Example of Knowledge-Based Systems format
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Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format
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Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format Example of Knowledge-Based Systems format
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Knowledge-Based Systems — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Management Information Systems #6 of 114 down down by 1 rank
Information Systems and Management #9 of 125 down down by 3 ranks
Artificial Intelligence #16 of 227 down down by 1 rank
Software #31 of 389 down down by 8 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1894 Published Papers | 21478 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 03/06/2020
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Related Journals

open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 12.1
SJR: 1.524
SNIP: 2.585
open access Open Access
recommended Recommended

IEEE

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

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.

11.3

3% from 2019

CiteRatio for Knowledge-Based Systems from 2016 - 2020
Year Value
2020 11.3
2019 11.7
2018 10.1
2017 8.6
2016 8.2
graph view Graph view
table view Table view

1.587

10% from 2019

SJR for Knowledge-Based Systems from 2016 - 2020
Year Value
2020 1.587
2019 1.754
2018 1.46
2017 1.378
2016 1.709
graph view Graph view
table view Table view

2.89

0% from 2019

SNIP for Knowledge-Based Systems from 2016 - 2020
Year Value
2020 2.89
2019 2.902
2018 2.757
2017 2.353
2016 2.714
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 3% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

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

insights Insights

  • SNIP of this journal has decreased by 0% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Knowledge-Based Systems

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Elsevier

Knowledge-Based Systems

Knowledge-Based Systems is the international, interdisciplinary and applications-oriented journal on KBS. Knowledge-Based Systems focuses on systems that use knowledge-based techniques to support human decision-making, learning and action. Such systems are capable of cooperati...... Read More

Management Information Systems

Software

Artificial Intelligence

Information Systems and Management

Business, Management and Accounting

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Last updated on
03 Jun 2020
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ISSN
0950-7051
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Impact Factor
High - 2.603
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Open Access
No
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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
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Citation Type
Numbered
[25]
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Bibliography Example
G. E. Blonder, M. Tinkham, T. M. Klapwijk, Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion, Phys. Rev. B 25 (7) (1982) 4515–4532. URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1016/J.KNOSYS.2013.03.012
Recommender systems survey
Jesús Bobadilla1, Fernando Ortega1, Antonio Hernando1, Abraham Gutiérrez1
01 Jul 2013 - Knowledge Based Systems

Abstract:

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provid... Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance. read more read less

Topics:

Recommender system (73%)73% related to the paper, Collaborative filtering (68%)68% related to the paper, Cold start (67%)67% related to the paper, Personally identifiable information (51%)51% related to the paper
View PDF
2,217 Citations
Journal Article DOI: 10.1016/J.KNOSYS.2015.07.006
Moth-flame optimization algorithm
Seyedali Mirjalili1
01 Nov 2015 - Knowledge Based Systems

Abstract:

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective me... In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html. read more read less

Topics:

Population-based incremental learning (60%)60% related to the paper, Meta-optimization (59%)59% related to the paper, Stochastic optimization (53%)53% related to the paper, Constrained optimization (51%)51% related to the paper
1,649 Citations
Journal Article DOI: 10.1016/J.KNOSYS.2015.12.022
SCA: A Sine Cosine Algorithm for solving optimization problems
Seyedali Mirjalili1, Seyedali Mirjalili2
15 Mar 2016 - Knowledge Based Systems

Abstract:

This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine function... This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html . read more read less

Topics:

Test functions for optimization (60%)60% related to the paper, Local optimum (57%)57% related to the paper, Optimization problem (56%)56% related to the paper, Population (51%)51% related to the paper, Test case (51%)51% related to the paper
1,571 Citations
Journal Article DOI: 10.1016/0950-7051(96)81920-4
Survey and critique of techniques for extracting rules from trained artificial neural networks
Robert Andrews1, Joachim Diederich1, Alan Tickle1
01 Dec 1995 - Knowledge Based Systems

Abstract:

It is becoming increasingly apparent that, without some form of explanation capability, the full potential of trained artificial neural networks (ANNs) may not be realised. This survey gives an overview of techniques developed to redress this situation. Specifically, the survey focuses on mechanisms, procedures, and algorithm... It is becoming increasingly apparent that, without some form of explanation capability, the full potential of trained artificial neural networks (ANNs) may not be realised. This survey gives an overview of techniques developed to redress this situation. Specifically, the survey focuses on mechanisms, procedures, and algorithms designed to insert knowledge into ANNs (knowledge initialisation), extract rules from trained ANNs (rule extraction), and utilise ANNs to refine existing rule bases (rule refinement). The survey also introduces a new taxonomy for classifying the various techniques, discusses their modus operandi, and delineates criteria for evaluating their efficacy. read more read less

Topics:

Artificial neural network (52%)52% related to the paper
1,156 Citations
open accessOpen access Journal Article DOI: 10.1016/J.KNOSYS.2018.03.022
Graph embedding techniques, applications, and performance: A survey
Palash Goyal1, Emilio Ferrara1
01 Jul 2018 - Knowledge Based Systems

Abstract:

Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform the analysis. Recently, met... Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform the analysis. Recently, methods which use the representation of graph nodes in vector space have gained traction from the research community. In this survey, we provide a comprehensive and structured analysis of various graph embedding techniques proposed in the literature. We first introduce the embedding task and its challenges such as scalability, choice of dimensionality, and features to be preserved, and their possible solutions. We then present three categories of approaches based on factorization methods, random walks, and deep learning, with examples of representative algorithms in each category and analysis of their performance on various tasks. We evaluate these state-of-the-art methods on a few common datasets and compare their performance against one another. Our analysis concludes by suggesting some potential applications and future directions. We finally present the open-source Python library we developed, named GEM (Graph Embedding Methods, available at https://github.com/palash1992/GEM ), which provides all presented algorithms within a unified interface to foster and facilitate research on the topic. read more read less

Topics:

Graph embedding (63%)63% related to the paper, Embedding (54%)54% related to the paper, Deep learning (52%)52% related to the paper
View PDF
1,097 Citations
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Knowledge-Based Systems format uses elsarticle-num citation style.

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Frequently asked questions

1. Can I write Knowledge-Based 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 Knowledge-Based Systems guidelines and auto format it.

2. Do you follow the Knowledge-Based Systems guidelines?

Yes, the template is compliant with the Knowledge-Based 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 Knowledge-Based 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 Knowledge-Based Systems citation style.

4. Can I use the Knowledge-Based Systems 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 Knowledge-Based Systems.

5. Can I use a manuscript in Knowledge-Based Systems 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 Knowledge-Based Systems that you can download at the end.

6. How long does it usually take you to format my papers in Knowledge-Based Systems?

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

7. Where can I find the template for the Knowledge-Based Systems?

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 Knowledge-Based Systems'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 Knowledge-Based Systems'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. Knowledge-Based Systems an online tool or is there a desktop version?

SciSpace's Knowledge-Based Systems 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 Knowledge-Based Systems?

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 Knowledge-Based Systems?”

11. What is the output that I would get after using Knowledge-Based Systems?

After writing your paper autoformatting in Knowledge-Based Systems, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Knowledge-Based Systems'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 Knowledge-Based 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 Knowledge-Based 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 Knowledge-Based Systems?

The 5 most common citation types in order of usage for Knowledge-Based Systems 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 Knowledge-Based Systems?

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 Knowledge-Based Systems's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Knowledge-Based Systems 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 Knowledge-Based Systems 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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