inflation rate with a fixed rate. For the calculation of the Inflation Leg cash flows it is necessary to build a mathematical model suitable for the Consumer Price Index (CPI) projection. For this purpose, quants typically start by using market quotes for the Zero-Coupon swaps in order to derive the future trend of the inflation index, together with a seasonality model for capturing the typical periodical effects. In this study, we propose a forecasting model for inflation seasonality based on a Long Short Term Memory (LSTM) network: a deep learning methodology particularly useful for forecasting purposes. The CPI predictions are conducted using a FinTech paradigm, but in respect of the traditional quantitative finance theory developed in this research field. The paper is structured according to the following sections: the first two parts illustrate the pricing methodologies for the most popular IIS: the Zero Coupon Inflation-Indexed Swap (ZCIIS) and the Year-on-Year Inflation-Indexed Swap (YYIIS); section 3 deals with the traditional standard method for the forecast of CPI values (trend + seasonality), while section 4 describes the LSTM architecture, and section 5 focuses on CPI projections, also called inflation bootstrap. Then section 6 describes a robust check, implementing a traditional SARIMA model in order to improve the interpretation of the LSTM outputs; finally, section 7 concludes with a real market case, where the two methodologies are used for computing the fair-value for a YYIIS and the model risk is quantified.

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