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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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
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Related Journals

open access Open Access
recommended Recommended

Elsevier

Quality:  
High
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

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SJR: 3.811
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Quality:  
High
CiteRatio: 11.4
SJR: 1.005
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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.

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

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

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

  • CiteRatio of this journal has increased by 20% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 13% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

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

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
1,767 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
923 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
344 Citations
Journal Article DOI: 10.1007/S13042-019-01053-X
Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm
Ali Wagdy Mohamed1, Ali Wagdy Mohamed2, Anas A. Hadi3, Ali Khater Mohamed

Abstract:

This paper proposes a novel nature-inspired algorithm called Gaining Sharing Knowledge based Algorithm (GSK) for solving optimization problems over continuous space. The GSK algorithm mimics the process of gaining and sharing knowledge during the human life span. It is based on two vital stages, junior gaining and sharing pha... This paper proposes a novel nature-inspired algorithm called Gaining Sharing Knowledge based Algorithm (GSK) for solving optimization problems over continuous space. The GSK algorithm mimics the process of gaining and sharing knowledge during the human life span. It is based on two vital stages, junior gaining and sharing phase and senior gaining and sharing phase. The present work mathematically models these two phases to achieve the process of optimization. In order to verify and analyze the performance of GSK, numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. Besides, the GSK algorithm has been applied to solve the set of real world optimization problems proposed for the IEEE-CEC2011 evolutionary algorithm competition. A comparison with 10 state-of-the-art and recent metaheuristic algorithms are executed. Experimental results indicate that in terms of robustness, convergence and quality of the solution obtained, GSK is significantly better than, or at least comparable to state-of-the-art approaches with outstanding performance in solving optimization problems especially with high dimensions. read more read less

Topics:

Evolutionary algorithm (57%)57% related to the paper, Optimization problem (56%)56% related to the paper
258 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
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208 Citations
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International Journal of Machine Learning and Cybernetics format uses SPBASIC citation style.

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Frequently asked questions

1. Can I write International Journal of Machine Learning and Cybernetics in LaTeX?

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

2. Do you follow the International Journal of Machine Learning and Cybernetics guidelines?

Yes, the template is compliant with the International Journal of Machine Learning and Cybernetics 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 International Journal of Machine Learning and Cybernetics?

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 International Journal of Machine Learning and Cybernetics 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 International Journal of Machine Learning and Cybernetics.

5. Can I use a manuscript in International Journal of Machine Learning and Cybernetics 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 International Journal of Machine Learning and Cybernetics that you can download at the end.

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7. Where can I find the template for the International Journal of Machine Learning and Cybernetics?

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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.

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SciSpace's International Journal of Machine Learning and Cybernetics 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.

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12. Is International Journal of Machine Learning and Cybernetics'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 International Journal of Machine Learning and Cybernetics?

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 International Journal of Machine Learning and Cybernetics. 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 International Journal of Machine Learning and Cybernetics?

The 5 most common citation types in order of usage for International Journal of Machine Learning and Cybernetics 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 International Journal of Machine Learning and Cybernetics?

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 International Journal of Machine Learning and Cybernetics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download International Journal of Machine Learning and Cybernetics 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 International Journal of Machine Learning and Cybernetics Endnote style according to Elsevier guidelines.

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