Online First

International Journal of Knowledge Content Development & Technology - Vol. 14 , No. 1
[Paper List]

[ Article ]
International Journal of Knowledge Content Development & Technology - Vol. 10, No. 4, pp. 29-46
ISSN: 2234-0068 (Print) 2287-187X (Online)
Print publication date 31 Dec 2020
Received 02 Mar 2020 Revised 01 Sep 2020 Accepted 04 Sep 2020
DOI: https://doi.org/10.5865/IJKCT.2020.10.4.029

Indian Research on Artificial Neural Networks: A Bibliometric Assessment of Publications Output during 1999-2018
B. M. Gupta* ; S. M. Dhawan**
*Ex-Chief Scientist, CSIR-NISTADS, New Delhi, India (bmgupta1@gmail.com)
**Ex-Sr Principal Scientist, CSIR-National Physical Laboratory, New Delhi, India (smdhawan@yahoo.com)

Abstract

The paper describes the quantitative and qualitative dimensions of artificial neural networks (ANN) in India in the global context. The study is based on research publications data (8260) as covered in the Scopus database during 1999-2018. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper, and contributed 9.77% share to the global ANN research. ANN research is skewed as the top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveals that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. ANN research applications across subjects were the largest in medical science and environmental science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36% to 9.12%), etc. The Indian Institute of Technology, Kharagpur and the Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, the Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals.

Keywords: Artificial Neural Networks, Machine Learning, Deep Learning, Indian Publications, Scientometrics, Bibliometrics
1. Introduction

Artificial neural networks (ANN) have been around for a long time as a subset of deep learning concepts (McCulloch & Pitts, 1943). Neural networks are designed to learn and make intelligent decisions on their own on complex tasks such as classification of images, pictures, and concepts and also address prediction problems (Lek & Park, 2008). Over time, networks have expanded in data processing capabilities and the ability to process more complex tasks. ANN algorithms simulate the processing concepts of the human brain to model complex patterns and predictions problems. ANN algorithms work to extract information from raw data and represent it in some type of model. The model so developed provides intelligence on how to infer things from other data not yet modeled. Put simply, artificial neural networks have emerged as a family of computational models that exhibit artificial intelligence effective at solving tasks such as pattern recognition, learning, pattern approximation, generalization, classification, and clustering. Neural networks have been applied in diverse fields including aerospace, automotive, banking, defense, electronics, entertainment, financial, insurance, manufacturing, medical, oil and gas, speech, securities, telecommunications, transportation, and environment (Elprocus). ANN computing as a subject is fast expanding in all its dimensions, like ANN concepts, types, applications, and models. During the last two decades, a lot of papers have been published in the domain of ANN computing. Given these developments, it is deemed appropriate and necessary that a bibliometric study is undertaken to evaluate ANN research in India in the global context and describe India’s performance in the subject in terms of its global ranking, and the institutions and researchers that are driving the research within the country.

The study is designed to examine qualitative and qualitative aspects of India’s overall research output in the area of artificial neural networks as indexed in the Scopus database during 1999-2018. The specific objectives of this study are: (i) Analyze global research in the subject in terms of publications growth and global share of top 10 most productive countries, (ii) Analyze ANN research in India in terms of publications growth, its distribution by document types, source publication types, broad subject areas, network type, and ANN application sectors, (iii) Analyze ANN research in India in terms of citation impact and describe bibliographic features of highly-cited papers, and (iv) Identify top 25 most productive organizations and authors and top 20 journals for research communications.

2. Literature Review

Bibliometric/scientometric studies related to the analysis of the “Artificial Neural Networks” research covering publications and patents are not available in large numbers in the literature on the subject, both at the national and international levels. Amongst the existing studies, Kumar (2016a) examined Artificial Neural Networks (ANNs) research in India during 1991-14 on measures like research growth, global publication share, activity index, relative citation impact, and impact factor. Kumar (2016b) also examined the conformity of Lotka’s law to authorship distribution in the field of Artificial Neural Networks research (ANNs) comprising 3411 articles and 5654 unique authors. Claude et al. (2004) studied the distribution of articles involving artificial neural networks in the fields of medicine and biology as indexed in ISI databases during 2000-2001 (1803 articles and 49 countries), with a focus on the parameters: the number of articles, the total impact factor, the ISI journal category, the source country population, and the gross domestic product.

However, in addition to bibliometric studies on ANN research per se, quite a few bibliometric studies are available on the neural network. Amongst these studies, Noyons and van Raan (1996, 1998) evaluated neural network research to identify productive countries and research institutions globally. Using bibliometric mapping technique they developed a methodology of “self-organized” structuring of scientific fields. Tijssen (1993) examined perceptions of scientific experts regarding the intellectual shape and contents (cognitive structure) of their scientific domain. Amsaveni (2016) tested the possible application of Bradford law to the Indian literature (2001-15) on neural networks, consisting of 5209 articles and 58249 citations. Ponnudurai and Priya (2011) analyzed the relative growth rate and doubling time of neural network research output at the international level using time series data from 1969 to 2007.

