Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: A systematic survey
Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …
and complex datasets but have been criticized as a black-box. This downside has recently …
Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature
PV de Campos Souza - Applied soft computing, 2020 - Elsevier
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 …
fuzzy systems and artificial neural networks, mainly focused on supervised methods for …
Autonomous learning for fuzzy systems: a review
As one of the three pillars in computational intelligence, fuzzy systems are a powerful
mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy …
mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy …
Spatial and temporal saliency based four-stream network with multi-task learning for action recognition
Action recognition is a challenging video understanding task for the following two reasons:(i)
the complex video background impairs the recognition of desirable actions, and (ii) the …
the complex video background impairs the recognition of desirable actions, and (ii) the …
Demagnetization fault diagnosis of permanent magnet synchronous motors using magnetic leakage signals
F Huang, X Zhang, G Qin, J Xie, J Peng… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
In most industrial applications, it is difficult to obtain complete demagnetization fault signals
of all conditions with labels for permanent magnet synchronous motor (PMSM), and motors …
of all conditions with labels for permanent magnet synchronous motor (PMSM), and motors …
Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation
Fine renal artery segmentation on abdominal CT angiography (CTA) image is one of the
most important tasks for kidney disease diagnosis and pre-operative planning. It will help …
most important tasks for kidney disease diagnosis and pre-operative planning. It will help …
MSMatch: Semisupervised multispectral scene classification with few labels
Supervised learning techniques are at the center of many tasks in remote sensing.
Unfortunately, these methods, especially recent deep learning methods, often require large …
Unfortunately, these methods, especially recent deep learning methods, often require large …
An incremental construction of deep neuro fuzzy system for continual learning of nonstationary data streams
Existing fuzzy neural networks (FNNs) are mostly developed under a shallow network
configuration having lower generalization power than those of deep structures. This article …
configuration having lower generalization power than those of deep structures. This article …
Mapping roofing with asbestos-containing material by using remote sensing imagery and machine learning-based image classification: A state-of-the-art review
Building roofing produced with asbestos-containing materials is a significant concern due to
its detrimental health hazard implications. Efficiently locating asbestos roofing is essential to …
its detrimental health hazard implications. Efficiently locating asbestos roofing is essential to …
Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
A Mahami, C Rahmoune, T Bettahar… - Advances in …, 2021 - journals.sagepub.com
In this paper, a novel noncontact and nonintrusive framework experimental method is used
for the monitoring and the diagnosis of a three phase's induction motor faults based on an …
for the monitoring and the diagnosis of a three phase's induction motor faults based on an …