International Journal of Machine Learning and Cybernetics

  in Journal   Posted on September 23, 2020

Journal Ranking & Metrics

G2R Score : 3.13
G2R H-Index : 11
JCR Impact Factor : 3.753
Scopus Citescore : 6
SCIMAGO SJR : 0.782
SCIMAGO H-index : 38
Guide2Research Overall Ranking : 343

Journal Information

ISSN : 1868-8071
Publisher :
Periodicity : Bimonthly
Editors-in-Chief : Xi-Zhao Wang, Daniel S. Yeung
Journal & Submission Website : https://www.springer.com/journal/13042

Top Scientists who published in this Journal

Number of top scientists* : 20
Documents published by top scientists* : 28
* Based on data published during the last three years.

Aims & Scope of the Journal

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 systemsPattern Recognition technology to support discovery of system-environment interactionControl of system-environment interactions Biochemical interaction in biological and biologically-inspired systemsLearning for improvement of communication schemes between systems

Closed Special Issues