3. Methodology

In order to undertake a study of India’s contribution to the artificial neural network research, publications data was sourced from the Scopus database (http://www.scopus.com) covering a 20-year period 1999-2018. Single keyword “Artificial neural networks” was used in “Keyword tag” as well as in “Article Title tag” (joined by Boolean operator “OR”) simultaneously and restricted the output to period “1999-2018” in the “period tag”, to get global publication data (consisting of 84,509 records). The above-described search strategy was refined by country of publication (including India) to get publication output data on the top 10 countries. India’s publication output comprised of 8260 records. The search strategy for obtaining India’s output was further refined to get statistics on India’s output by subject, collaborating country, organization, author, and journal. Citations to publications were counted from the date of their publication till 20 November 2019. Separate search strategies were formulated to get data on various types of networks, tasks, and applications. A complete counting method, wherein every contributing author or organization covered in multiple authorship papers was fully counted and used. All authors or organizations of multi-authored papers have received equal credit in data counting and analysis. All types of publications have been used in this study.

( KEY ( “Artificial neural networks” ) OR TITLE ( “Artificial neural networks” ) ) AND PUBYEAR > 1998 AND PUBYEAR < 2019 AND ( LIMIT-TO ( AFFILCOUNTRY, “India” ) )

4. Results
4.1 Publication Growth

The global research output in the field of “Artificial Neural Networks” (ANN) in 20 years was 84,509 publications during 1999-2018, an average of 4225.4 publications per year. India accumulated 8,260 publications during the period, with an average of 413 publications per year. India registered 10.53% growth compared to 24.53% by the world. India’s absolute growth between 1999-2008 and 2008-2018 was 388.74% compared to 130.64% by the world. India contributed 9.77% share to global output in 20 years; its 10-year global publications share surged from 5.49% (1999-2008) to 11.63% (2009-2018). In the field of ANN research, India’s citation impact on a 20-year window averaged to 11.95 citations per paper (CPP), and its 10-year citation impact dropped from 25.99 CPP to 9.07 CPP during 1999-2008 to 2009-2018 (Table 1, Fig. 1).

Table 1. 
Annual and Cumulative Publications on “Artificial Neural Network Research” (ANN) in India during 1999-2018
Publication Year Global Publications
(TP)		 Indian Publications
TP %TP TC CPP ICP %ICP
1999 1477 31 2.10 1321 42.61 3 9.68
2000 1547 45 2.91 979 21.76 7 15.56
2001 1701 41 2.41 1404 34.24 7 17.07
2002 1905 91 4.78 2521 27.70 12 13.19
2003 2063 98 4.75 3099 31.62 17 17.35
2004 2474 123 4.97 5058 41.12 15 12.20
2005 2850 175 6.14 4489 25.65 25 14.29
2006 3332 220 6.60 5890 26.77 34 15.45
2007 3592 241 6.71 5188 21.53 30 12.45
2008 4609 338 7.33 6511 19.26 43 12.72
2009 5248 406 7.74 6907 17.01 51 12.56
2010 5362 474 8.84 8101 17.09 52 10.97
2011 5385 493 9.16 6462 13.11 52 10.55
2012 4736 525 11.09 6484 12.35 73 13.90
2013 4819 485 10.06 6220 12.82 55 11.34
2014 5378 667 12.40 6666 9.99 77 11.54
2015 5606 751 13.40 6792 9.04 73 9.72
2016 6389 1007 15.76 7159 7.11 113 11.22
2017 6865 813 11.84 4230 5.20 95 11.69
2018 9162 1236 13.49 3176 2.57 164 13.27
1999-2008 25559 1403 5.49 36469 25.99 193 13.76
2009-2018 58950 6857 11.63 62197 9.07 805 11.74
1999-2018 84509 8260 9.77 98666 11.95 998 12.08
TP=Total Papers; TC=Total Citations; CPP=Citations Per Paper; ICP=International Collaborative Papers


Fig. 1. 
Artificial Neural Networks Research Growth- Global Vs India 1999-2018

Of the total publications, 63.85% appeared as articles, 31.69% as conference papers, 2.08% as reviews, 1.33% as book chapters, and all others less than 1.0%.

4.2 Top 10 Countries in Artificial Neural Network

In all, 163 countries participated in global Artificial Neural Network (ANN) research. The distribution of ANN research across participating countries was uneven. For instance, 57 countries published 1-10 papers, 32 published 11-70 papers, 7 published 71-100 papers, 38 published 101-600 papers, 10 published 601-1000, 15 published 1001-5000 papers and 4 published 5001-16744 papers.

The bulk of the global research output (75.15%) in the field was contributed by the top 10 most productive countries alone. China and USA are in the leadership position in the world ranking, accounting for 19.81% and 14.79% global publications share respectively. India ranks third in the world ranking with 9.77% share. The global publication share of 7 other top 10 counties has been in single-digit ranging between 0.14% and 6.82% (Table 2, Fig. 2).

Table 2. 
Global Artificial Neural Network Research: Top 10 Most Productive Countries 1999-2018
S.No Name of the
Country		 Number of Papers Share of Papers
1999-2008 2009-2018 1999-2018 1999-2008 2009-2018 1999-2018
1 China 4392 12351 16743 17.18 20.95 19.81
2 USA 4852 7651 12503 18.98 12.98 14.79
3 India 1403 6857 8260 5.49 11.63 9.77
4 Iran 658 5103 5761 2.57 8.66 6.82
5 U.K. 1949 2846 4795 7.63 4.83 5.67
6 Turkey 896 2757 3653 3.51 4.68 4.32
7 Spain 938 2079 3017 3.67 3.53 3.57
8 Germany 1171 1842 3013 4.58 3.12 3.57
9 Canada 1072 1831 2903 4.19 3.11 3.44
10 Italy 989 1870 2859 3.87 3.17 3.38
Total 18320 45187 63507 71.68 76.65 75.15
World 25559 58950 84509


Fig. 2. 
Global ANN Research : Top 10 Most Productive Countries 1999-2018

4.2.1 India’s International Collaboration

During 1999-2018, India’s collaboration with 10 select countries was the largest, nearly 12% (998 papers) of its total output in ANN research (8260). Five of India’s collaborative countries have also been the world’s most productive countries - USA, UK, China, Iran, and Canada (Table 3, Fig. 2). India’s collaboration with the USA was the largest (25.55% of India’s ICP output), but with the other 9 of 10 collaborative countries, its international collaborative publications (ICP) share was in single-digit between 4.51% and 7.72%.

