Example of International Journal of Machine Learning and Cybernetics format
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Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format
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Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format Example of International Journal of Machine Learning and Cybernetics format
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open access Open Access ISSN: 18688071 e-ISSN: 1868808X

International Journal of Machine Learning and Cybernetics — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Software #70 of 389 up up by 65 ranks
Computer Vision and Pattern Recognition #17 of 85 up up by 7 ranks
Artificial Intelligence #47 of 227 up up by 23 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 736 Published Papers | 5291 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 06/07/2020
Insights & related journals
General info
Top papers
Popular templates
Get started guide
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FAQ

Journal Performance & Insights

  • CiteRatio
  • SJR
  • SNIP

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

7.2

20% from 2019

CiteRatio for International Journal of Machine Learning and Cybernetics from 2016 - 2020
Year Value
2020 7.2
2019 6.0
2018 5.0
2017 3.5
2016 3.3
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 20% 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.

0.681

13% from 2019

SJR for International Journal of Machine Learning and Cybernetics from 2016 - 2020
Year Value
2020 0.681
2019 0.782
2018 0.786
2017 0.7
2016 0.659
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 13% 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.

1.299

12% from 2019

SNIP for International Journal of Machine Learning and Cybernetics from 2016 - 2020
Year Value
2020 1.299
2019 1.471
2018 1.361
2017 1.309
2016 1.263
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has decreased by 12% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Related Journals

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recommended Recommended

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CiteRatio: 15.7 | SJR: 1.492 | SNIP: 3.419
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CiteRatio: 6.7 | SJR: 0.669 | SNIP: 1.739
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IEEE

CiteRatio: 44.2 | SJR: 3.811 | SNIP: 11.215
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CiteRatio: 11.4 | SJR: 1.005 | SNIP: 2.547

International Journal of Machine Learning and Cybernetics

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Springer

International Journal of Machine Learning and Cybernetics

Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life. Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems. The merging of...... Read More

Computer Vision and Pattern Recognition

Software

Artificial Intelligence

Computer Science

i
Last updated on
06 Jul 2020
i
ISSN
1868-8071
i
Impact Factor
Very High - 3.922
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM (1982) Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B 25(7):4515_x0015_ 4532, URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1007/S13042-011-0019-Y
Extreme learning machines: a survey
Guang-Bin Huang1, Dianhui Wang2, Yuan Lan1

Abstract:

Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learn... Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM. read more read less

Topics:

Extreme learning machine (55%)55% related to the paper, Computational intelligence (52%)52% related to the paper, Artificial neural network (52%)52% related to the paper
1,578 Citations
Journal Article DOI: 10.1007/S13042-010-0001-0
Understanding bag-of-words model: A statistical framework
Yin Zhang1, Rong Jin2, Zhi-Hua Zhou1

Abstract:

The bag-of-words model is one of the most popular representation methods for object categorization. The key idea is to quantize each extracted key point into one of visual words, and then represent each image by a histogram of the visual words. For this purpose, a clustering algorithm (e.g., K-means), is generally used for ge... The bag-of-words model is one of the most popular representation methods for object categorization. The key idea is to quantize each extracted key point into one of visual words, and then represent each image by a histogram of the visual words. For this purpose, a clustering algorithm (e.g., K-means), is generally used for generating the visual words. Although a number of studies have shown encouraging results of the bag-of-words representation for object categorization, theoretical studies on properties of the bag-of-words model is almost untouched, possibly due to the difficulty introduced by using a heuristic clustering process. In this paper, we present a statistical framework which generalizes the bag-of-words representation. In this framework, the visual words are generated by a statistical process rather than using a clustering algorithm, while the empirical performance is competitive to clustering-based method. A theoretical analysis based on statistical consistency is presented for the proposed framework. Moreover, based on the framework we developed two algorithms which do not rely on clustering, while achieving competitive performance in object categorization when compared to clustering-based bag-of-words representations. read more read less

Topics:

Cluster analysis (66%)66% related to the paper, Conceptual clustering (65%)65% related to the paper, Fuzzy clustering (65%)65% related to the paper, Correlation clustering (64%)64% related to the paper, Bag-of-words model in computer vision (63%)63% related to the paper
View PDF
582 Citations
Journal Article DOI: 10.1007/S13042-010-0007-7
Multiple classifier systems for robust classifier design in adversarial environments
Battista Biggio1, Giorgio Fumera1, Fabio Roli1

