Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 183

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988–2018)

Shukla, Amit K., Janmaijaya, Manvendra, Abraham, Ajith, Muhuri, Pranab K.·2019·Engineering Applications of Artificial Intelligence
DOI 10.1016/j.engappai.2019.06.010
Open this paper in CoChat — free account

Abstract

Abstract The Engineering Applications of Artificial Intelligence (EAAI) is a journal of very high repute in the domain of Engineering and Computer Science. This paper gives a broad view of the publications in EAAI from 1988–2018, which are indexed in Web of Science (WoS) and Scopus. The main purpose of this research is to bring forward the prime impelling factors that bring about the EAAI publications and its citation structure. The publication and citation structure of EAAI is analyzed, which includes the distribution of publication over the years, citations per year and a bird’s eye view of the citation structure. Then the co-citation analysis and over the year’s trend of top keywords is given. The co-authorship networks and a geographic analysis of the sources is also provided. Further, a country-wise temporal and quantitative analysis of the publications is given along with the highly cited documents among the EAAI publications.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.engappai.2019.06.010

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Research works printed but not linked: 68

  1. Alvarez-Betancourt, 2014, An overview of iris recognition: A bibliometric analysis of the period 2000–2012, Scientometrics, 101, 2003, 10.1007/s11192-014-1336-1 · DOI
  2. Ballé, 2000, Closed-loop fault diagnosis based on a nonlinear process model and automatic fuzzy rule generation, Eng. Appl. Artif. Intell., 13, 695, 10.1016/S0952-1976(00)00049-X · doi:10.1016/s0952-1976(00)00049-x
  3. Bashyal, 2008, Recognition of facial expressions using Gabor wavelets and learning vector quantization, Eng. Appl. Artif. Intell., 21, 1056, 10.1016/j.engappai.2007.11.010 · DOI · Paper page
  4. Bateni, 2007, Neural network and neuro-fuzzy assessments for scour depth around bridge piers, Eng. Appl. Artif. Intell., 20, 401, 10.1016/j.engappai.2006.06.012 · doi:10.1016/j.engappai.2006.06.012
  5. Bauer, 2005, UML 2.0 and agents: how to build agent-based systems with the new UML standard, Eng. Appl. Artif. Intell., 18, 141, 10.1016/j.engappai.2004.11.016 · doi:10.1016/j.engappai.2004.11.016
  6. Benardos, 2007, Optimizing feedforward artificial neural network architecture, Eng. Appl. Artif. Intell., 20, 365, 10.1016/j.engappai.2006.06.005 · DOI · Paper page
  7. Bhatta, 1996, Model-based design indexing and index learning in engineering design, Eng. Appl. Artif. Intell., 9, 601, 10.1016/S0952-1976(96)00055-3 · DOI
  8. Camacho, 2006, Roboskeleton: An architecture for coordinating robot soccer agents, Eng. Appl. Artif. Intell., 19, 179, 10.1016/j.engappai.2005.07.002 · doi:10.1016/j.engappai.2005.07.002
  9. Chau, 2004, Hybrid knowledge representation in a blackboard KBS for liquid retaining structure design, Eng. Appl. Artif. Intell., 17, 11, 10.1016/j.engappai.2003.11.007 · doi:10.1016/j.engappai.2003.11.007
  10. Chen, 2015, A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model, Eng. Appl. Artif. Intell., 46, 258, 10.1016/j.engappai.2015.09.010 · DOI · Paper page
  11. Chen, 2015, Highly cited articles in biomass research: A bibliometric analysis, Renew. Sustain. Energy Rev., 49, 12, 10.1016/j.rser.2015.04.060 · DOI
  12. Chen, 2008, The thematic and citation landscape of data and knowledge engineering (1985–2007), Data Knowl. Eng., 67, 234, 10.1016/j.datak.2008.05.004 · DOI · Paper page
