Example of Journal of Intelligent Systems format
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Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format
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Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format Example of Journal of Intelligent Systems format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Journal of Intelligent Systems — Template for authors

Publisher: De Gruyter
Categories Rank Trend in last 3 yrs
Information Systems #205 of 329 down down by 31 ranks
Artificial Intelligence #163 of 227 down down by 32 ranks
Software #283 of 389 down down by 1 rank
journal-quality-icon Journal quality:
Medium
calendar-icon Last 4 years overview: 257 Published Papers | 456 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 02/07/2020
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.8

10% from 2019

CiteRatio for Journal of Intelligent Systems from 2016 - 2020
Year Value
2020 1.8
2019 2.0
2018 1.6
2017 1.3
2016 0.8
graph view Graph view
table view Table view

0.21

4% from 2019

SJR for Journal of Intelligent Systems from 2016 - 2020
Year Value
2020 0.21
2019 0.201
2018 0.188
2017 0.193
2016 0.177
graph view Graph view
table view Table view

0.751

35% from 2019

SNIP for Journal of Intelligent Systems from 2016 - 2020
Year Value
2020 0.751
2019 0.557
2018 0.537
2017 0.572
2016 0.254
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 10% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 4% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 35% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of Intelligent Systems

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De Gruyter

Journal of Intelligent Systems

The Journal of Intelligent Systems provides readers with a compilation of stimulating and up-to-date articles within the field of intelligent systems. The focus of the journal is on high quality research that addresses paradigms, development, applications and implications in t...... Read More

Mathematics

i
Last updated on
02 Jul 2020
i
ISSN
2191-026X
i
Impact Factor
Low - 0.206
i
Acceptance Rate
Not provided
i
Frequency
4 Issues per year
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
unsrt
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Citation Type
Numbered
[25]
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Bibliography Example
C. W. J. Beenakker. Specular andreev reflection in graphene. Phys. Rev. Lett., 97(6):067007, 2006.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1002/INT.V25:6
Hesitant fuzzy sets
Vicenç Torra1

Abstract:

Several extensions and generalizations of fuzzy sets have been introduced in the literature, for example, Atanassov's intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets. In this paper, we propose hesitant fuzzy sets. Although from a formal point of view, they can be seen as fuzzy multisets, we will show that th... Several extensions and generalizations of fuzzy sets have been introduced in the literature, for example, Atanassov's intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets. In this paper, we propose hesitant fuzzy sets. Although from a formal point of view, they can be seen as fuzzy multisets, we will show that their interpretation differs from the two existing approaches for fuzzy multisets. Because of this, together with their definition, we also introduce some basic operations. In addition, we also study their relationship with intuitionistic fuzzy sets. We prove that the envelope of the hesitant fuzzy sets is an intuitionistic fuzzy set. We prove also that the operations we propose are consistent with the ones of intuitionistic fuzzy sets when applied to the envelope of the hesitant fuzzy sets. © 2010 Wiley Periodicals, Inc. read more read less

Topics:

Fuzzy set operations (76%)76% related to the paper, Type-2 fuzzy sets and systems (74%)74% related to the paper, Defuzzification (71%)71% related to the paper, Fuzzy number (70%)70% related to the paper, Fuzzy set (68%)68% related to the paper
2,232 Citations
Journal Article DOI: 10.1002/INT.21676
Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets
Xiaolu Zhang1, Zeshui Xu2

Abstract:

Recently, a new model based on Pythagorean fuzzy set PFS has been presented to manage the uncertainty in real-world decision-making problems. PFS has much stronger ability than intuitionistic fuzzy set to model such uncertainty. In this paper, we define some novel operational laws of PFSs and discuss their desirable propertie... Recently, a new model based on Pythagorean fuzzy set PFS has been presented to manage the uncertainty in real-world decision-making problems. PFS has much stronger ability than intuitionistic fuzzy set to model such uncertainty. In this paper, we define some novel operational laws of PFSs and discuss their desirable properties. For the multicriteria decision-making problems with PFSs, we propose an extended technique for order preference by similarity to ideal solution method to deal effectively with them. In this approach, we first propose a score function based comparison method to identify the Pythagorean fuzzy positive ideal solution and the Pythagorean fuzzy negative ideal solution. Then, we define a distance measure to calculate the distances between each alternative and the Pythagorean fuzzy positive ideal solution as well as the Pythagorean fuzzy negative ideal solution, respectively. Afterward, a revised closeness is introduced to identify the optimal alternative. At length, a practical example is given to illustrate the developed method and to make a comparative analysis. read more read less

Topics:

Fuzzy number (64%)64% related to the paper, Fuzzy set operations (63%)63% related to the paper, Type-2 fuzzy sets and systems (63%)63% related to the paper, Fuzzy classification (60%)60% related to the paper, TOPSIS (58%)58% related to the paper
1,084 Citations
Journal Article DOI: 10.1002/INT.21738
Some Results for Pythagorean Fuzzy Sets
Xindong Peng1, Yong Yang1

Abstract:

