To identify the continuous research status for a specific journal is the purpose of this study, employing a visualization software named CiteSpace, which is an effective scientometrics tool widely spread. 3,786 articles published in the Journal of Intelligent & Fuzzy Systems between 2002 and 2018 were retrieved from Web of Science. The co-citation network of references was linked and mapped to explore the important documents. Soon afterwards, the author co-citation network, the journal co-citation network, and the keywords co-citation network are equally important. The results of this study are instructive and meaningful to guide practice in scientific research.
Aksapada, Maxims of Gorky: Quotes of Maxim Gorky, Independently published 2018.
2.
CancinoC., MerigóJ.M., CoronadoF., DessoukyY. and DessoukyM., Forty years of Computers & Industrial Engineering: A bibliometric analysis, Computers & Industrial Engineering113 (2017), 614–629.
3.
MerigóJ.M., Blanco-MesaF., Gil-LafuenteA.M. and YagerR.R., Thirty years of the International Journal of Intelligent Systems: A bibliometric review, International Journal of Intelligent Systems32(5) (2017), 526–554.
4.
MerigóJ.M., PedryczW., WeberR. and deC., la Sotta, Fifty years of Information Sciences: A bibliometric overview, Information Sciences432 (2018), 245–268.
5.
WangW., LaengleS., MerigóJ.M., YuD., Herrera-ViedmaE., CoboM.J. and Bouchon-MeunierB., A bibliometric analysis of the first twenty-five years of the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems26(2) (2018), 169–193.
6.
TangM., LiaoH. and SuS.F., A bibliometric overview and visualization of the International Journal of Fuzzy Systems between 2007 and 2017, International Journal of Fuzzy Systems20(5) (2018), 1403–1422.
7.
MuhuriP.K., ShuklaA.K., JanmaijayaM. and BasuA., Applied soft computing: A bibliometric analysis of the publications and citations during (2004–2016), Applied Soft Computing69 (2018), 381–392.
8.
YuD., XuZ., KaoY. and LinC.T., The structure and citation landscape of IEEE Transactions on Fuzzy Systems (1994–2015), IEEE Transactions on Fuzzy Systems26(2) (2018), 430–442.
9.
RussellS.J., NorvigP.Artificial intelligence: a modern approach, Pearson Education Limited 2016.
10.
ChowdhuryN.K. and LeungK.S., Improved travel time prediction algorithms for intelligent transportation systems, Journal of Intelligent & Fuzzy Systems21((1–2)) (2011), 5–7.
11.
LimC.P., AbeynayakeC., Sato-IlicM. and JainL.C., Special issue: computational intelligence models for image processing and information reasoning, Journal of Intelligent & Fuzzy Systems24(2) (2013), 199–200.
12.
SarganoA.B., SarfrazM. and HaqN.U., An intelligent system for paper currency recognition with robust features, Journal of Intelligent & Fuzzy Systems27(4) (2014), 1905–1913.
13.
HolzmannH., GermannS., HalfmannC. and IsermannR., Intelligent fuzzy distance and cruise control for passenger cars, Journal of Intelligent & Fuzzy Systems6(3) (1998), 315–327.
14.
SethukkarasiR., GanapathyS., YogeshP. and KannanA., An intelligent neuro fuzzy temporal knowledge representation model for mining temporal patterns, Journal of Intelligent & Fuzzy Systems26(3) (2014), 1167–1178.
ChenC., CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature, Journal of the American Society for Information Science and Technology57(3) (2006), 359–377.
17.
LiuG., Visualization of patents and papers in terahertz technology: A comparative study, Scientometrics94(3) (2013), 1037–1056.
18.
ChenC., DubinR. and KimM.C., Emerging trends and new developments in regenerative medicine: A scientometric update (2000–2014), Expert Opinion on Biological Therapy14(9) (2014), 1295–1317.
19.
KimM. and ChenC., A scientometric review of emerging trends and new developments in recommendation systems, Scientometrics104(1) (2015), 239–263.
20.
YuD., LiD., MerigóJ. and FangL., Mapping development of linguistic decision making studies, Journal of Intelligent & Fuzzy Systems30(5) (2016), 2727–2736.
21.
XuX., MishraG. and JonesM., Mapping the global research landscape and knowledge gaps on multimorbidity: A bibliometric study, Journal of Global Health7(1) (2017), 1–11.
22.
GarfieldE., Citation indexes for science. A new dimension in documentation through association of ideas, International journal of epidemiology35(5) (2006), 1123–1127.
23.
ZadehL., Fuzzy sets, Information & Control8(3) (1965), 338–353.
24.
AtanassovK., Intuitionistic fuzzy sets, Fuzzy sets and Systems20(1) (1986), 87–96.
25.
PawlakZ., Rough sets, International Journal of Parallel Programming11(5) (1982), 341–356.
26.
TorraV., Hesitant fuzzy sets, International Journal of Intelligent Systems25(6) (2010), 529–539.
27.
XiaM. and XuZ., Hesitant fuzzy information aggregation in decision making, International Journal of Approximate Reasoning52(3) (2011), 395–407.
