Visualization analysis of the journal of intelligent & fuzzy systems (2002

Abstract

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.

Keywords

Journal analysis visualization analysis CiteSpace burst detection

1 Introduction

Maxim Gorky once said that books are the stepping stones to human progress [1]. However, in the field of scientific research, journals have taken on the role. Due to the explosive growth in recent scientific research, especially in interdisciplinary science, to make an aerial view that would require experts in many fields who could follow and evaluate the massive literature is seen as a near impossibility. Hence, scientometrics gives an overall perspective of related scientific activity.

Scientometrics involves statistical analysis of written publications such as conference reports or scientific articles. In a typical manner, bibliographical data from citation index database (i.e. titles, abstracts, keywords, authors, institutions, etc.) are statistically analyzed to evaluate the influence of specific contents. The statistics of a particular single journal can provide an objective and comprehensive analysis of the related discipline progress. Several studies in the scientometrics of fuzzy and related journals have already been done [2 –8].

Intelligent system is a sub category of computer sciences concerned with specific goals and tasks through flexible adaptation [9]. The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. These consist of particular traits or capabilities that researchers expect an intelligent system to display [10 –12]. Intelligent system in its present form came after a degree of influence from fuzzy logic [13, 14].

The Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology (JIFS) is one of the prominent international journals in the areas of fuzzy logic, intelligent systems, and web-based applications. It is published by IOS Press. According to the Journal Citation Report from Clarivate Analytics in 2018, the journal impact factor for JIFS is 1.426 [15].

The purpose of JIFS is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies such as the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues, covering a broad cross-section of technical disciplines.

The motivation behind this study is to discover the important influencing aspects that conduct the JIFS publications and its citation framework using visualization methods. In this study, a data set of 3,786 articles published in JIFS between 2002 and 2018 was retrieved from Web of Science (WoS) and used for quantitative and visual research. It is indispensable to make a knowledge domain map for the journal’s details, to create a bibliometric profile of JIFS including its growth, and to identify the most frequently published authors, the main subject areas, the institutions and the countries where they were from.

2 Research tools and data

This article introduces richly functional visualization software named CiteSpace, which is developed by Prof. Chaomei Chen in Drexel University [16]. CiteSpace stemming from Java platform can describe the tendency to react to the latest development in a certain period or a field of knowledge explicitly.

The literature of CiteSpace has increased considerably over the past five years, and more than 70 documents, including journal articles and the papers in proceedings in which CiteSpace was used as a research tool, have been published and included in Science Citation Index. Thereinto, Liu [17] compared the existing research state on terahertz technology between 1990 and 2010 using visualization techniques of CiteSpace. Chen et al. [18] constructed and visualized networks of co-cited references representing the literature of regenerative medicine, based on a combined dataset of 71,393 articles published between 2000 and 2014. Kim et al. [19] identified intellectual landscapes, landmark articles and bursting keywords from the literature of recommendation systems between1992 and 2014 using the similar software. Yu et al. [20] mapped the reference co-citation network to explore the important documents and research clusters in linguistic decision making area, and generated the co-citation networks to reveal the influential scholars and the dominant journals. Xu et al. [21] used co-citation and hierarchal clustering analysis to summarize global research trends and activities on multimorbidity.

Web of Science (WoS) is a large, multidisciplinary and comprehensive academic online subscription-based scientific citation indexing service database, with which the Institute for Scientific Information (ISI) was founded in 1955 [22]. Then it was sold to the Thomson Corporation in 1992, and now maintained by Clarivate Analytics. The contents of WoS cover the most influential scientific journals in many fields such as natural sciences, engineering technology, biology and medicine, social sciences, arts and humanities. Therefore, WoS is undoubtedly an appropriate document retrieval tool, from which further required information in this study is derived.

Web of Science Core Collection was selected as the target database. Meanwhile, publication name was determined to be “Journal of Intelligent & Fuzzy Systems”, and document types building was declared as “ARTICLE”. Ultimately, a record containing 3,786 articles from 2002 to 2018 was collected to provide research materials. An overall time distribution of the articles with respect to year of publication is displayed in Fig. 1.

