Interpretation of intelligence in CNN-pooling processes: a methodological survey
N Akhtar, U Ragavendran - Neural computing and applications, 2020 - Springer
The convolutional neural network architecture has different components like convolution and
pooling. The pooling is crucial component placed after the convolution layer. It plays a vital …
pooling. The pooling is crucial component placed after the convolution layer. It plays a vital …
Deep concatenated residual network with bidirectional LSTM for one-hour-ahead wind power forecasting
This paper presents a deep residual network for improving time-series forecasting models,
indispensable to reliable and economical power grid operations, especially with high shares …
indispensable to reliable and economical power grid operations, especially with high shares …
A deep one-dimensional convolutional neural network for microplastics classification using Raman spectroscopy
W Zhang, W Feng, Z Cai, H Wang, Q Yan… - Vibrational …, 2023 - Elsevier
Microplastics have emerged as major global environmental contaminations. Finding
accurate and effective identification methods for microplastics is of great significance. In this …
accurate and effective identification methods for microplastics is of great significance. In this …
Deep independently recurrent neural network (indrnn)
Recurrent neural networks (RNNs) are known to be difficult to train due to the gradient
vanishing and exploding problems and thus difficult to learn long-term patterns and …
vanishing and exploding problems and thus difficult to learn long-term patterns and …
Multi-task learning model based on recurrent convolutional neural networks for citation sentiment and purpose classification
Automated citation analysis is a method of identifying sentiment and purpose of citations in
the citing works. Most of the existing approaches use machine learning techniques to boost …
the citing works. Most of the existing approaches use machine learning techniques to boost …
IndRNN based long-term temporal recognition in the spatial and frequency domain
This paper targets the SHL recognition challenge, which focuses on the location-
independent and user-independent activity recognition using smartphone sensors. To …
independent and user-independent activity recognition using smartphone sensors. To …
Smartphone-sensors based activity recognition using IndRNN
Human activity recognition based on the smartphone sensors has the potential to impact a
wide range of applications such as healthcare, smart home, and remote monitoring. For …
wide range of applications such as healthcare, smart home, and remote monitoring. For …
An explicit self-attention-based multimodality CNN in-loop filter for versatile video coding
The newest video coding standard, versatile video coding (VVC), has just been published
recently. While it greatly improves the performance over the last High Efficiency Video …
recently. While it greatly improves the performance over the last High Efficiency Video …
Narrow band time–frequency space matched passive detector for underwater signal
Y Haiyang, Z Zhichen, W Haiyan, W Yong - Applied Acoustics, 2021 - Elsevier
Passive target detection under low signal-to-noise ratio is essential in various underwater
circumstances, but difficult as lacking of targets' prior information, special propagation …
circumstances, but difficult as lacking of targets' prior information, special propagation …
[HTML][HTML] Multi-channel delineation of intracardiac electrograms for arrhythmia substrate analysis using implicitly regularized convolutional neural network with wide …
Objective Automated segmentation of intracardiac electrograms and extraction of
fundamental cycle length intervals is crucial for reproducible arrhythmia substrate analysis …
fundamental cycle length intervals is crucial for reproducible arrhythmia substrate analysis …