  13. Cheng, 2003, An integrated multi-criteria decision analysis and inexact mixed integer linear programming approach for solid waste management, Eng. Appl. Artif. Intell., 16, 543, 10.1016/S0952-1976(03)00069-1 · doi:10.1016/s0952-1976(03)00069-1
  14. Cobo, 2015, 25 years at knowledge-based systems: a bibliometric analysis, Knowl.-Based Syst., 80, 3, 10.1016/j.knosys.2014.12.035 · DOI · Paper page
  15. Cook, 2000, Combining a neural network with a genetic algorithm for process parameter optimization, Eng. Appl. Artif. Intell., 13, 391, 10.1016/S0952-1976(00)00021-X · DOI
  16. Das, 2012, A novel fractional order fuzzy PID controller and its optimal time domain tuning based on integral performance indices, Eng. Appl. Artif. Intell., 25, 430, 10.1016/j.engappai.2011.10.004 · doi:10.1016/j.engappai.2011.10.004
  17. De Silva, 2012, State of the art of smart homes, Eng. Appl. Artif. Intell., 25, 1313, 10.1016/j.engappai.2012.05.002 · DOI · Paper page
  18. Dharia, 2003, Neural network model for rapid forecasting of freeway link travel time, Eng. Appl. Artif. Intell., 16, 607, 10.1016/j.engappai.2003.09.011 · DOI · Paper page
  19. Du, 2003, Non-fragile output feedback h∞ vehicle suspension control using genetic algorithm, Eng. Appl. Artif. Intell., 16, 667, 10.1016/j.engappai.2003.09.008 · doi:10.1016/j.engappai.2003.09.008
  20. Du, 2012, A bibliometric account of chinese economics research through the lens of the China Economic Review, China Econ. Rev., 23, 743, 10.1016/j.chieco.2012.04.009 · DOI
  21. Faruk, 2010, A hybrid neural network and ARIMA model for water quality time series prediction, Eng. Appl. Artif. Intell., 23, 586, 10.1016/j.engappai.2009.09.015 · DOI · Paper page
  22. Fu, 2011, A review on time series data mining, Eng. Appl. Artif. Intell., 24, 164, 10.1016/j.engappai.2010.09.007 · DOI · Paper page
  23. Gokceoglu, 2004, A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock, Eng. Appl. Artif. Intell., 17, 61, 10.1016/j.engappai.2003.11.006 · DOI
  24. He, 2007, An effective co-evolutionary particle swarm optimization for constrained engineering design problems, Eng. Appl. Artif. Intell., 20, 89, 10.1016/j.engappai.2006.03.003 · DOI · Paper page
  25. Heck, 1986, Six decades of The Accounting Review: a summary of author and institutional contributors, Account. Rev., 735
  26. Ho, 2008, A hybrid genetic algorithm for the multi-depot vehicle routing problem, Eng. Appl. Artif. Intell., 21, 548, 10.1016/j.engappai.2007.06.001 · doi:10.1016/j.engappai.2007.06.001
  27. Kim, 2000, Application of interactive genetic algorithm to fashion design, Eng. Appl. Artif. Intell., 13, 635, 10.1016/S0952-1976(00)00045-2 · DOI
  28. Kuila, 2014, Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach, Eng. Appl. Artif. Intell., 33, 127, 10.1016/j.engappai.2014.04.009 · doi:10.1016/j.engappai.2014.04.009
  29. Laengle, 2017, Forty years of the European journal of operational research: A bibliometric overview, European J. Oper. Res., 10.1016/j.ejor.2017.04.027 · DOI · Paper page
  30. Leitao, 2009, Agent-based distributed manufacturing control: A state-of-the-art survey, Eng. Appl. Artif. Intell., 22, 979, 10.1016/j.engappai.2008.09.005 · DOI · Paper page
  31. Li, 2008, Adaboost with SVM-based component classifiers, Eng. Appl. Artif. Intell., 21, 785, 10.1016/j.engappai.2007.07.001 · DOI · Paper page
  32. Ma, 2011, Blended biogeography-based optimization for constrained optimization, Eng. Appl. Artif. Intell., 24, 517, 10.1016/j.engappai.2010.08.005 · DOI · Paper page