Fig. 1

Articles publication trends from 2002 to 2018.

3 Journal analysis results

3.1 Highly cited references

Highly Cited references are extrapolated from counts of articles published in scholarly journals and the citations to those articles. In this study, the total amount of references is statistically figured out by the software called CiteSpace. And that is 73,443, generated by 3,786 articles attributable all to JIFS. Therefore, each article has an average of more than 19 references. Then 20 highly cited articles in recent 10 years are selected from 73,443 references to augment the knowledge and understanding already presented from JIFS.

Figure 2 depicts intuitively the main references and their networks with the application of CiteSpace. Every reference is labeled by the first author’s name and publication year.

Fig. 2

Most cited articles with their first authors since 2009.

Table 1 describes the top 20 most cited documents from JIFS since 2009. In addition to the above, many different researchers could be inspired by the classic literatures of Zadeh [23], Atanassov [24], and Pawlak [25], et al, which have been cited more than 100 times likewise. The indicator of reference counts means the accurate number of the citations. From Table 1, we found that “Hesitant fuzzy sets” became the most cited document and stayed No. 1 after the year of 2009. Torra [26] in 2010 showed that the interpretation of hesitant fuzzy sets differed from the two existing approaches for fuzzy multisets and also introduced some basic operations and their relationship with intuitionistic fuzzy sets.

Table 1

Top 20 most cited articles in the recent 10 years

No.	Title	Reference counts	Publication year
1	Hesitant fuzzy sets [26]	150	2010
2	Hesitant fuzzy information aggregation in decision making [27]	109	2011
3	Hesitant fuzzy prioritized operators and their application to multiple attribute decision making [28]	89	2012
4	Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making [29]	86	2010
5	Distance and similarity measures for hesitant fuzzy sets [30]	74	2011
6	Hesitant fuzzy linguistic term sets for decision making [31]	62	2012
7	Gray relational analysis method for intuitionistic fuzzy multiple attribute decision making [32]	60	2011
8	Some hesitant interval-valued fuzzy aggregation operators and their applications to multiple attribute decision making [33]	56	2013
9	On hesitant fuzzy sets and decision [34]	53	2009
10	On distance and correlation measures of hesitant fuzzy information [35]	52	2011
11	GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting [36]	51	2010
12	Approaches to interval intuitionistic trapezoidal fuzzy multiple attribute decision making with incomplete weight information [37]	50	2015
13	Hesitant fuzzy geometric Bonferroni means [38]	49	2012
14	On some new operations in soft set theory [39]	49	2009
15	Induced hesitant interval-valued fuzzy Einstein aggregation operators and their application to multiple attribute decision making [40]	48	2013
16	Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment [41]	48	2010
17	A survey of decision making methods based on certain hybrid soft set models [42]	47	2017
18	A novel soft rough set: Soft rough hemirings and corresponding multicriteria group decision making [43]	46	2017
19	Potential optimality and robust optimality in multiattribute decision analysis with incomplete information: A comparative study [44]	46	2013
20	Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment [45]	45	2010

3.2 Predominant authors and potential younger researchers

Figure 3 shows the most cited scholars in JIFS from 2002 to 2018. In this figure, the name of Zadeh LA has pride of place. Prof. Zadeh is best known for proposing fuzzy mathematics consisting of these fuzzy-related concepts such as fuzzy sets, fuzzy logic, fuzzy control, fuzzy systems, and etcetera [23]. Hence, Prof. Zadeh, one of the founders of the fuzzy discipline, is widely cited. Figure 4 reveals hundreds of citations on Zadeh’s academic works as the average occurrence number of the word “Zadeh” in each article’s references is nearly 0.3, which signifies Zadeh’s papers are cited at least once in every four articles published in JIFS.

Fig. 3

Most cited scholars.

Fig. 4

The average citation rate of Zadeh in JIFS.

