AIMC Topic: Neural Networks, Computer

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Establishing identifiable characteristic fingerprints of mulberry leaves: Integrating chemical composition and bioactivity through machine learning.

Journal of ethnopharmacology
ETHNOPHARMACOLOGICAL RELEVANCE: Mulberry leaves (Morus alba L.) are used in traditional Chinese medicine to clear the lungs and dispel wind-heat. Despite their common use, chemical reference substance rely solely on rutin, which may not reflect their...

Recognizing American Sign Language gestures efficiently and accurately using a hybrid transformer model.

Scientific reports
Gesture recognition plays a vital role in computer vision, especially for interpreting sign language and enabling human-computer interaction. Many existing methods struggle with challenges like heavy computational demands, difficulty in understanding...

BrainNet-GAN: Generative Adversarial Graph Convolutional Network for Functional Brain Network Synthesis from Routine Clinical Brain Structural T1-Weighted Sequence.

Brain topography
Functional brain network (FBN) derived from functional Magnetic Resonance Imaging (fMRI) has promising prospects in clinical research, but fMRI is not a routine acquisition data, which limits its popularity in clinical applications. Therefore, it is ...

Realistic fundus photograph generation for improving automated disease classification.

The British journal of ophthalmology
AIMS: This study aims to investigate whether denoising diffusion probabilistic models (DDPMs) could generate realistic retinal images, and if they could be used to improve the performance of a deep convolutional neural network (CNN) ensemble for mult...

Use of Artificial Neural Networks (ANNs) to assess xenobiotics in a river catchment using macroinvertebrates as bioindicators.

Aquatic toxicology (Amsterdam, Netherlands)
The Danube flows through various European regions, exposing its aquatic ecosystem to multiple stressors, including dams, canalization, and agricultural activities. Fertilizers, manures, pesticides, animal husbandry activities, irrigation practices, d...

Detection of pre-ictal epileptic events using a self-attention based neural network from raw Neonatal EEG data.

Computers in biology and medicine
Epileptic seizures can occur unpredictably, making real-time monitoring and early warning systems critical, especially in neonatal patients, where timely intervention can significantly improve outcomes. Neonatal seizures are often subtle and difficul...

Shapley value-driven multi-modal deep reinforcement learning for complex decision-making.

Neural networks : the official journal of the International Neural Network Society
Deep Reinforcement Learning (DRL) has made significant strides in addressing various sequential decision-making problems, particularly in domains such as game simulations and robotic control. However, substantial challenges arise when DRL is applied ...

Comprehensive disentanglement with fine-grained feature mitigation for domain generalization.

Neural networks : the official journal of the International Neural Network Society
Domain generalization is proposed as an approach capable of solving the domain shift challenge, which aims at generalizing knowledge learned from multiple source domains with different distributions to the target domain that is invisible during the t...

Path-aware multi-scale learning for heterogeneous graph neural network.

Neural networks : the official journal of the International Neural Network Society
Heterogeneous Graph Neural Networks (HGNNs) are a powerful tool for modeling data with diverse node and edge types, found in applications like social networks, recommendation systems, and knowledge graphs, including tasks such as node classification,...

The architecture design and training optimization of spiking neural network with low-latency and high-performance for classification and segmentation.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) are the new third generation of bio-mimetic neural networks suitable for large-scale parallel computation due to its advantages of low power consumption and low latency. However, most of the training algorithms and netw...