Ensemble learning for remaining fatigue life prediction of structures with stochastic parameters: a data-driven approach
An effective approach is proposed to predict the remaining fatigue life (RFL) of structures
with stochastic parameters. The extended finite element method (XFEM) was firstly used to …
with stochastic parameters. The extended finite element method (XFEM) was firstly used to …
Automatic prognosis of lung cancer using heterogeneous deep learning models for nodule detection and eliciting its morphological features
Among cancers, lung cancer has the highest morbidity, and mortality rate. The survival
probability of lung cancer patients depends largely on an early diagnosis. For predicting …
probability of lung cancer patients depends largely on an early diagnosis. For predicting …
Assessing wetland habitat vulnerability in moribund Ganges delta using bivariate models and machine learning algorithms
The present study aims to measure wetland habitat vulnerability (WHV) in moribund deltaic
part of India using ten conditioning parameters eg, WPF, water depth, change in WPF …
part of India using ten conditioning parameters eg, WPF, water depth, change in WPF …
Pneumonia screening on chest X-rays with optimized ensemble model
S Nalluri, R Sasikala - Expert Systems with Applications, 2024 - Elsevier
Pneumonia is a lung illness that may result from a variety of various viral diseases and may
be lethal. It might be difficult to diagnose and treat pneumonia on chest X-ray pictures …
be lethal. It might be difficult to diagnose and treat pneumonia on chest X-ray pictures …
Tri-regularized nonnegative matrix tri-factorization for co-clustering
The objective of co-clustering is to simultaneously identify blocks of similarity between the
sample set and feature set. Co-clustering has become a widely used technique in data …
sample set and feature set. Co-clustering has become a widely used technique in data …
Enhancing nonlinear dynamics analysis of railway vehicles with artificial intelligence: a state-of-the-art review
Z Tang, Y Hu, Z Qu - Nonlinear Dynamics, 2024 - Springer
Railway vehicle dynamics involves modelling, simulating, and analysing the motion and
interaction of rail vehicles under external force, exhibiting numerous nonlinear behaviours …
interaction of rail vehicles under external force, exhibiting numerous nonlinear behaviours …
Convolution-based linear discriminant analysis for functional data classification
GEC Guzman, A Fujita - Information Sciences, 2021 - Elsevier
Technological advances have allowed for the rise in more reliable and less expensive
sensors to collect data over time (eg, on temperature, heartbeat, and neural activity) …
sensors to collect data over time (eg, on temperature, heartbeat, and neural activity) …
Nonlinear fault detection for batch processes via improved chordal kernel tensor locality preserving projections
Y Zhou, K Xu, F He, D He - Control Engineering Practice, 2020 - Elsevier
The quality and stability of products are seriously influenced by the process conditions. A
large number of modern production processes can be considered as batch processes, with …
large number of modern production processes can be considered as batch processes, with …
Crop type discrimination using Geo-Stat Endmember extraction and machine learning algorithms
The identification of crop diversity in today's world is very crucial to ensure adaptation of the
crop with changing climate for better productivity as well as food security. Towards this …
crop with changing climate for better productivity as well as food security. Towards this …
Robust deep fuzzy K-means clustering for image data
X Wu, YF Yu, L Chen, W Ding, Y Wang - Pattern Recognition, 2024 - Elsevier
Image clustering is a difficult task with important application value in computer vision. The
key to this task is the quality of images features. Most of current clustering methods …
key to this task is the quality of images features. Most of current clustering methods …