[HTML][HTML] A survey on machine learning for recurring concept drifting data streams
AL Suárez-Cetrulo, D Quintana, A Cervantes - Expert Systems with …, 2023 - Elsevier
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 …
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …
Unsupervised learning methods for data-driven vibration-based structural health monitoring: a review
Structural damage detection using unsupervised learning methods has been a trending
topic in the structural health monitoring (SHM) research community during the past decades …
topic in the structural health monitoring (SHM) research community during the past decades …
SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification
The infection by SARS-CoV-2 which causes the COVID-19 disease has widely spread all
over the world since the beginning of 2020. On January 30, 2020 the World Health …
over the world since the beginning of 2020. On January 30, 2020 the World Health …
Towards explainable deep neural networks (xDNN)
In this paper, we propose an elegant solution that is directly addressing the bottlenecks of
the traditional deep learning approaches and offers an explainable internal architecture that …
the traditional deep learning approaches and offers an explainable internal architecture that …
On continuous health monitoring of bridges under serious environmental variability by an innovative multi-task unsupervised learning method
Abstract Design of an automated and continuous framework is of paramount importance to
structural health monitoring (SHM). This study proposes an innovative multi-task …
structural health monitoring (SHM). This study proposes an innovative multi-task …
Non-parametric empirical machine learning for short-term and long-term structural health monitoring
A Entezami, H Shariatmadar… - Structural Health …, 2022 - journals.sagepub.com
Early damage detection is an initial step of structural health monitoring. Thanks to recent
advances in sensing technology, the application of data-driven methods based on the …
advances in sensing technology, the application of data-driven methods based on the …
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 …
An evolving tinyml compression algorithm for iot environments based on data eccentricity
Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor
data at a very high pace, making it a challenge to collect and store the data. This scenario …
data at a very high pace, making it a challenge to collect and store the data. This scenario …
Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets
Glaucoma damages the optical nerve, which sends visual pictures to the brain, and results
in irreversible vision loss. This chronic infection is the second leading cause of permanent …
in irreversible vision loss. This chronic infection is the second leading cause of permanent …
Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery: A case study in a citrus …
This study aimed to compare and assess different Geographic Object-Based Image Analysis
(GEOBIA) and machine learning algorithms using unmanned aerial vehicles (UAVs) …
(GEOBIA) and machine learning algorithms using unmanned aerial vehicles (UAVs) …