28.
WeiG., Hesitant fuzzy prioritized operators and their application to multiple attribute decision making, Knowledge-Based Systems31(7) (2012), 176–182.
29.
WeiG., Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making, Applied Soft Computing10(2) (2010), 423–431.
30.
XuZ. and XiaM., Distance and similarity measures for hesitant fuzzy sets, Information Sciences181(11) (2011), 2128–2138.
31.
RodriguezR., MartinezL. and HerreraF., Hesitant fuzzy linguistic term sets for decision making, IEEE Transactions on Fuzzy Systems20(1) (2012), 109–119.
32.
WeiG., Gray relational analysis method for intuitionistic fuzzy multiple attribute decision making, Expert Systems with Applications38(9) (2011), 11671–11677.
33.
WeiG., ZhaoX. and LinR., Some hesitant interval-valued fuzzy aggregation operators and their applications to multiple attribute decision making, Knowledge-Based Systems46(1) (2013), 43–53.
34.
TorraV. and NarukawaY., On hesitant fuzzy sets and decision, IEEE International Conference on Fuzzy Systems (2009), 1378–1382.
35.
XuZ. and XiaM., On distance and correlation measures of hesitant fuzzy information, International Journal of Intelligent Systems26(5) (2011), 410–425.
36.
WeiG., GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting, Knowledge-Based Systems23(3) (2010), 243–247.
37.
WeiG., Approaches to interval intuitionistic trapezoidal fuzzy multiple attribute decision making with incomplete weight information, International Journal of Fuzzy Systems17(3) (2015), 484–489.
38.
ZhuB., Hesitant fuzzy geometric bonferroni means, Information Sciences205(1) (2012), 72–85.
39.
AliM., FengF., LiuX., MinW. and ShabirM., On some new operations in soft set theory, Computers & Mathematics with Applications57(9) (2009), 1547–1553.
40.
WeiG. and ZhaoX., Induced hesitant interval-valued fuzzy Einstein aggregation operators and their application to multiple attribute decision making, Journal of Intelligent & Fuzzy Systems24(4) (2013), 789–803.
41.
YeJ., Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment, European Journal of Operational Research205(1) (2010), 202–204.
42.
MaX., LiuQ. and ZhanJ., A survey of decision making methods based on certain hybrid soft set models, Artificial Intelligence Review47(4) (2017), 507–530.
43.
ZhanJ., LiuQ. and HerawanT., A novel soft rough set: Soft rough hemirings and corresponding multi-criteria group decision making, Applied Soft Computing54 (2017), 393–402.
44.
WeiG., WangJ. and ChenJ., Potential optimality and robust optimality in multi-attribute decision analysis with incomplete information: A comparative study, Decision Support Systems55(3) (2013), 679–684.
45.
YeJ., Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment, European Journal of Operational Research205(1) (2010), 202–204.
46.
YagerR., On ordered weighted averaging aggregation operators in multi-criteria decision making, Readings in Fuzzy Sets for Intelligent Systems18(1) (1993), 80–87.
47.
DuboisD. and PradeH., Ranking fuzzy numbers in the setting of possibility theory, Information Sciences30(3) (1983), 183–224.
48.
ZhaoX. and GuoX., Chemical reaction optimization algorithm for solving robotic cell scheduling problem, Operations Research and Management Science26(6) (2017), 35–41.
49.
Kavousi-FardA., Akbari-ZadehM.R., Kavousi-FardF. and RostamiM.A., Effect of wind turbine on the economic load dispatch problem considering the wind speed uncertainty, Journal of Intelligent & Fuzzy Systems28(2) (2015), 693–705.
50.
LiaoH. and XuZ., Multi-criteria decision making with intuitionistic fuzzy PROMETHEE, Journal of Intelligent & Fuzzy Systems27(4) (2014), 1703–1717.
51.
LotkaA.J., The frequency distribution of scientific productivity, Journal of the Washington academy of sciences16(12) (1926), 317–323.
52.
StiglerS.M., Gauss and the invention of least squares, The Annals of Statistics9(3) (1981), 465–474.
53.
PaoM.L., Lotka’s law: a testing procedure, Information Processing & Management21(4) (1985), 305–320.
54.
RumelhartD., HintonG. and WilliamsR., Learning representation by back-propagating errors, Nature323(6088) (1986), 533–536.
55.
MackeyM. and GlassL., Oscillation and chaos in physiological control systems, Science197(4300) (1977), 287–289.
56.
KirkpatrickS., GelattC. and VecchiM., Optimization by simulated annealing, Science220(4598) (1983), 671–680.
57.
HwangC.L., YoonK., Multiple attribute decision making: methods and applications a state-of-the-art survey, Springer Science & Business Media, 2012.
58.
YagerR.R., OWA Aggregation with an uncertainty over the arguments, Information Fusion52 (2019), 206–212.
59.
ZhangS. and YuD., Some geometric choquet aggregation operators using Einstein operations under intuitionistic fuzzy environment, Journal of Intelligent & Fuzzy Systems26(1) (2014), 491–500.