Table 3. 
India’ International Collaboration in ANN Research during 1999-2018
S.No. Collaborative
Country		 International Collaborative Papers (ICP) Share of ICP
1999-2008 2009-2018 1999-2018 1999-2008 2009-2018 1999-2018
1 USA 72 183 255 37.31 22.73 25.55
2 South Korea 11 66 77 5.70 8.20 7.72
3 U.K. 23 48 71 11.92 5.96 7.11
4 China 0 69 69 0.00 8.57 6.91
5 Malaysia 5 54 59 2.59 6.71 5.91
6 Iran 0 55 55 0.00 6.83 5.51
7 Saudi Arabia 0 54 54 0.00 6.71 5.41
8 Australia 0 53 53 0.00 6.58 5.31
9 Canada 11 36 47 5.70 4.47 4.71
10 Singapore 10 36 46 5.18 4.47 4.61
Total India’s
ICP output		 193 805 998 100.00 100.00 100.00

4.3 Subject-Wise Distribution of India’s Research Output

In all, Artificial Neural Network (ANN) research in India intersected with 14 disciplines (as identified in Scopus database classification). Of these, engineering and computer science have been the most favored subject areas in ANN research pursuits (with 45.67% and 41.79% national publications share respectively). In the other 12 disciplines, national publications share ranged between 3.04% and 10.87%.

Research activity index in all of 14 disciplines witnessed fluctuations between 1999-2008 and 2009-2018. Compared to the world average index of 100, seven disciplines registered a significant rise in their activity index, and in 5 other disciplines, it registered a significant decline. In two other areas, the decline in the activity index was marginal. Neuroscience recorded the highest citation impact per paper of 23.40 and mathematics the least (7.23) during 2008-19 (Table 4, Fig. 3).

Table 4. 
Subject-Wise Breakup of Indian Publications in Artificial Neural Network Research during 1999-2018
S.No Subject* Number of Papers (TP) Activity Index TC CPP %TP
1999-2008 2009-2018 1999-2018 1999-2008 2009-2018 1999-2018
1 Engineering 673 3099 3772 105.04 98.97 40661 10.78 45.67
2 Computer Science 465 2987 3452 79.31 104.23 37178 10.77 41.79
3 Environment Science 220 678 898 144.23 90.95 19776 22.02 10.87
4 Mathematics 130 757 887 86.29 102.81 6410 7.23 10.74
5 Materials Science 154 601 755 120.09 95.89 9966 13.20 9.14
6 Energy 95 602 697 80.24 104.04 9556 13.71 8.44
7 Earth & Planetary Sciences 133 527 660 118.64 96.19 8906 13.49 7.99
8 Physics & Astronomy 93 505 598 91.56 101.73 6458 10.80 7.24
9 Chemical Engineering 137 398 535 150.76 89.61 7437 13.90 6.48
10 Biochemistry, Genetics
& Molecular Biology		 94 433 527 105.01 98.97 7785 14.77 6.38
11 Medicine 60 449 509 69.40 106.26 7962 15.64 6.16
12 Agriculture & Biological
Sciences		 49 291 340 84.85 103.10 4650 13.68 4.12
13 Pharmacology, Toxicology
& Pharmaceutics		 21 249 270 45.79 111.09 2646 9.80 3.27
14 Neurosciences 52 199 251 121.97 95.50 5873 23.40 3.04
World Output 1403 6857 8260 98666 11.95
* There is overlapping of literature covered under various subjects
TP=Total Papers; TC=Total Citations; CPP=Citations Per Paper


Fig. 3. 
Global ANN Research : Activity Index Change between 1999-2008 and 2009-19

4.4 Distribution of Publications by Type of ANN Research

In all, ANN research can be classified into 10 types and these 10 types account for 19.46% share of total output by India in the subject. Of these, Feed Forward Neural Network type and Adaptive Neural Network type account for the predominant share (15.56%) of the national output in ANN research in the country. The other eight types account for a marginal 3.9% national publications share (Table 5). Nearly 10% of the national output of the country was devoted to ANN research by network architecture. Of this, Radial Basis Network type research accounted for 4.97% share and Multi-Layer Neural Network type research, 3.93% national share. Research in other network architecture type accounts for 2.22% national share (Table 6).

