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 …

Neuro-fuzzy systems have attracted the growing interest of researchers in various scientific
and engineering areas due to its effective learning and reasoning capabilities. The neuro …

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 …

Deep learning (DL) is an emerging and powerful paradigm that allows large-scale task-
driven feature learning from big data. However, typical DL is a fully deterministic model that …

Feature reduction is an important aspect of Big Data analytics on today's ever-larger
datasets. Rough sets are a classical method widely applied in attribute reduction. Most …

Modern life is increasingly influenced by networks, making cybersecurity a crucial area of
study. However, due to their few resources and varied makeup, they are more vulnerable to …

The main reason of the extensive usage of the fuzzy systems in many branches of science is
their approximation ability. In this paper, an interval type-3 fuzzy system (IT3FS) is proposed …

In today's industrial systems, economic markets, life and health-care sciences fuzzy systems
play an important role in many application scenarios such as system identification, fault …

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 …

Novel information processing techniques are being actively explored to overcome
fundamental limitations associated with CMOS scaling. A new paradigm of adaptive …