A fuzzy convolutional neural network for enhancing multi-focus image fusion
The images captured by the cameras contain distortions, misclassified pixels, uncertainties
and poor contrast. Therefore, the multi-focus image fusion (MFIF) integrates various input …
and poor contrast. Therefore, the multi-focus image fusion (MFIF) integrates various input …
An attention-aware long short-term memory-like spiking neural model for sentiment analysis
Q Liu, Y Huang, Q Yang, H Peng… - International journal of …, 2023 - World Scientific
LSTM-SNP model is a recently developed long short-term memory (LSTM) network, which is
inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP …
inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP …
SDDC-Net: A U-shaped deep spiking neural P convolutional network for retinal vessel segmentation
As an essential step in the early diagnosis of retinopathy, the blood vessels morphological
attributes assist specialists to obtain pathological information efficiently. Most existing deep …
attributes assist specialists to obtain pathological information efficiently. Most existing deep …
Attention-enabled gated spiking neural P model for aspect-level sentiment classification
Y Huang, H Peng, Q Liu, Q Yang, J Wang… - Neural Networks, 2023 - Elsevier
Gated spiking neural P (GSNP) model is a recently developed recurrent-like network, which
is abstracted by nonlinear spiking mechanism of nonlinear spiking neural P systems. In this …
is abstracted by nonlinear spiking mechanism of nonlinear spiking neural P systems. In this …
Edge detection method based on nonlinear spiking neural systems
R **an, R Lugu, H Peng, Q Yang, X Luo… - International journal of …, 2023 - World Scientific
Nonlinear spiking neural P (NSNP) systems are a class of neural-like computational models
inspired from the nonlinear mechanism of spiking neurons. NSNP systems have a …
inspired from the nonlinear mechanism of spiking neurons. NSNP systems have a …
Gated spiking neural P systems for time series forecasting
Q Liu, L Long, H Peng, J Wang, Q Yang… - … on Neural Networks …, 2021 - ieeexplore.ieee.org
Spiking neural P (SNP) systems are a class of neural-like computing models, abstracted by
the mechanism of spiking neurons. This article proposes a new variant of SNP systems …
the mechanism of spiking neurons. This article proposes a new variant of SNP systems …
A prediction model based on gated nonlinear spiking neural systems
Nonlinear spiking neural P (NSNP) systems are one of neural-like membrane computing
models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems …
models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems …
Echo spiking neural P systems
L Long, R Lugu, X **ong, Q Liu, H Peng, J Wang… - Knowledge-Based …, 2022 - Elsevier
Nonlinear spiking neural P (NSNP) systems are distributed parallel neural-like computing
models that abstract the nonlinear spiking mechanisms of biological neurons. Echo state …
models that abstract the nonlinear spiking mechanisms of biological neurons. Echo state …
Reservoir computing models based on spiking neural P systems for time series classification
H Peng, X **ong, M Wu, J Wang, Q Yang… - Neural Networks, 2024 - Elsevier
Nonlinear spiking neural P (NSNP) systems are neural-like membrane computing models
with nonlinear spiking mechanisms. Because of this nonlinear spiking mechanism, NSNP …
with nonlinear spiking mechanisms. Because of this nonlinear spiking mechanism, NSNP …
An unsupervised segmentation method based on dynamic threshold neural P systems for color images
Y Cai, S Mi, J Yan, H Peng, X Luo, Q Yang, J Wang - Information Sciences, 2022 - Elsevier
Dynamic threshold neural P (DTNP) systems are a new variant of spiking neural P (SNP)
systems, abstracted by the spiking and dynamic threshold mechanisms of biological …
systems, abstracted by the spiking and dynamic threshold mechanisms of biological …