Classification of brain signals for character recognition is a challenging task associated with
uncertainties. This study aims to present an Improved Type-2 Fuzzy Deep Learning (IT2FDL) …

The main issue of time-series prediction is to determine the grade of uncertainty in
knowledge, with its essential vagueness and haziness in complex problems. In this study, a …

The statistical attributes of the non-stationary problems such as air quality and other natural
phenomena frequently changed. Type-2 fuzzy logic is a robust and capable model to cope …

Prediction with high reliability is very important in solving real-world problems, especially
those that affect public health. The statistical properties of complex problems such as Covid …

In this study, a self-organizing interval type-2 fuzzy neural network (SOIT2FNN) based on the
Adaptive Quantum-behaved Particle Swarm Optimization (AQPSO) algorithm is proposed as …

This paper presents the trajectory-tracking control of transport robots for smart logistics using
a fuzzy controller. We propose a new method of designing tracking control using the speed …

Abstract This study presents Interval Type-2 Fuzzy Evolutionary models to manage
uncertainty in the process of uncertain time-series prediction. This study presents two type-2 …

Graph and hypergraph matching are fundamental problems in pattern analysis problems.
They are applied to various tasks requiring 2D and 3D feature matching, such as image …

Prediction is a very important problem that appears in many disciplines. Better weather
forecasts can save lives in the event of a catastrophic hurricane; better financial forecasts …

In this research, we first present some background on the sample size estimation for
conducting clinical trials, discussing the necessity of a computational enrichment criterion …