A systematic review on supervised and unsupervised machine learning algorithms for data science
Abstract Machine learning is as growing as fast as concepts such as Big data and the field of
data science in general. The purpose of the systematic review was to analyze scholarly …
data science in general. The purpose of the systematic review was to analyze scholarly …
Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review
Data-driven methods in structural health monitoring (SHM) is gaining popularity due to
recent technological advancements in sensors, as well as high-speed internet and cloud …
recent technological advancements in sensors, as well as high-speed internet and cloud …
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of
epilepsy. The EEG signal contains information about the electrical activity of the brain …
epilepsy. The EEG signal contains information about the electrical activity of the brain …
Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types
Computer vision‐based techniques were developed to overcome the limitations of visual
inspection by trained human resources and to detect structural damage in images remotely …
inspection by trained human resources and to detect structural damage in images remotely …
Automatic pixel‐level crack detection and measurement using fully convolutional network
The spatial characteristics of cracks are significant indicators to assess and evaluate the
health of existing buildings and infrastructures. However, the current manual crack …
health of existing buildings and infrastructures. However, the current manual crack …
Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences
Artificial intelligence (AI) and Machine learning (ML) train machines to achieve a high level
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
Efficient machine learning models for prediction of concrete strengths
In this study, an efficient implementation of machine learning models to predict compressive
and tensile strengths of high-performance concrete (HPC) is presented. Four predictive …
and tensile strengths of high-performance concrete (HPC) is presented. Four predictive …
Machine learning and deep learning in smart manufacturing: The smart grid paradigm
Industry 4.0 is the new industrial revolution. By connecting every machine and activity
through network sensors to the Internet, a huge amount of data is generated. Machine …
through network sensors to the Internet, a huge amount of data is generated. Machine …
Road damage detection and classification using deep neural networks with smartphone images
Research on damage detection of road surfaces using image processing techniques has
been actively conducted. This study makes three contributions to address road damage …
been actively conducted. This study makes three contributions to address road damage …
Efficient training of physics‐informed neural networks via importance sampling
Physics‐informed neural networks (PINNs) are a class of deep neural networks that are
trained, using automatic differentiation, to compute the response of systems governed by …
trained, using automatic differentiation, to compute the response of systems governed by …