On hyperparameter optimization of machine learning algorithms: Theory and practice
Abstract Machine learning algorithms have been used widely in various applications and
areas. To fit a machine learning model into different problems, its hyper-parameters must be …
areas. To fit a machine learning model into different problems, its hyper-parameters must be …
A systematic review on data scarcity problem in deep learning: solution and applications
Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …
applications. Deep learning models require a large amount of data to train the model. In …
A survey on data‐efficient algorithms in big data era
A Adadi - Journal of Big Data, 2021 - Springer
The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …
many application domains do not have access to big data because acquiring data involves a …
Performance evaluation of deep CNN-based crack detection and localization techniques for concrete structures
This paper proposes a customized convolutional neural network for crack detection in
concrete structures. The proposed method is compared to four existing deep learning …
concrete structures. The proposed method is compared to four existing deep learning …
Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions
Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of
practical importance. For this purpose, ensemble transfer convolutional neural networks …
practical importance. For this purpose, ensemble transfer convolutional neural networks …
[HTML][HTML] Predicting flood susceptibility using LSTM neural networks
Identifying floods and producing flood susceptibility maps are crucial steps for decision-
makers to prevent and manage disasters. Plenty of studies have used machine learning …
makers to prevent and manage disasters. Plenty of studies have used machine learning …
Recycling waste classification using optimized convolutional neural network
An automatic classification robot based on effective image recognition could help reduce
huge labors of recycling tasks. Convolutional neural network (CNN) model, such as …
huge labors of recycling tasks. Convolutional neural network (CNN) model, such as …
EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their …
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact
with the environment. Recent advancements in technology and machine learning algorithms …
with the environment. Recent advancements in technology and machine learning algorithms …
An efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural network
Photovoltaic (PV) power generation is one of the remarkable energy types to provide clean
and sustainable energy. Therefore, rapid fault detection and classification of PV modules …
and sustainable energy. Therefore, rapid fault detection and classification of PV modules …
A framework for breast cancer classification using multi-DCNNs
Background Deep learning (DL) is the fastest-growing field of machine learning (ML). Deep
convolutional neural networks (DCNN) are currently the main tool used for image analysis …
convolutional neural networks (DCNN) are currently the main tool used for image analysis …