State of the art in structural health monitoring of offshore and marine structures
This paper deals with state of the art in structural health monitoring (SHM) methods in
offshore and marine structures. Most SHM methods have been developed for onshore …
offshore and marine structures. Most SHM methods have been developed for onshore …
Machine learning in perovskite solar cells: recent developments and future perspectives
Within a short period of time, perovskite solar cells (PSC) have attracted paramount research
interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC …
interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC …
Hybrid semantic segmentation for tunnel lining cracks based on Swin Transformer and convolutional neural network
Z Zhou, J Zhang, C Gong - Computer‐Aided Civil and …, 2023 - Wiley Online Library
In the field of tunnel lining crack identification, the semantic segmentation algorithms based
on convolution neural network (CNN) are extensively used. Owing to the inherent locality of …
on convolution neural network (CNN) are extensively used. Owing to the inherent locality of …
Automatic pixel‐level crack detection with multi‐scale feature fusion for slab tracks
Cracks are common defects in slab tracks, which can grow and expand over time, leading to
a deterioration of the mechanical properties of slab tracks and shortening service life …
a deterioration of the mechanical properties of slab tracks and shortening service life …
Heuristic and metaheuristic methods for the parallel unrelated machines scheduling problem: a survey
M Ɖurasević, D Jakobović - Artificial Intelligence Review, 2023 - Springer
Scheduling has an immense effect on various areas of human lives, be it though its
application in manufacturing and production industry, transportation, workforce allocation, or …
application in manufacturing and production industry, transportation, workforce allocation, or …
Multiclass seismic damage detection of buildings using quantum convolutional neural network
The traditional visual inspection technique for damage assessment of buildings immediately
after an earthquake can be time‐consuming, labor‐intensive, and risky. Numerous studies …
after an earthquake can be time‐consuming, labor‐intensive, and risky. Numerous studies …
[HTML][HTML] An experimental analysis of different deep learning based models for Alzheimer's disease classification using brain magnetic resonance images
Classification of Alzheimer's disease (AD) is one of the most challenging issues for
neurologists. Manual methods are time consuming and may not be accurate all the time …
neurologists. Manual methods are time consuming and may not be accurate all the time …
Convolutional neural network pruning based on multi-objective feature map selection for image classification
Deep convolutional neural networks (CNNs) are widely used for image classification. Deep
CNNs often require a large memory and abundant computation resources, limiting their …
CNNs often require a large memory and abundant computation resources, limiting their …
[HTML][HTML] Surrogate-assisted automatic evolving of dispatching rules for multi-objective dynamic job shop scheduling using genetic programming
Dispatching rules are simple but efficient heuristics to solve multi-objective job shop
scheduling problems, particularly useful to face the challenges of dynamic shop …
scheduling problems, particularly useful to face the challenges of dynamic shop …
Eight years of AutoML: categorisation, review and trends
Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …
applied in a large number of application domains. However, apart from the required …