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International Journal of Machine Learning and Cybernetics

International Journal of Machine Learning and Cybernetics OnlineFirst articles

14-10-2020 | Original Article

A knowledge discovery and visualisation method for unearthing emotional states from physiological data

In this paper we propose a knowledge discovery and visualisation method for unearthing emotional states from physiological data typically available from wearable devices. In addition we investigate the viability of using a limited set of wearable …

14-10-2020 | Original Article

Dynamic pricing in profit-driven task assignment: a domain-of-influence based approach

The development of mobile Internet and sharing economy brings the prosperity of spatial crowdsourcing (SC). Pricing is a crucial step for SC platforms to solve the Profit-driven Task Assignment (PTA) problem to maximize their total profit.

14-10-2020 | Original Article

Matrix-based incremental updating approximations in multigranulation rough set under two-dimensional variation

Multigranulation rough set model (MGRS) uses multiple equivalence relations on the universe to calculate the approximations, which can solve problem in mutigranulation spaces. In practical applications, information systems often dynamically update …

12-10-2020 | Original Article

Spatiotemporal attention enhanced features fusion network for action recognition

In recent years, action recognition has become a popular and challenging task in computer vision. Nowadays, two-stream networks with appearance stream and motion stream can make judgment jointly and get excellent action classification results. But …

11-10-2020 | Original Article

Balanced Graph-based regularized semi-supervised extreme learning machine for EEG classification

Machine learning algorithms play a critical role in electroencephalograpy (EEG)-based brain-computer interface (BCI) systems. However, collecting labeled samples for classifier training and calibration is still difficult and time-consuming …

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About this 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 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
Additional information