  33. Manjarres, 2013, A survey on applications of the harmony search algorithm, Eng. Appl. Artif. Intell., 26, 1818, 10.1016/j.engappai.2013.05.008 · DOI · Paper page
  34. Margaliot, 2001, Design and analysis of fuzzy schedulers using fuzzy Lyapunov synthesis, Eng. Appl. Artif. Intell., 14, 183, 10.1016/S0952-1976(00)00066-X · doi:10.1016/s0952-1976(00)00066-x
  35. McFarlane, 2003, Auto ID systems and intelligent manufacturing control, Eng. Appl. Artif. Intell., 16, 365, 10.1016/S0952-1976(03)00077-0 · DOI · Paper page
  36. Menhaj, 2000, A novel neuro-estimator and it’s application to parameter estimation in a remotely piloted vehicle, Eng. Appl. Artif. Intell., 13, 459, 10.1016/S0952-1976(00)00026-9 · doi:10.1016/s0952-1976(00)00026-9
  37. Merigó, 2015, A bibliometric overview of the Journal of Business Research between 1973 and 2014, J. Bus. Res., 68, 2645, 10.1016/j.jbusres.2015.04.006 · DOI · Paper page
  38. Muhuri, 2019, Industry 4.0: A bibliometric analysis and detailed overview, Eng. Appl. Artif. Intell., 78, 218, 10.1016/j.engappai.2018.11.007 · DOI · Paper page
  39. Muhuri, 2018, Applied soft computing: A bibliometric analysis of the publications and citations during (2004–2016), Appl. Soft Comput., 69, 381, 10.1016/j.asoc.2018.03.041 · DOI
  40. Muttil, 2007, Machine-learning paradigms for selecting ecologically significant input variables, Eng. Appl. Artif. Intell., 20, 735, 10.1016/j.engappai.2006.11.016 · doi:10.1016/j.engappai.2006.11.016
  41. Naqvi, 2005, Journal of documentation: a bibliometric study, Int. Inf. Commun. Educ., 24, 53
  42. Negenborn, 2008, Multi-agent model predictive control for transportation networks: Serial versus parallel schemes, Eng. Appl. Artif. Intell., 21, 353, 10.1016/j.engappai.2007.08.005 · DOI · Paper page
  43. Niknam, 2011, An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering, Eng. Appl. Artif. Intell., 24, 306, 10.1016/j.engappai.2010.10.001 · DOI · Paper page
  44. Niska, 2004, Evolving the neural network model for forecasting air pollution time series, Eng. Appl. Artif. Intell., 17, 159, 10.1016/j.engappai.2004.02.002 · DOI · Paper page
  45. Nourani, 2009, A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation, Eng. Appl. Artif. Intell., 22, 466, 10.1016/j.engappai.2008.09.003 · doi:10.1016/j.engappai.2008.09.003
  46. Rao, 2013, Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm, Eng. Appl. Artif. Intell., 26, 524, 10.1016/j.engappai.2012.06.007 · DOI · Paper page
  47. Rao, 2013, Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm, Eng. Appl. Artif. Intell., 26, 430, 10.1016/j.engappai.2012.02.016 · doi:10.1016/j.engappai.2012.02.016
  48. Rashedi, 2011, Filter modeling using gravitational search algorithm, Eng. Appl. Artif. Intell., 24, 117, 10.1016/j.engappai.2010.05.007 · DOI · Paper page
  49. Rengaswamy, 1995, A syntactic pattern-recognition approach for process monitoring and fault diagnosis, Eng. Appl. Artif. Intell., 8, 35, 10.1016/0952-1976(94)00058-U · DOI
  50. Samanta, 2003, Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection, Eng. Appl. Artif. Intell., 16, 657, 10.1016/j.engappai.2003.09.006 · DOI · Paper page
  51. Sardinas, 2006, Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes, Eng. Appl. Artif. Intell., 19, 127, 10.1016/j.engappai.2005.06.007 · doi:10.1016/j.engappai.2005.06.007
  52. Shukla, 2018, A review of the scopes and challenges of the modern real-time operating systems, Int. J. Embed. Real Time Commun. Sys. (IJERTCS), 9, 66, 10.4018/IJERTCS.2018010104 · doi:10.4018/ijertcs.2018010104
