Example of Expert Systems with Applications format
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Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format
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Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format Example of Expert Systems with Applications format
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open access Open Access ISSN: 9574174
recommended Recommended

Expert Systems with Applications — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Engineering (all) #5 of 297 down down by 1 rank
Computer Science Applications #30 of 693 down down by 14 ranks
Artificial Intelligence #12 of 227 -
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 2710 Published Papers | 34460 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 01/06/2020
Insights & related journals
General info
Top papers
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

5.452

27% from 2018

Impact factor for Expert Systems with Applications from 2016 - 2019
Year Value
2019 5.452
2018 4.292
2017 3.768
2016 3.928
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 27% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

12.7

15% from 2019

CiteRatio for Expert Systems with Applications from 2016 - 2020
Year Value
2020 12.7
2019 11.0
2018 10.2
2017 9.5
2016 8.2
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 15% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

1.368

8% from 2019

SJR for Expert Systems with Applications from 2016 - 2020
Year Value
2020 1.368
2019 1.494
2018 1.19
2017 1.271
2016 1.343
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 8% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

3.079

2% from 2019

SNIP for Expert Systems with Applications from 2016 - 2020
Year Value
2020 3.079
2019 3.139
2018 2.862
2017 2.538
2016 2.514
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has decreased by 2% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Related Journals

open access Open Access ISSN: 0142694X
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CiteRatio: 5.8 | SJR: 0.91 | SNIP: 2.575
open access Open Access ISSN: 16879724 e-ISSN: 16879732

Hindawi

CiteRatio: 5.0 | SJR: 0.371 | SNIP: 1.169
open access Open Access ISSN: 21682291
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IEEE

CiteRatio: 9.5 | SJR: 0.873 | SNIP: 2.302
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CiteRatio: 19.8 | SJR: 2.882 | SNIP: 3.86

Expert Systems with Applications

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Elsevier

Expert Systems with Applications

EXPERT SYSTEMS WITH APPLICATIONS is a refereed international journal whose focus is on exchanging information relating to expert and intelligent systems applied in industry, government, and universities worldwide. The thrust of the journal is to publish papers dealing with the...... Read More

Engineering

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Last updated on
01 Jun 2020
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ISSN
0957-4174
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Impact Factor
High - 2.362
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
elsarticle-num
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Citation Type
Author Year
(Blonder et al., 1982)
i
Bibliography Example
Blonder, G. E., Tinkham, M., & Klapwijk, T. M. (1982). Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and super- current conversion. Phys. Rev. B, 25(7):4515–4532.

Top papers written in this journal

Journal Article DOI: 10.1016/J.ESWA.2006.04.005
Educational data mining: A survey from 1995 to 2005
Cristóbal Romero1, Sebastián Ventura1

Abstract:

Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive ... Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area. read more read less

Topics:

Educational data mining (77%)77% related to the paper, Web mining (73%)73% related to the paper, Concept mining (67%)67% related to the paper, Data stream mining (66%)66% related to the paper, Association rule learning (55%)55% related to the paper
View PDF
1,256 Citations
Journal Article DOI: 10.1016/J.ESWA.2008.01.039
A simple and fast algorithm for K-medoids clustering
Hae-Sang Park1, Chi-Hyuck Jun1

Abstract:

This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting initial medoids. The proposed algorithm calculates the distance matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm, we use some rea... This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting initial medoids. The proposed algorithm calculates the distance matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm, we use some real and artificial data sets and compare with the results of other algorithms in terms of the adjusted Rand index. Experimental results show that the proposed algorithm takes a significantly reduced time in computation with comparable performance against the partitioning around medoids. read more read less

Topics:

Medoid (72%)72% related to the paper, Canopy clustering algorithm (61%)61% related to the paper, Ramer–Douglas–Peucker algorithm (60%)60% related to the paper, FSA-Red Algorithm (60%)60% related to the paper, k-means clustering (59%)59% related to the paper
1,246 Citations
Journal Article DOI: 10.1016/J.ESWA.2005.09.024
A GA-based feature selection and parameters optimizationfor support vector machines
Cheng-Lung Huang1, Chieh-Jen Wang2

Abstract:

Support Vector Machines, one of the new techniques for pattern classification, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the classification accuracy. Feature selection is another factor that impacts classification accuracy. The objective of this res... Support Vector Machines, one of the new techniques for pattern classification, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the classification accuracy. Feature selection is another factor that impacts classification accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM classification accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem. We tried several real-world datasets using the proposed GA-based approach and the Grid algorithm, a traditional method of performing parameters searching. Compared with the Grid algorithm, our proposed GA-based approach significantly improves the classification accuracy and has fewer input features for support vector machines. q 2005 Elsevier Ltd. All rights reserved. read more read less

Topics:

Linear classifier (62%)62% related to the paper, Structured support vector machine (60%)60% related to the paper, Relevance vector machine (60%)60% related to the paper, Feature selection (59%)59% related to the paper, Support vector machine (58%)58% related to the paper
View PDF
1,201 Citations
Journal Article DOI: 10.1016/J.ESWA.2012.05.056
Review: A state-of the-art survey of TOPSIS applications
Majid Behzadian, S. Khanmohammadi Otaghsara1, Morteza Yazdani1, Joshua Ignatius2

Abstract:

Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Or... Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors' nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners. read more read less

Topics:

TOPSIS (65%)65% related to the paper, Literature survey (53%)53% related to the paper, Multiple-criteria decision analysis (52%)52% related to the paper
1,162 Citations
Journal Article DOI: 10.1016/J.ESWA.2009.03.039
A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method
Fatih Emre Boran1, Serkan Genç1, Mustafa Kurt1, Diyar Akay1

Abstract:

Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain. On the other hand, it is a hard ... Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain. On the other hand, it is a hard problem since supplier selection is typically a multi criteria group decision-making problem involving several conflicting criteria on which decision maker's knowledge is usually vague and imprecise. In this study, TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate supplier in group decision making environment. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers for rating the importance of criteria and alternatives. Finally, a numerical example for supplier selection is given to illustrate application of intuitionistic fuzzy TOPSIS method. read more read less

Topics:

TOPSIS (61%)61% related to the paper, Group decision-making (54%)54% related to the paper
1,087 Citations
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Expert Systems with Applications format uses elsarticle-num citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the Expert Systems with Applications guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Expert Systems with Applications citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Expert Systems with Applications's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

To be honest, the answer is NO. The impact factor is one of the many elements that determine the quality of a journal. Few of those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo 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.

The 5 most common citation types in order of usage 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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After uploading your paper on SciSpace, you would see a button to request a journal submission service for Expert Systems with Applications.

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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.

SciSpace would allow download of your references in Expert Systems with Applications Endnote style, according to elsevier guidelines.

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