There are probably four or five other world’s authorities in this area besides Prof. Zadeh. Among them, Prof. Krassimir Atanassov [24] is best known for launching the concept of intuitionistic fuzzy sets, which is the extension of the concept of fuzzy sets. Prof. Zdzislaw Pawlak [25] has introduced the rough set theory, and is also known for his fundamental works on it. And Prof. Ronald Yager [46] is an active IEEE Fellow in computational intelligence, decision making under uncertainty and fuzzy logic. And then Prof. Didier Dubois [47] is a CNRS Research Advisor in fuzzy logic at the IRIT Laboratory of Paul Sabatier University, whose research interests include fuzzy set theory, possibility theory, and knowledge representation.

Figure 5 shows the names of top 20 productive and influential authors in JIFS. Each of the authors has contributed more than 16 papers in JIFS. From them, top 10 authors with the strongest citation bursts have been taken in Table 2.

Fig. 5

Top 20 productive authors in JIFS.

Table 2

Top 10 authors with the strongest citation bursts

No.	Authors	Burst strength	Begin	End	Graphical remarks (2002–2018)
1	ZHAO XF	8.0237	2013	2014
2	WEI GW	6.6644	2013	2014
3	JAIN LC	6.6068	2009	2013
4	ZHAN JM	4.3322	2013	2014
5	DAVVAZ B	4.2706	2006	2013
6	KAVOUSI-FARD A	4.2467	2014	2015
7	ABBASI A	3.8586	2014	2015
8	XU ZS	3.5537	2014	2015
9	SPAANENBURG L	3.4413	2003	2004
10	LIM CP	3.3208	2010	2013

The first one from Table 2 is Prof. Xiaofei Zhao, who was born in 1980, and from Chongqing University of Arts and Sciences. Prof. Zhao diverted himself into the field of decision making study [48]. Prof. Guiwu Wei, Porf. Lakhmi Jain, Prof. Jianming Zhan, and Prof. Bijan Davvaz were all born before the 1980s. Dr. Abdollah Kavousi-Fard, as another potential younger researcher, has got his Ph.D degree around 2016 in Shiraz University of Technology [49].

Prof. Zeshui Xu is an authoritative expert from China. He is the IEEE Fellow, IFSA Fellow, IET Fellow, the Distinguished Young Scholar of NSFC, and the Chang Jiang Scholar of the Ministry of Education of China. Prof. Huchang Liao is one of his top co-authors. Prof. Liao was born in 1989, and has published more than 120 peer-reviewed papers, many in high-quality international journals [50].

Another problem studied in this paper goes back to the year 1926, when Alfred Lotka [51] introduced a law on the fraction of the authors that publish a certain number of papers: $f (x) = \frac{C}{x^{n}}$ (1)

Where f (x) is the fraction of the authors, x is the number of papers, n is usually bigger than 1, most classically; Lotka’s law was expressed for n equaling to 2, C then being roughly equivalent to 0.61.

The first step in testing of Lotka’s law in JIFS is to calculate the value of n (see Table 3). The method of linear least square [52] is used in this paper to define n = 2.558. $n = | \frac{N \sum XY - \sum X \sum Y}{N \sum XX - \sum X \sum X} |$ (2)

Where N = 27.

The computed value of the constant C in the current data is according to Prof. Pao [53]. $C = \frac{1}{[\sum_{x = 1}^{19} \frac{1}{x^{n}} + \frac{1}{(n - 1) 20^{n - 1}} + \frac{1}{2 \times 20^{n}} + \frac{n}{24 \times 19^{n + 1}}]}$ (3)

Where n = 2.558.

For the data from Table 3, the value of the parameter was computed as C = 0.7573. Thus this set of JIFS data complies with Lotka’s formulation: $f (x) = \frac{0.7573}{x^{2.558}}$ (4)

The computed value of the constant C in the current data is c = 0.7573 (75.73%), which indicates that the proportion of contributors who publish only one paper in JIFS is over seventy-five percent. Table 3 shows that fourteen percent of the authors contributed two articles and five articles by one percent and so on. The constant C also implies that JIFS is an emerging field to which more and more attention has been paid and the number of new authors continues to grow.