  53. Stergiopoulou, 2009, Hand gesture recognition using a neural network shape fitting technique, Eng. Appl. Artif. Intell., 22, 1141, 10.1016/j.engappai.2009.03.008 · DOI · Paper page
  54. Suryadevara, 2013, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Eng. Appl. Artif. Intell., 26, 2641, 10.1016/j.engappai.2013.08.004 · DOI · Paper page
  55. Taormina, 2012, Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the venice lagoon, Eng. Appl. Artif. Intell., 25, 1670, 10.1016/j.engappai.2012.02.009 · DOI · Paper page
  56. Ündey, 2003, Real-time batch process supervision by integrated knowledge-based systems and multivariate statistical methods, Eng. Appl. Artif. Intell., 16, 555, 10.1016/j.engappai.2003.09.003 · doi:10.1016/j.engappai.2003.09.003
  57. Uraikul, 2007, Artificial intelligence for monitoring and supervisory control of process systems, Eng. Appl. Artif. Intell., 20, 115, 10.1016/j.engappai.2006.07.002 · DOI
  58. Van Fleet, 2006, The journal of management’s first 30 years, J. Manag., 32, 477
  59. Weber, 2012, Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas, Eng. Appl. Artif. Intell., 25, 671, 10.1016/j.engappai.2010.06.002 · DOI · Paper page
  60. Wu, 2006, Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers, Eng. Appl. Artif. Intell., 19, 829, 10.1016/j.engappai.2005.12.011 · DOI · Paper page
  61. Xiang, 2008, Ant colony intelligence in multi-agent dynamic manufacturing scheduling, Eng. Appl. Artif. Intell., 21, 73, 10.1016/j.engappai.2007.03.008 · DOI · Paper page
  62. Xu, 2017, The structure and citation landscape of IEEE transactions on Fuzzy systems (1994-2015), IEEE Trans. Fuzzy Syst.
  63. Yataganbaba, 2016, A scientific approach with bibliometric analysis related to brick and tile drying: A review, Renew. Sustain. Energy Rev., 59, 206, 10.1016/j.rser.2015.12.357 · DOI
  64. Yeung, 2004, Strip-packing using hybrid genetic approach, Eng. Appl. Artif. Intell., 17, 169, 10.1016/j.engappai.2004.02.003 · doi:10.1016/j.engappai.2004.02.003
  65. Yildiz, 2013, Comparison of evolutionary-based optimization algorithms for structural design optimization, Eng. Appl. Artif. Intell., 26, 327, 10.1016/j.engappai.2012.05.014 · DOI · Paper page
  66. Yu, 2017, Information sciences 1968-2016: A retrospective analysis with text mining and bibliometric, Inform. Sci., 10.1016/j.ins.2017.08.031 · DOI · Paper page
  67. Zavadskas, 2014, Performance analysis of Civil Engineering Journals based on the Web of Science database, Arch. Civ. Mech. Eng., 14, 519, 10.1016/j.acme.2014.05.008 · DOI
  68. Zouggari, 2012, Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem, Eng. Appl. Artif. Intell., 25, 507, 10.1016/j.engappai.2011.10.012 · DOI · Paper page

Cited by

See all 183

Metadata

Authors
Shukla, A. K., Janmaijaya, M., Abraham, A., & Muhuri, P. K.
Venue
Engineering Applications of Artificial Intelligence
Publisher
Elsevier BV
Published
2019-10
Volume
85
Pages
517-532
ISSN
0952-1976 (ISSN-L)
DOI
10.1016/j.engappai.2019.06.010
OpenAlex
W2961273696
Cited by
183
References
1
FWCI
10.85
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
  • INSPIRE fellowship received from the Department of Science and Technology, Government of India
  • South Asian University, New Delhi, India

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988–2018). https://app.cochat.ai/papers/10.1016/j.engappai.2019.06.010
The paper
Shukla, A. K., Janmaijaya, M., Abraham, A., & Muhuri, P. K. (2019). Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988–2018). Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2019.06.010

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.