Table 5. 
Classification of India’s Publications on ANN by Type of Network during 1999-2018
S.No Type of ANN Number of Papers Share of Papers TC CPP
1999-2008 2009-2018 1999-2018 1999-2008 2009-2018 1999-2018 1999-2018
1 Feed Forward Neural Network 235 606 841 16.75 8.84 10.18 10280 12.22
2 Feed Backward Neural
Network		 54 156 210 3.85 2.28 2.54 2104 10.02
3 Convolution Neural Network 0 55 55 0.00 0.80 0.67 434 7.89
4 Modular Neural Network 3 6 9 0.21 0.09 0.11 136 15.11
5 Fixed Weight Neural Network 1 22 23 0.07 0.32 0.28 434 18.87
6 Adaptive Weight Neural
Network		 53 391 444 3.78 5.70 5.38 4777 10.76
7 Bayesian regularized Neural
Network		 2 16 18 0.14 0.23 0.22 340 18.89
8 Competitive Neural Network 2 5 7 0.14 0.07 0.08 114 16.29
Total 1403 6857 8260

Table 6. 
Classification of India’s Publications on ANN by Type of Network Architecture during 1999-2018
S.No. Classification by
Architecture Type		 Total Papers (TP) %TP TC CPP
1999-2008 2009-2018 1999-2018 1999-2008 2009-2018 1999-2018 1999-2018
1 Single Layer Neural
Network		 2 31 33 0.14 0.45 0.40 192 5.82
2 Radial Basis Network 47 341 388 3.35 4.97 4.70 4904 12.64
3 Multi-Layer Neural Network 31 294 325 2.21 4.29 3.93 3366 10.36
4 Recurrent Neural Network 13 100 113 0.93 1.46 1.37 2269 20.08
5 Hopfield Network 4 24 28 0.29 0.35 0.34 251 8.96
6 Boltzmann Machine 1 8 9 0.07 0.12 0.11 32 3.56
Total 1403 6857 8260

4.5 Distribution of ANN Publications by Applications and Task

In all, 74.41% of ANN research in India was devoted to ANN applications in 19 disciplines. Of these, ANN applications in medical science and environment science accounted for the largest 11.82% and 10.84% national share respectively during 1999-2018. ANN applications in the remaining disciplines ranged between 0.08% and 9.12% national share. Among application areas, marketing registered the highest citation impact per paper of 98.43, followed by hydrology (49.24), environment science (22.89), water resources (22.74), etc. (Table 7).

Table 7. 
Distribution of ANN Research by Applications during 1999-2018
S.No Application Area Number of Papers Share of Papers TC CPP
1999-2008 2009-2018 1999-2018 1999-2008 2009-2018 1999-2018 1999-2018
1 Medical Science 124 852 976 8.84 12.43 11.82 16618 17.03
2 Environment Science 219 676 895 15.61 9.86 10.84 20484 22.89
3 Materials Science 154 599 753 10.98 8.74 9.12 10327 13.71
4 Energy 95 601 696 6.77 8.76 8.43 9892 14.21
5 Chemical Engineering 137 402 539 9.76 5.86 6.53 7715 14.31
6 Water Resources 123 402 525 8.77 5.86 6.36 11940 22.74
7 Mining 15 301 316 1.07 4.39 3.83 3307 10.47
8 Management 45 248 293 3.21 3.62 3.55 4316 14.73
9 Geosciences 56 234 290 3.99 3.41 3.51 4465 15.40
10 Security 14 138 152 1.00 2.01 1.84 1817 11.95
11 Agriculture 19 117 136 1.35 1.71 1.65 673 4.95
12 Banking, Finance & Insurance 11 120 131 0.78 1.75 1.59 1400 10.69
13 Manufacuring 21 107 128 1.50 1.56 1.55 1039 8.12
14 Hydrology 43 70 113 3.06 1.02 1.37 5564 49.24
15 Transport 18 68 86 1.28 0.99 1.04 1402 16.30
16 Education 5 38 43 0.36 0.55 0.52 393 9.14
17 Construction 9 33 42 0.64 0.48 0.51 377 8.98
18 Policy 2 20 22 0.14 0.29 0.27 290 13.18
19 Marketing 4 3 7 0.29 0.04 0.08 689 98.43
1403 6857 8260 79282 9.60

Nearly 41% of ANN research in India accounts for task-specific applications: prediction (21.25%), classification (16.14$), and pattern recognition (4.27%). Prediction task accounted for the largest number of papers (255) in environmental science, followed by materials science (206), medical sciences (168 papers), energy (139 papers), chemical engineering (138 papers), etc. Pattern recognition task accounted for the largest number of papers (65) in medical sciences, followed by materials science (15), energy (14 papers), mining (13), security and chemical engineering (9 each), agriculture (8 papers), management (7 papers), etc. Classification type task accounted for the largest number of papers (238) in medical sciences, followed by energy (75 papers), mining (70 papers), materials science (62 papers), environment science (53 papers), chemical engineering (36 papers), etc. during 1999-2018 (Table 8).

Table 8. 
Classification of ANN by Task in Indian Artificial Neural Network LiIterature
S.No. Classification by
Task		 TC CPP
1999-2008 2009-2018 1999-2018 1999-2008 2009-2018 1999-2018 1999-2018
1 Prediction 290 1465 1755 20.67 21.37 21.25 26474 15.08
2 Pattern Rcognition 89 264 353 6.34 3.85 4.27 5780 16.37
3 Classification 210 1123 1333 14.97 16.38 16.14 15473 11.61
Total 1403 6857 8260

4.6 India’s Top 25 Most Productive Organizations

In all, 2194 organizations participated in Indian research on “Artificial Neural Network” during 1999-2018, of which 1243 organizations published 1 paper each, 543 organizations published 2-5 papers each, 251 organizations 6-10 papers each, 133 organizations 11-20 papers each and 74 organizations 21-50 papers, 50 organizations 51-289 papers each. The productivity of the top 25 most productive organizations varied from 69 to 289 publications per organization; together they contributed 41.50% (3428) Indian publications share and 54.77% (54041) Indian citations share during 1999-2018. Their HI has been above average citations per paper (Table 9, Fig. 4).

