SCI Journal

Impact Factor Database

International Journal of Machine Learning and Cybernetics

Basic Journal Info

Country

United States
Journal ISSN: 1868808X, 18688071
Publisher: Springer Science + Business Media
History: 2010-ongoing
Journal Hompage: Link
Note:
You can find more information about getting published on this journal here: https://www.editorialmanager.com/jmlc/default.aspx

Research Categories

International Journal of Machine Learning and Cybernetics

2-year
Impact Factor

4.262

3-year
Impact Factor

4.187

4-year
Impact Factor

3.871

Scope/Description:

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 the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data. The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area. The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications. New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC. Key research areas to be covered by the journal include: -Machine Learning for modeling interactions between systems -Pattern Recognition technology to support discovery of system-environment interaction -Control of system-environment interactions -Biochemical interaction in biological and biologically-inspired systems -Learning for improvement of communication schemes between systems

International Journal of Machine Learning and Cybernetics

2-year Impact Factor Trend
Note: impact factor data for reference only

International Journal of Machine Learning and Cybernetics

3-year Impact Factor Trend
Note: impact factor data for reference only

International Journal of Machine Learning and Cybernetics

4-year Impact Factor Trend
Note: impact factor data for reference only

Impact Factor

The impact factor (IF) or journal impact factor (JIF) of an academic journal is a scientometric factor based on the yearly average number of citations on articles published by a particular journal in the last two years. In other words, the impact factor of 2020 is the average of the number of cited publications divided by the citable publications of a journal. A journal impact factor is frequently used as a proxy for the relative importance of a journal within its field. Normally, journals with higher impact factors are often deemed to have more influence than those with lower ones. However, the science community has also noted that review articles typically are more citable than research articles.

(Read More: What is a good impact factor?)

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International Journal of Machine Learning and Cybernetics

Impact Factor History
  • 2019 Impact Factor 4.262
  • 2018 Impact Factor 3.947
  • 2017 Impact Factor 2.656
  • 2016 Impact Factor 1.981
  • 2015 Impact Factor 1.416
  • 2014 Impact Factor 2.340
  • 2013 Impact Factor 6.103
  • 2012 Impact Factor 7.758
  • 2011 Impact Factor 14.429
  • 2010 Impact Factor 0.000
  • 2009 Impact Factor NA
  • 2008 Impact Factor NA
  • 2007 Impact Factor NA
  • 2006 Impact Factor NA
  • 2005 Impact Factor NA
  • 2004 Impact Factor NA
  • 2003 Impact Factor NA
  • 2002 Impact Factor NA
  • 2001 Impact Factor NA
  • 2000 Impact Factor NA
Note: impact factor data for reference only

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Other Journal Impact Indicator

Any journal impact factor or scientometric indicator alone will not give you the full picture of a science journal. That’s why every year, scholars review current metrics to improve upon them and sometimes come up with new ones. There are also other factors to sider for example, H-Index, Self-Citation Ratio, SJR (SCImago Journal Rank Indicator) and SNIP (Source Normalized Impact per Paper). Researchers may also consider the practical aspect of a journal such as publication fees, acceptance rate, review speed.

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International Journal of Machine Learning and Cybernetics

H-Index

The h-index is an author-level metric that attempts to measure both the productivity and citation impact of the publications of a scientist or scholar. The index is based on the set of the scientist's most cited papers and the number of citations that they have received in other publications

30

International Journal of Machine Learning and Cybernetics

SCImago Journal Rank (SJR)

SCImago Journal Rank (SJR indicator) is a measure of scientific influence of scholarly journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from.

0.79