Table 3

The distribution of authors and articles

No.	Fraction of the authors [f(x)]	Number of publications [x]	Y = log[f (x)]	X = log(x)	XY	XX
1	75.25%	1	–0.1235	0.0000	0.0000	0.0000
2	13.82%	2	–0.8594	0.3010	–0.2587	0.0906
3	4.90%	3	–1.3097	0.4771	–0.6249	0.2276
4	2.30%	4	–1.6380	0.6021	–0.9862	0.3625
5	1.09%	5	–1.9610	0.6990	–1.3707	0.4886
6	0.72%	6	–2.1399	0.7782	–1.6652	0.6055
7	0.47%	7	–2.3290	0.8451	–1.9682	0.7142
8	0.36%	8	–2.4496	0.9031	–2.2122	0.8156
9	0.27%	9	–2.5688	0.9542	–2.4512	0.9106
10	0.20%	10	–2.7014	1.0000	–2.7014	1.0000
11	0.13%	11	–2.8933	1.0414	–3.0130	1.0845
12	0.07%	12	–3.1485	1.0792	–3.3978	1.1646
13	0.04%	13	–3.3704	1.1139	–3.7544	1.2409
14	0.01%	14	–3.8475	1.1461	–4.4097	1.3136
15	0.03%	15	–3.5465	1.1761	–4.1710	1.3832
16	0.06%	16	–3.2455	1.2041	–3.9079	1.4499
17	0.03%	17	–3.5465	1.2304	–4.3638	1.5140
18	0.03%	18	–3.5465	1.2553	–4.4518	1.5757
19	0.03%	19	–3.5465	1.2788	–4.5351	1.6352
20	0.01%	20	–3.8475	1.3010	–5.0057	1.6927
21	0.06%	21	–3.2455	1.3222	–4.2912	1.7483
22	0.01%	22	–3.8475	1.3424	–5.1650	1.8021
23	0.01%	24	–3.8475	1.3802	–5.3104	1.9050
24	0.03%	25	–3.5465	1.3979	–4.9578	1.9542
25	0.01%	26	–3.8475	1.4150	–5.4441	2.0021
26	0.03%	28	–3.5465	1.4472	–5.1323	2.0943
27	0.01%	48	–3.8475	1.6812	–6.4686	2.8266

3.3 Famous journals cited

A sizeable number of journals appeared in the references section from JIFS can be seen below in Fig. 6. According to the software of CiteSpace, right now there are more than 2,800 journals which are worth mentioning in many kinds of academical papers. Among these journals, some have been cited over 1,000 times.

Fig. 6

Famous journals cited.

The journal “Fuzzy Sets and Systems” has been devoted to the international advancement of the theory and application of fuzzy sets and systems since its launching in 1978. In addition, “Information Sciences” publishes original, innovative and creative research results related to academic activities in information, knowledge engineering and intelligent systems. In Table 4, it shows the cited journals with the strongest citation bursts, and the classic papers from the top journals such as “Nature” or “Science” [54 –56] are included.

Table 4

Top 20 cited journals with the strongest citation bursts

No.	Names of journals	Burst strength	Begin	End
1	IEEE Transactions on Neural Networks	19.3797	2002	2012
2	Proceedings of the IEEE	15.4655	2002	2013
3	Artificial Intelligence	13.7330	2002	2012
4	International Journal of Man-machine Studies	11.7330	2003	2008
5	Genetic Algorithms and Simulated Annealing	9.9359	2002	2013
6	Fuzzy Sets &Systems	7.3812	2003	2015
7	Robotics and Autonomous Systems	6.7082	2003	2013
8	Machine Learning	5.7659	2002	2008
9	Artificial Intelligence in Medicine	5.5289	2002	2012
10	Neural Networks	5.4254	2002	2011
11	IEEE Transactions on Robotics and Automation	5.3450	2003	2006
12	Science	5.1249	2002	2008
13	Control Engineering Practice	5.0302	2003	2013
14	Lecture Notes in Computer Science	4.7111	2002	2010
15	IEEE Transactions on Automatic Control	4.2231	2003	2011
16	IEEE Transactions on Pattern Analysis and Machine Intelligence	4.1627	2002	2012
17	Bioinformatics	3.8858	2002	2007
18	Proceedings of the National Academy of Sciences, USA	3.8524	2002	2008
19	AI Magazine	3.4131	2002	2003
20	Nature	3.2379	2002	2007