Abstract:

Pattern recognition systems are increasingly being used in adversarial environments like network intrusion detection, spam filtering and biometric authentication and verification systems, in which an adversary may adaptively manipulate data to make a classifier ineffective. Current theory and design methods of pattern recogni... Pattern recognition systems are increasingly being used in adversarial environments like network intrusion detection, spam filtering and biometric authentication and verification systems, in which an adversary may adaptively manipulate data to make a classifier ineffective. Current theory and design methods of pattern recognition systems do not take into account the adversarial nature of such kind of applications. Their extension to adversarial settings is thus mandatory, to safeguard the security and reliability of pattern recognition systems in adversarial environments. In this paper we focus on a strategy recently proposed in the literature to improve the robustness of linear classifiers to adversarial data manipulation, and experimentally investigate whether it can be implemented using two well known techniques for the construction of multiple classifier systems, namely, bagging and the random subspace method. Our results provide some hints on the potential usefulness of classifier ensembles in adversarial classification tasks, which is different from the motivations suggested so far in the literature. read more read less

Topics:

Classifier (UML) (53%)53% related to the paper, Random subspace method (51%)51% related to the paper, Robustness (computer science) (50%)50% related to the paper
View PDF
187 Citations
open accessOpen access Journal Article DOI: 10.1007/S13042-016-0505-3
A ranking method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems

Abstract:

The concept of a single valued neutrosophic number (SVN-number) is of importance for quantifying an ill-known quantity and the ranking of SVN-numbers is a very difficult problem in multi-attribute decision making problems. The aim of this paper is to present a methodology for solving multi-attribute decision making problems w... The concept of a single valued neutrosophic number (SVN-number) is of importance for quantifying an ill-known quantity and the ranking of SVN-numbers is a very difficult problem in multi-attribute decision making problems. The aim of this paper is to present a methodology for solving multi-attribute decision making problems with SVN-numbers. Therefore, we firstly defined the concepts of cut sets of SVN-numbers and then applied to single valued trapezoidal neutrosophic numbers (SVTN-numbers) and triangular neutrosophic numbers (SVTrN-numbers). Then, we proposed the values and ambiguities of the truth-membership function, indeterminacy-membership function and falsity-membership function for a SVN-numbers and studied some desired properties. Also, we developed a ranking method by using the concept of values and ambiguities, and applied to multi-attribute decision making problems in which the ratings of alternatives on attributes are expressed with SVTN-numbers. read more read less

Topics:

Ranking (52%)52% related to the paper
173 Citations
Journal Article DOI: 10.1007/S13042-017-0705-5
A review of hand gesture and sign language recognition techniques
Ming Jin Cheok1, Zaid Omar1, Mohamed Hisham Jaward2

Abstract:

Hand gesture recognition serves as a key for overcoming many difficulties and providing convenience for human life. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. One specific field of interest is sign language recognition. This paper provides a thorou... Hand gesture recognition serves as a key for overcoming many difficulties and providing convenience for human life. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. One specific field of interest is sign language recognition. This paper provides a thorough review of state-of-the-art techniques used in recent hand gesture and sign language recognition research. The techniques reviewed are suitably categorized into different stages: data acquisition, pre-processing, segmentation, feature extraction and classification, where the various algorithms at each stage are elaborated and their merits compared. Further, we also discuss the challenges and limitations faced by gesture recognition research in general, as well as those exclusive to sign language recognition. Overall, it is hoped that the study may provide readers with a comprehensive introduction into the field of automated gesture and sign language recognition, and further facilitate future research efforts in this area. read more read less

Topics:

Gesture recognition (75%)75% related to the paper, Sign language (65%)65% related to the paper, Sketch recognition (62%)62% related to the paper, Gesture (60%)60% related to the paper, Pattern recognition (psychology) (54%)54% related to the paper
171 Citations
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International Journal of Machine Learning and Cybernetics format uses SPBASIC 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 International Journal of Machine Learning and Cybernetics 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 International Journal of Machine Learning and Cybernetics 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 International Journal of Machine Learning and Cybernetics'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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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 International Journal of Machine Learning and Cybernetics Endnote style, according to springer guidelines.

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