AIMC Topic: Neural Networks, Computer

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A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins.

Science advances
Nuclear magnetic resonance (NMR) spectroscopy is an important technique for deriving the dynamics and interactions of macromolecules; however, characterizations of aromatic residues in proteins still pose a challenge. Here, we present a deep neural n...

Quantifying interpretation reproducibility in Vision Transformer models with TAVAC.

Science advances
Deep learning algorithms can extract meaningful diagnostic features from biomedical images, promising improved patient care in digital pathology. Vision Transformer (ViT) models capture long-range spatial relationships and offer robust prediction pow...

Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery.

PloS one
Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that are even better at eliminating infections. The fundamental transformation in a variety of scientific discip...

Improving prediction of solar radiation using Cheetah Optimizer and Random Forest.

PloS one
In the contemporary context of a burgeoning energy crisis, the accurate and dependable prediction of Solar Radiation (SR) has emerged as an indispensable component within thermal systems to facilitate renewable energy generation. Machine Learning (ML...

Implementing the discontinuous-Galerkin finite element method using graph neural networks with application to diffusion equations.

Neural networks : the official journal of the International Neural Network Society
Machine learning (ML) has benefited from both software and hardware advancements, leading to increasing interest in capitalising on ML throughout academia and industry. There have been efforts in the scientific computing community to leverage this de...

Intrinsic plasticity coding improved spiking actor network for reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Deep reinforcement learning (DRL) exploits the powerful representational capabilities of deep neural networks (DNNs) and has achieved significant success. However, compared to DNNs, spiking neural networks (SNNs), which operate on binary signals, mor...

RAIN: Reconstructed-aware in-context enhancement with graph denoising for session-based recommendation.

Neural networks : the official journal of the International Neural Network Society
Session-based recommendation aims to recommend the next item based on short-term interactions. Traditional session-based recommendation methods assume that all interacted items are closely related to the user's interests. However, noise (e.g., accide...

ST-GMLP: A concise spatial-temporal framework based on gated multi-layer perceptron for traffic flow forecasting.

Neural networks : the official journal of the International Neural Network Society
The field of traffic forecasting has been the subject of considerable attention as a critical component in alleviating traffic congestion and improving urban services. Given the regular patterns of human activities, it is evident that traffic flow is...

Local interpretable spammer detection model with multi-head graph channel attention network.

Neural networks : the official journal of the International Neural Network Society
Fraudulent reviews posted by spammers on the online shopping websites mislead consumers' purchasing decisions. To curb fraudulent reviews, many methods have been proposed for detecting spammers. However, the existing spammer detection methods operate...

Accelerated quadratic penalty dynamic approaches with applications to distributed optimization.

Neural networks : the official journal of the International Neural Network Society
In this paper, we explore accelerated continuous-time dynamic approaches with a vanishing damping α/t, driven by a quadratic penalty function designed for linearly constrained convex optimization problems. We replace these linear constraints with pen...