  • ∙ Nine organizations registered their publications output above their group average (137.12): Indian Institute of Technology, Kharagpur (289 papers), Indian Institute of Technology, Roorkee (264 papers), Anna University, Madras (237 papers), etc.;
  • ∙ Twelve organizations registered their citations per paper and relative citation index above the group average (15.76 and 1.32) of all organizations: Indian Institute of Technology, Kanpur (33.04 and 2.76), Indian Institute of Technology, Madras (24.26 and 2.03), Indian Institute of Technology, Bombay (22.94 and 1.92), Indian Statistical Institute, Kolkata (22.7 and 1.9), Indian Institute of Technology, Roorkee (22.41 and 1.88), Indian Institute of Science, Bangalore (21.91 and 1.83), etc.
Table 9. 
Scientometric Profile of Top 25 Most Productive Indian Organizations in Artificial Neural Networks during 1999-2018
S.No Name of the Organization TP TC CPP HI ICP %ICP RCI
1 Indian Institute of Technology, Kharagpur 289 6253 21.64 41 53 18.34 1.81
2 Indian Institute of Technology, Roorkee 264 5917 22.41 40 37 14.02 1.88
3 Anna University, Madras 237 2059 8.69 23 18 7.59 0.73
4 Vellore Institute of Technology 222 1301 5.86 20 30 13.51 0.49
5 Indian Institute of Technology, New Delhi 219 3440 15.71 30 20 9.13 1.31
6 Jadavpur University, Calcutta 204 3263 16.00 31 27 13.24 1.34
7 National Institute of Technology, Rourkela 189 3066 16.22 28 21 11.11 1.36
8 Indian Institute of Technology, Bombay 180 4129 22.94 36 26 14.44 1.92
9 Indian Institute of Technology, Madras 178 4319 24.26 34 26 14.61 2.03
10 Indian Institute of Science, Bangalore 129 2827 21.91 28 32 24.81 1.83
11 Indian Institute of Technology, Guwahati 117 2223 19.00 27 19 16.24 1.59
12 National Institute of Technology, Tiruchirappalli 111 1089 9.81 18 1 0.90 0.82
13 Indian Institute of Technology, Kanpur 107 3535 33.04 31 21 19.63 2.76
14 SHASTRA Deemed University 100 831 8.31 18 17 17.00 0.70
15 Sathyabama Institute of Science & Technology 95 203 2.14 7 2 2.11 0.18
16 University of Calcutta 91 943 10.36 17 10 10.99 0.87
17 Birla Institute of Technology & Science, Mesra 89 865 9.72 15 6 6.74 0.81
18 Indian School of Mines, Dhanbad 84 527 6.27 14 3 3.57 0.53
19 Birla Institute of Technology & Science, Pilani 82 1392 16.98 22 11 13.41 1.42
20 Shiksha o Anusandhan Deemed University 80 528 6.60 10 4 5.00 0.55
21 Amity University, Noida 78 479 6.14 11 8 10.26 0.51
22 Indian Statistical Institute, Kolkata 73 1657 22.70 23 17 23.29 1.90
23 Institute of Technology, BHU, Varanasi 71 1471 20.72 20 8 11.27 1.73
24 National Institute of Technology, Surathkal 70 1009 14.41 16 13 18.57 1.21
25 MN National Institute of Technology, Allahabad 69 715 10.36 15 4 5.80 0.87
Total of 25 organizations 3428 54041 15.76 23.00 434 12.66 1.32
Total of World 8260 98666 11.95
Share of top 25 organizations in India’s total output 41.50 54.77


Fig. 4. 
ANN Research in India : Top 25 Productive Organizations 1999-2018

4.7 India’s Top 25 Most Productive Authors

6546 authors participated in Indian research on “Artificial Neural Network” during 1999-2018, of which 4451 authors published 1 paper each, 1440 authors 2-5 papers each, 109 authors 6-10 papers each, 133 authors 11-20 papers each, 74 authors 21-50 papers each and 1 author 58 papers. The research productivity of the top 25 most productive authors varied from 19 to 58 publications per author. Together they contributed 8.17% (675) global publications share and 19.13% (18872) global citations share during 2009-2018. Their detailed scientometric profile is presented in Table 10.

