
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
Journal information
- Editors-in-Chief
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- Xi-Zhao Wang,
- Daniel S. Yeung
- Publishing model
- Hybrid (Transformative Journal). Learn about publishing Open Access with us
Journal metrics
- 3.753 (2019)
- Impact factor
- 3.140 (2019)
- Five year impact factor
- 79 days
- Submission to first decision
- 289 days
- Submission to acceptance
- 108,150 (2019)
- Downloads
Latest issue
Latest articles
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Fault detection of railway freight cars mechanical components based on multi-feature fusion convolutional neural network
Authors (first, second and last of 5)
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Uncertainty measurement for a fuzzy set-valued information system
Authors (first, second and last of 5)
About this journal
- Electronic ISSN
- 1868-808X
- Print ISSN
- 1868-8071
- Abstracted and indexed in
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- ACM Digital Library
- ANVUR
- CNKI
- Chemical Abstracts Service (CAS)
- DBLP
- Dimensions
- EBSCO Discovery Service
- EI Compendex
- Google Scholar
- INSPEC
- Institute of Scientific and Technical Information of China
- Japanese Science and Technology Agency (JST)
- Journal Citation Reports/Science Edition
- Naver
- OCLC WorldCat Discovery Service
- ProQuest Advanced Technologies & Aerospace Database
- ProQuest Central
- ProQuest SciTech Premium Collection
- ProQuest Technology Collection
- ProQuest-ExLibris Primo
- ProQuest-ExLibris Summon
- SCImago
- SCOPUS
- Science Citation Index Expanded (SciSearch)
- TD Net Discovery Service
- UGC-CARE List (India)
- WTI Frankfurt eG
- Copyright information