A convolutional neural network (CNN) is a machine learning method under supervised learning. It not only has the advantages of high fault tolerance and self-learning ability of other traditional neural networks but also has the advantages of weight sharing, automatic feature extraction, and the ...
In supervised learning, a machine is trained with input data previously labeled by humans to predict the desired outcome such that it can solve classification and regression problems. However, this approach is time-consuming because it requires a considerable amount of data to be labeled manually. ...
Predicting curve progression during the initial visit is pivotal in the disease management of patients with adolescent idiopathic scoliosis (AIS)—identifying patients at high risk of progression is essential for timely and proactive interventions. Both radiological and clinical factors have been investigated...
transformation (FFT), Fuzzy logic, and Park's vector analysis. The latest development in artificial intelligence (AI) is 'transfer learning', which can detect failure patterns of different devices. This technique can be used instead of MCSA to find localized anomalies and has been suggested in p...
The complex consists of such facilities as TAIGA-IACT, TAIGA-HiSCORE, and a variety of others. The goal of the study is to introduce a deep learning-based technique for EAS axis reconstruction. A convolutional neural network (CNN) model is proposed, while HiSCORE events, consisting of time-...
While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified probabilistic model for learning the latent space of imaging data and performing supervised...
Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it i
In a few cases, malicious samples in the untrained dataset can be successfully detected, with an average detection rate of 99.62%. Then in 2021, Ahsan et al. [57] adopted the idea of stacked ensemble meta-learning based on the Dynamic Feature Selector (DFS), and integrated CNN + ...
integrated fully convolutional networks and conditional random fields for the segmentation of brain tumours [9]. Other studies used long-short term memory and residual convolutional neural network for the same task [10], [11]. Convolutional neural networks (CNN) are most frequently used in image ...
Herein the use of Convolutional Neural Networks (CNNs) is investigated to interpret fluorescence spectra and predict the formation of disinfection by-products during drinking water treatment. Using deep CNNs, mean absolute prediction error on a test set of data for total trihalomethanes, total ...