  • ∙ Eight of the top 25 authors registered their publications output above the group average of 27.0: P. Samui (58 papers), T.N. Singh (42 papers), M.C. Deo (35 papers), G. Panda (35 papers), K.P. Sudheer(34 papers), R. Rakkiyappan and K.K. Sarma (32 papers each) and P. Chandra (30 papers);
  • ∙ Ten of the top 25 authors registered their citation per paper and relative citation index above the group average (27.96 and 2.34) of all authors: G.P.S.Raghava (86.21 and 7.21), K.P. Sudheer (84.88 and 7.1), A. Jain (77.0 and 6.44), R. Rakkiyappan (45.34 and 3.79), T.N. Singh (39.62 and 3.32), G. Panda (34.57 and 2.89), M. Khandelwal (34.08 and 2.85), M.C. Deo (32.47 and 2.72), S.S. Tampa (29.62 and 2.48) and N.R.Pal (29.0 and 2.43).
Table 10. 
Scientometric Profile of Top 25 Most Productive Indian Authors in Artificial Neural Network Research during 1999-2018
S.No Name of the
Author		 Affiliation of the Author TP TC CPP HI ICP %ICP RCI
1 P. Samui Indian Institute of Science, Bangalore 58 721 12.43 16 19 32.76 1.04
2 T.N. Singh Institute of Technology, BHU,
Varanasi		 42 1664 39.62 21 5 11.90 3.32
3 M.C. Deo Indian Institute of Technology, Bombay 38 1234 32.47 17 2 5.26 2.72
4 G. Panda National Institute of Technology, Rourkela 35 1210 34.57 14 3 8.57 2.89
5 K.P. Sudheer Indian Institute of Technology, Madras 34 2886 84.88 24 10 29.41 7.10
6 R. Rakkiyappan Bharathiar University 32 1451 45.34 24 22 68.75 3.79
7 K.K. Sarma Gauhati University 32 124 3.88 8 2 6.25 0.32
8 P. Chandra G.G.S. Indraprashta University, Delhi 30 230 7.67 8 0 0.00 0.64
9 A. Shukla ABV-IIIT & Management, Gwalior 26 227 8.73 10 1 3.85 0.73
10 A.Majumdar Indian Institute of Technology, New Delhi 25 330 13.20 10 2 8.00 1.10
11 C. Balaji Indian Institute of Technology, Madras 24 333 13.88 11 0 0.00 1.16
12 A.Jain Indian Institute of Technology, Kanpur 24 1848 77.00 13 5 20.83 6.44
13 M.Khandelwal M.P.University of Agriculture & Technology 24 818 34.08 14 13 54.17 2.85
14 R. Tiwari ABV-IIIT & Management, Gwalior 24 227 9.46 10 0 0.00 0.79
15 B.Majhi G.G..Vishwavidyale, Indore 23 23 145 6.30 8 1 4.35 0.53
16 S.Chattopadhyay Palian College of Management & Technology 22 385 17.50 11 1 4.55 1.46
17 S.K. Das National Institute of Technology, Rourkela 22 465 21.14 10 5 22.73 1.77
18 S.Dutta Indian Institute of Engineering Science & Technology, Shibpur 22 297 13.50 10 3 13.64 1.13
19 D.Devaraj Kalasalingam Academy of Research & Education 21 430 20.48 9 0 0.00 1.71
20 S.S.Tampa National Chemical Laboratory, Pune 21 622 29.62 11 2 9.52 2.48
21 V.Jothiprakash Indian Institute of Technology, Bombay 20 198 9.90 9 2 10.00 0.83
22 S.S.Mahapatra National Institute of Technology, Rourkela 19 471 24.79 12 0 0.00 2.07
23 N.R.Pal Indian Statistical Institute, Kolkata 19 551 29.00 12 6 31.58 2.43
24 H.Malik Netaji Subhash Institute of Technology, Delhi 19 367 19.32 10 0 0.00 1.62
25 G.P.S.Raghava Institute of Microbial Technology, Chandigarh 19 1638 86.21 18 1 5.26 7.21
Total 675 18872 27.96 12.8 105 15.56 2.34
Total of World 8260 98666 11.95
Share of 25
Authors in World
Total Output		 8.17 19.13
TP=Total Papers; TC=Total Citations; CPP=Citations Per Paper; HI=h-index; ICP=International Collaborative Papers; RCI=Relative Citation Index

4.8 Medium of Research Communication

Nearly 67.0% (5534) of ANN research in India appeared in 1053 journals, 25.91% (2140) in conference proceedings, 5.63% (465) in book series, 0.98% (81) as books, 0.47% (39) in trade publications and 0.01% (1) as undefined. Of the 1059 journals (reporting 5534 articles), 802 published 1-5 papers each, 165 published 6-10 papers each, 59 published 11-20 papers each, 29 published 21-50 papers each and 4 published 51-136 papers each during 1999-2018.

The top 20 most productive journals accounted for 16.77% of total Indian output in journals (covering artificial neural networks research) during 1999-2018, The 10-year output in journals increased from 14.43% to 17.33% between 1999-2008 and 2009-2018. Neurocomputing was the topmost productive journal (with 136 papers) in reporting Indian research in the field of ANN research, followed by the International Journal of Applied Engineering Research (106 papers), Applied Soft Computing (71 papers), Neural Computing & Applications (51 papers), etc. during 1999-2018 (Table 11).

Table 11. 
Top 20 Most Productive Journals in Artificial Neural Networks during 1999-2018
S.No Name of the Journal Number of Papers
1999-2008 2009-2018 1999-2018
1 Neurocomputing 31 105 136
2 International Journal of Applied Engineering Research 0 106 106
3 Applied Soft Computing 7 64 71
4 Neural Computing & Applications 1 50 51
5 Journal of Medical Systems 8 39 47
6 International Journal of Advanced Manufacturing Technology 18 28 46
7 Water Resources Management 15 29 44
8 Journal of Hydrological Engineering 22 19 41
9 Journal of Medical Imaging & Health 0 41 41
10 Expert Systems with Applications 2 34 36
11 Neural Networks 6 30 36
12 International Journal of Pharmacy and Technology 0 34 34
13 ARPN Journal of Engineering & Applied Science 0 33 33
14 Research Journal of Pharmacological Biological & Chemical Sciences 0 33 33
15 Indian Journal of Science & Technology 0 31 31
16 Biomedical Research India 0 29 29
17 International Journal of Biomedical Engineering & Technology 1 28 29
18 Journal of Hydrology 8 21 29
19 Journal of the Institute of Engineers INdia. Electrical Engineering Division 21 7 28
20 International Journal of Remote Sensing 15 12 27
Total of 20 journals 155 773 928
Total global journal output 1074 4460 5534
Share of top 20 journals in global journal output 14.43 17.33 16.77