3.4 Noticeable research keywords

The computational results in Fig. 7 illustrate something concerned about the attributes of keywords. One of the main differences is that “set”, “model” and “system” are the three most common recurring words, and all of them appear at least 300 times without exception. The links between the keywords of “fuzzy logic” and “fuzzy set” appear to be blue. It means that the two keywords were concentrated in the years before 2006.

Fig. 7

Keywords occurring in order of increasing frequency.

Also, the top 10 recently bursting keywords were listed in Table 5 and “multi-attribute decision making” (MADM) was made to maintain No. 1 ranking. For “decision making” MADM is a critical subset of its mission, which is one of the quantitative research methods. MADM can give managers many dimensions to consider related elements, and evaluate all possible options under variable degrees [57]. According to the burst strength of MADM, this field of research could be the future work on fuzzy systems.

Table 5

Top 10 keywords with the strongest citation bursts since 2016

No.	Keywords	Burst strength
1	Multi-attribute decision making (MADM)	14.4621
2	Aggregation operator	5.2872
3	Lattice	4.9559
4	Term set	4.4282
5	Existence	4.2936
6	Approximation operator	4.2936
7	Intuitionistic fuzzy number	3.5328
8	Triangular fuzzy number	3.4334
9	Incomplete weight information	3.0354
10	Correlation coefficient	2.9279

And secondly, the study of aggregation operators would be another newly emerged research direction. The aggregation operators perform mathematical operations like Average, Aggregate, Count, Max, Min and Sum, on the numeric property of the elements in the collection. In applied mathematics, specifically in fuzzy logic, the ordered weighted averaging (OWA) operators provide a parameterized class of mean type aggregation operators. They were introduced by Prof. Yager [58]. Many notable mean operators such as the max, arithmetic average, median and min, are members of this class. They have been widely used in computational intelligence because of their ability to model linguistically expressed aggregation instructions [59].

4 Conclusions

The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications. And it is a core journal of fuzzy systems research.

In this paper, we have explored the 17 years (2002–2018) publications and citation history of JIFS using bibliometric methods and techniques to exhibit an aerial view and hidden publication structure of it. The bibliographic details of a total of 3,786 papers were analyzed ( 220 per year). JIFS published the largest number of papers in 2018 (858), and it’s estimated that the rate of growth would sustains.

Some visualized results have also been discovered in this study. Firstly, the highlight of “hesitant fuzzy sets” began in the last ten years. Then, Zadeh, Atanassov, Pawlak, Yager and Dubois are the world’s authorities specifically mentioned in the fuzzy discipline. But Zhao, Kavousi-Fard, and Liao are potential younger researchers. The proportion of contributors who published papers in JIFS complies with Lotka’s formulation, with n = 2.558 and C = 0.7573 (75.73%).

“Fuzzy Sets and Systems” and “Information Sciences” can be regarded as the first-tier influential journals in the references of JIFS. And the other cited journals with the strongest citation bursts were also sorted. The future work on fuzzy systems could include MADM and the study of aggregation operators.

A preliminary demonstration of the development of JIFS has been given out in this paper. The following study would be launched in some important respects, such as comparative analysis between different countries or different periods, including other more journals focusing on fuzzy systems in WoS.

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Visualization analysis of the journal of intelligent & fuzzy systems (2002–2018)

Abstract

Keywords

1 Introduction

2 Research tools and data

3.1 Highly cited references

References