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
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
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Related Journals

open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 5.8
SJR: 0.91
SNIP: 2.575
open access Open Access

Hindawi

Quality:  
High
CiteRatio: 5.0
SJR: 0.371
SNIP: 1.169
open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 19.8
SJR: 2.882
SNIP: 3.86

Journal Performance & Insights

Impact Factor

CiteRatio

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.

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

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

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

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

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)

Source Normalized Impact per Paper (SNIP)

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.

1.368

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

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

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

insights Insights

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

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
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
elsarticle-num
i
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.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
1,629 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
1,571 Citations
Journal Article DOI: 10.1016/J.ESWA.2016.12.035
Learning from class-imbalanced data
Guo Haixiang1, Li Yijing1, Jennifer Shang2, Gu Mingyun1, Huang Yuanyue1, Gong Bing3

Abstract:

527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are dis... 527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields. read more read less

Topics:

Rare events (54%)54% related to the paper
1,448 Citations
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
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1,357 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
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1,316 Citations
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Expert Systems with Applications format uses elsarticle-num citation style.

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Frequently asked questions

1. Can I write Expert Systems with Applications in LaTeX?

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

2. Do you follow the Expert Systems with Applications guidelines?

Yes, the template is compliant with the Expert Systems with Applications 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 Expert Systems with Applications?

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 Expert Systems with Applications citation style.

4. Can I use the Expert Systems with Applications templates for free?

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 Expert Systems with Applications.

5. Can I use a manuscript in Expert Systems with Applications that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Expert Systems with Applications that you can download at the end.

6. How long does it usually take you to format my papers in Expert Systems with Applications?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Expert Systems with Applications.

7. Where can I find the template for the Expert Systems with Applications?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Expert Systems with Applications's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Expert Systems with Applications's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Expert Systems with Applications an online tool or is there a desktop version?

SciSpace's Expert Systems with Applications is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Expert Systems with Applications?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Expert Systems with Applications?”

11. What is the output that I would get after using Expert Systems with Applications?

After writing your paper autoformatting in Expert Systems with Applications, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Expert Systems with Applications's impact factor high enough that I should try publishing my article there?

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 these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Expert Systems with Applications?

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 Expert Systems with Applications. 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 Expert Systems with Applications?

The 5 most common citation types in order of usage for Expert Systems with Applications are:.

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

15. How do I submit my article to the Expert Systems with Applications?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Expert Systems with Applications's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Expert Systems with Applications in Endnote format?

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 Expert Systems with Applications Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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