4.9 Highly - Cited Papers

Of the total research output on “Artificial Neural Networks” in India (8260 publications), only 126 (1.53% share) accumulated 100 to 942 citations per paper (cumulative total 22170 citations) since their publication during 2009-2018, averaging to 175.95 citations per paper. The distribution of these 126 highly cited papers is skewed. One hundred nine papers accumulated citations in the range 100-199 per paper, 17 papers were in citation range 211-294, 8 papers in citation range 309-457, and 2 papers were in citation range 610-942.

  • ∙ Of the 126 highly cited papers, 54 resulted from the contribution by single organizations per paper (non-collaborative papers) and 72 from two or more organizations per paper (40 national collaborative and 32 international collaborative papers).
  • ∙ Among highly cited papers, USA collaborated in the largest number of papers (11 papers), followed by Singapore (4 papers), China and Iran (3 papers each), Australia, Japan, Malaysia, Portugal, Saudi Arabia and Turkey (2 papers each), Brazil, Canada, France, Greece, Iraq, Netherland, Nigeria, Norway, Serbia, South Africa, Taiwan, Turkey and Vietnam (1 paper each).
  • ∙ The 126 highly cited papers belonged to 388 authors and 235 organizations.
  • ∙ The leading organizations participating in highly-cited papers were: IIT-Roorkee (13 papers), IIT-Kanpur, IIT―Kharagpur and IIT-Chennai (9 papers each), IIT-Bombay (7 papers), IIT-Delhi (5 papers), NIT-Rourkela and IISc-Bangalore (4 papers each), Jadavpur University-Kolkata, IIT-Guwahati and NIT-Surathkal (3 papers each), BITS-Pilani, ISI-Kolkata and IIT-BHU-Varanasi (2 papers each), etc.
  • ∙ The leading authors participating in highly cited papers were: K.P.Sudheer (IIT-Madras)(8 papers), A.Jain (IIT-Kanpur)(7 papers), T.N.Singh (IIT-BHU)(4 papers), M.C.Deo(IIT-Bombay), G.Panda (NIT-Rourkela), M.Khandelwal (M.P. Univ of Agr & Tech), and G.P.S.Raghava (IMTECH-Chandigarh (3 papers each), etc.
  • ∙ Of the 126 highly cited papers, 109 were published as articles, 12 as review papers, 4 as conference papers and 1 as a short survey.
  • ∙ These 126 highly cited papers appeared across 90 journals, of which 6 papers were published in the Journal of Hydrology, 5 papers in Applied Soft Computing Journal, 4 papers each in Journal of Hydrological Engineering, IEEE Transactions on Neural Networks, Hydrological Processes and the Journal of Materials Processing Technology, 3 papers each in Biochemical Engineering Journal, Expert Systems & Applications, Neurocomputing and Renewable & Sustainable Energy Review, 2 papers each in Biomedical Signal Processing & Control, BMC Bioinformatics, Computer Methods & Program in Biomedicine, IEEE Transactions on Power Delivery, IEEE Transactions on Systems, Man & Cybernetics, Part B, International Journal of Rock Mechanics & Mining Sciences, Neural Networks, Ocean Engineering, Water Resources Management, Water Resources Research, Journal of Irrigation & Drainage Engineering, IEEE Transactions on Power Systems. Journal of Medical Systems and Renewable Energy and other journals 1 paper each.
5. Conclusion

This paper analyzes India’s research in the domain of “Artificial neural networks” (ANN) on select bibliometric indicators covering 20-year research as published during 1999-2019. During the period, ANN research by India registered a fast 24.52% average annual growth, contributed 9.77% share to global output, averaged citation impact of 11.95 citations per paper, and registered 126 papers (1.53% share of national output) as highly cited papers. In all, 163 countries contributed to global ANN research (84505 publications). The top 10 most productive countries in the world alone accounted for 75.15% bulk share to global publications output in the subject. China and the USA are in the leadership position in the world ranking, with 19.81% and 14.79% global publications share respectively. India ranks the third most productive country in the world with a 9.77% share. The global publication share of the other 7 amongst the top 10 counties has been in single digit ranging between 0.14% and 6.82%.

Engineering and computer science have been the preferred subjects in Artificial Neural Network (ANN) Research (with 45.67% and 41.79% publications share). Amongst the type of network in ANN research, Feed Forward Neural Network contributed the largest publication share (10.18%), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. during 1999-2018. In terms of research applications across subjects, medical science and environment science registered the highest publication share (11.82% and 10.84% share), followed by materials science, energy, chemical engineering, and water resources (from 6.36$ to 9.12%), etc. during the period.

