This paper presents a review of the central theories involved in hybrid models based on
fuzzy systems and artificial neural networks, mainly focused on supervised methods for …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …

This special issue explores big data analytics and applications for logistics and supply chain
management by examining novel methods, practices, and opportunities. The articles present …

Recent advances in computational intelligent systems have focused on addressing complex
problems related to the dynamicity of the environments. In increasing number of real world …

Recognition of epileptic electroencephalogram (EEG) signals using machine learning
techniques is becoming popular. In general, the construction of intelligent epileptic EEG …

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy
inference systems (FIS) using five well known computational frameworks: genetic-fuzzy …

Most of the dynamics in real-world systems are compiled by shifts and drifts, which are
uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in …

Autonomous Learning Systems is the result of over a decade of focused research and
studies in this emerging area which spans a number of well-known and well-established …

The age of online data stream and dynamic environments results in the increasing demand
of advanced machine learning techniques to deal with concept drifts in large data streams …