… spectroscopic data and based on a convolutional neural network (CNN) architecture. For …
x has 1 target value y, the output layer is formed by just 1 neuron and its output y (s L + 1 ) is …

… two one-dimensional (1D) convolutional neural networks (CNNs) for predicting dominant
wind speed and direction for the temporal wind dataset. The proposed 1D Single CNN (1DS) …

… system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design
to fuse both feature extraction and classification blocks into a single and compact learning …

… of one-dimensional convolutional neural networks in processing one-dimensional data. In …
) [42] cyclic neural network widely used in one-dimensional signal processing for comparison. …

… of deep learning. In this research we used one dimensional convolutional neural networks
(1D CNN), which works quite well on textual data in order to identify network intrusions and …

… is more suitable to one dimensional traffic data. By contrast, the 2D-CNN’s advantage of
learning two dimensional spatial features is not obvious when one dimensional encrypted traffic …

… paper focuses on developing a convolutional neural network to learn features directly from
… , the results show that the one-dimensional convolutional neural network (1-DCNN) model …

… Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) …
In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast …

… Deep learning has ushered in many breakthroughs in vision-based detection via convolutional
neural networks (… Thus, this study proposes a simple one-dimensional CNN that detects …

… In this study, we propose a deep convolutional neural network to forecast monthly rainfall
for a selected location. One dimensional convolutional neural networks are explained first then …