The distribution of India research by participating organizations is skewed. The top 25 organizations (out of total 1243) contributed 41.50% publications share and 54.77% citations share respectively during the period. Indian Institute of Technology, Kharagpur (289 papers), Indian Institute of Technology, Roorkee (264 papers), Anna University, Madras (237 papers), have been the most productive research organizations in the country. The organizations leading in terms of citation per paper and relative citation index were: Indian Institute of Technology, Kanpur (33.04 and 2.76), Indian Institute of Technology, Madras (24.26 and 2.03), Indian Institute of Technology, Bombay (22.94 and 1.92), Indian Statistical Institute, Kolkata (22.7 and 1.9), etc. The distribution of India research by participating authors is highly scattered. The top 25 authors (out of total 6546) across India contributed merely 8.17% publications share and 19.13% citations share respectively during the period. Neurocomputing and International Journal of Applied Engineering Research are the top two most popular journals in the subject that published 136 and 106 papers respectively. India published the highest numbers of highly cited papers in collaboration with the USA (11 out of 126), followed by Singapore (4 papers), China and Iran (3 papers each), and others. In all, 126 highly-cited papers received a total of 22170 citations, averaging to 175.95 citations per paper.

Conclusion - The USA and China lead the global ranking in ANN research. India is the third most productive country in the world. Besides, India registered a fast 25% growth in its national output in the subject. India’s AAN research pursuits were multidisciplinary and exploratory in nature, aimed at developing innovative technologies, new ANN applications, and ANN types. The top 25 research organizations in the country contributed high-quality ANN research as their h-index have been consistently above the national average citations per paper. Indian Institutes of Technology at Kharagpur, Roorkee, and Anna University, Madras provided leadership in ANN research in the country. Computer science and engineering science were the most preferred areas in AAN research pursuits. But to reach the top position in the world ranking India will have to immensely improve its research productivity and also improve its research quality and impact.

References
1. Amsaveni, N. (2016). Assessment of Bradford Law’s of Scattering to Neural Network Literature through Bibliometric Study. International Journal of Next Generation Library and Technologies, 2(3), 1-15. http://www.ijnglt.com/files/v2i3/Amsaveni.pdf
2. Claude, R., Charles-Daniel, A., Jean, A., & Jean-Francois, G. (2004). Bibliometric Overview of the Utilization of Artificial Neural Networks in Medicine and Biology. Scientometrics, 59(1), 117-130.
3. Elprocus. (n.d.). Artificial Neural Networks (ANN) and Different Types. https://www.elprocus.com/artificial-neural-networks-ann-and-their-types/
4. Kumar, S. (2016a). Scientometric Study of Artificial Neural Networks Research in India. International Journal of Information Dissemination and Technology, 6(S1), S16-20. http://www.ijidt.com/index.php/ijidt/article/view/324
5. Kumar, S. (2016b). An Evaluation of Author Productivity in Artificial Neural Networks Research in India during 1991-2014. Annals of Library and Information Studies, 63, 126-131. http://hdl.handle.net/123456789/34790
6. Lek, S., & Park, Y. S. (2008). Artificial Neural Networks in Encyclopedia of Ecology. Amsterdam: Elsevier.
7. McCulloch, W. S., & Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5, 115-133.
8. Noyons, E. C. M., & van Raan, A. F. J. (1996). Actor Analysis in Neural Network Research: the Position of Germany. Research Evaluation, 6(2), 133-142.
9. Noyons, E. C. M., & van Raan, A. F. J. (1998). Monitoring Scientific Developments from a Dynamic Perspective: Self-organized Structuring to Map Neural Network Research. Journal of the American Society for Information Science January, 49(1), 68-81.
10. Ponnudurai, R., & Priya, N. (2011). Studies on Research Output of Neural Network at International level: Bibliometric analysis. International Journal of Current Research, 3(6), 229-233.
11. Tijssen, R. A. (1993). Scientometric Cognitive Study of Neural Network Research: Expert Mental Maps versus Bibliometric Maps. Scientometrics, 28(1), 111-136.
[ About the authors ]

B. M. Gupta superannuated from NISTADS, CSIR, India as Senior Principal Scientist in 2008 and later served as Emeritus Scientist in the institute 2009 to 2013. He holds B.Lib Sci , Associatship in Documentation and PhD degrees. He is Fullbright Professional Fellow in Library & Information Science (1999) and Fellow of the Society for Information Science (2007). He was the Principal Investigator for several sponsored research projects of Indian and foreign funding agencies. Dr Gupta had published more than 350 research papers in the area of scientometrics in national and international journals. He has been editorial board member and Guest Editor of select special issues of the serial “Scientometrics”, “DESIDOC Journal of Library & Information Technology” and “International Journal of Information Dissemination and Technology” and “Journal of the Indian Library Association.”

S. M. Dhawan superannuated in 2005 as Senior Principal Scientist from National Physical Laboratory, CSIR, New Delhi, India. He is Ph.D in Library Science from University of Delhi, MLIS from University of New York, USA, and M.Sc Physics from Sardar Patel University, India. His areas of expertise are bibliometrics, digital library services, and special libraries. He has worked for library transformation into digital era, successfully completed several bibliometric/scientometric research projects, and published research reports covering several areas of library science, library management systems. Post-superannuation he has published more than 70 research papers in various national and international journals in the area of bibliometrics/scientometrics.

 

Copyright ⓒ 2011
Research Institute for Knowledge Content Development & Technology (RIKCDT)
#217, K3, Konkuk University Glocal Campus, 268 Chunwondaero Chungju-si, Chungcheongbuk-do, 380-701. Korea
el : 082-43-840-3415   RIKCDT homepage :http://kcdt.kku.ac.kr/
Youngji Shin: ijkcdt@gmail.com



Creative Commons LicenseInternational Journal of Knowledge Content Development & Technology is licensed
under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.