Pythagorean fuzzy sets PFSs, originally proposed by Yager Yager, Abbasov. Int J Intell Syst 2013;28:436-452, are a new tool to deal with vagueness considering the membership grades are pairs µ,i¾? satisfying the condition µ2+i¾?2i¾?1. As a generalized set, PFSs have close relationship with intuitionistic fuzzy sets IFSs. PFSs... Pythagorean fuzzy sets PFSs, originally proposed by Yager Yager, Abbasov. Int J Intell Syst 2013;28:436-452, are a new tool to deal with vagueness considering the membership grades are pairs µ,i¾? satisfying the condition µ2+i¾?2i¾?1. As a generalized set, PFSs have close relationship with intuitionistic fuzzy sets IFSs. PFSs can be reduced to IFSs satisfying the condition µ+i¾?i¾?1. However, the related operations of PFSs do not take different conditions into consideration. To better understand PFSs, we propose two operations: division and subtraction, and discuss their properties in detail. Then, based on Pythagorean fuzzy aggregation operators, their properties such as boundedness, idempotency, and monotonicity are investigated. Later, we develop a Pythagorean fuzzy superiority and inferiority ranking method to solve uncertainty multiple attribute group decision making problem. Finally, an illustrative example for evaluating the Internet stocks performance is given to verify the developed approach and to demonstrate its practicality and effectiveness. read more read less

Topics:

Fuzzy logic (53%)53% related to the paper
657 Citations
Journal Article DOI: 10.1002/INT.21809
A New Generalized Pythagorean Fuzzy Information Aggregation Using Einstein Operations and Its Application to Decision Making
Harish Garg1

Abstract:

The objective of this article is to extend and present an idea related to weighted aggregated operators from fuzzy to Pythagorean fuzzy sets PFSs. The main feature of the PFS is to relax the condition that the sum of the degree of membership functions is less than one with the square sum of the degree of membership functions ... The objective of this article is to extend and present an idea related to weighted aggregated operators from fuzzy to Pythagorean fuzzy sets PFSs. The main feature of the PFS is to relax the condition that the sum of the degree of membership functions is less than one with the square sum of the degree of membership functions is less than one. Under these environments, aggregator operators, namely, Pythagorean fuzzy Einstein weighted averaging PFEWA, Pythagorean fuzzy Einstein ordered weighted averaging PFEOWA, generalized Pythagorean fuzzy Einstein weighted averaging GPFEWA, and generalized Pythagorean fuzzy Einstein ordered weighted averaging GPFEOWA, are proposed in this article. Some desirable properties corresponding to it have also been investigated. Furthermore, these operators are applied to decision-making problems in which experts provide their preferences in the Pythagorean fuzzy environment to show the validity, practicality, and effectiveness of the new approach. Finally, a systematic comparison between the existing work and the proposed work has been given. read more read less

Topics:

Generalized mean (64%)64% related to the paper, Fuzzy set operations (63%)63% related to the paper, Fuzzy number (63%)63% related to the paper, Type-2 fuzzy sets and systems (61%)61% related to the paper, Fuzzy classification (60%)60% related to the paper
517 Citations
Journal Article DOI: 10.1002/INT.20474
On distance and correlation measures of hesitant fuzzy information
Zeshui Xu1, Meimei Xia1

Abstract:

A hesitant fuzzy set, allowing the membership of an element to be a set of several possible values, is very useful to express people's hesitancy in daily life. In this paper, we define the distance and correlation measures for hesitant fuzzy information and then discuss their properties in detail. These measures are all defin... A hesitant fuzzy set, allowing the membership of an element to be a set of several possible values, is very useful to express people's hesitancy in daily life. In this paper, we define the distance and correlation measures for hesitant fuzzy information and then discuss their properties in detail. These measures are all defined under the assumption that the values in all hesitant fuzzy elements (the fundamental units of hesitant fuzzy sets) are arranged in an increasing order and two hesitant fuzzy elements have the same length when we compare them. We can find that the results, by using the developed distance measures, are the smallest ones among those when the values in two hesitant fuzzy elements are arranged in any permutations. In addition, the derived correlation coefficients are based on different linear relationships and may have different results. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc. read more read less

Topics:

Fuzzy set operations (62%)62% related to the paper, Fuzzy number (62%)62% related to the paper, Type-2 fuzzy sets and systems (61%)61% related to the paper, Fuzzy classification (60%)60% related to the paper, Fuzzy set (60%)60% related to the paper
461 Citations
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Frequently asked questions

1. Can I write Journal of Intelligent Systems in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Journal of Intelligent Systems guidelines and auto format it.

2. Do you follow the Journal of Intelligent Systems guidelines?

Yes, the template is compliant with the Journal of Intelligent Systems guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Journal of Intelligent Systems?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Journal of Intelligent Systems citation style.

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Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Journal of Intelligent Systems.

5. Can I use a manuscript in Journal of Intelligent Systems that I have written in MS Word?

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12. Is Journal of Intelligent Systems's impact factor high enough that I should try publishing my article there?

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13. What is Sherpa RoMEO Archiving Policy for Journal of Intelligent Systems?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Intelligent Systems. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Intelligent Systems?

The 5 most common citation types in order of usage for Journal of Intelligent Systems are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of Intelligent Systems Endnote style according to Elsevier guidelines.

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