AIMC Topic: Neural Networks, Computer

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Enhanced sentiment analysis in tourism reviews via multimodal graph convolutional networks.

PloS one
In recent years, multimodal sentiment analysis has gained prominence due to its ability to leverage diverse data types for improved accuracy. However, combining text and image modalities presents challenges in effectively integrating and processing t...

A histogram transformer approach using attention-based 3D residual network for human action recognition.

PloS one
This paper proposes a lightweight video action recognition framework that integrates 3D Convolutional Neural Networks (CNNs), the Histogram Transformer Block (HTB), and the Split-Attention Residual Block (SAB), while also introducing Spatiotemporal T...

Chemprop v2: An Efficient, Modular Machine Learning Package for Chemical Property Prediction.

Journal of chemical information and modeling
Accurate prediction of molecular properties is essential for computational design in many areas of chemistry. Deep learning has been used in these prediction tasks for a wide variety of molecular properties, and the availability of user-friendly open...

An Innovative Method for Refractory Epilepsy Diagnosis Based on Microstate Analysis and Graph Convolutional Network.

Journal of medical systems
This study systematically investigates the alterations in electroencephalogram (EEG) microstates in patients with refractory epilepsy(RE) across different seizure stages. A novel EEG microstate analysis framework is proposed to address the limitation...

Adaptive fractional-order non-singular terminal sliding mode control for omnidirectional quadrotors based on WRBF neural network.

PloS one
This paper presents a novel robust six-degree-of-freedom trajectory tracking control strategy for tilt-rotor quadrotors operating under uncertainties and disturbances. The key contribution lies in a unified framework that synergistically co-designs a...

Coupling Machine Learning with Clusterization-Triggered Emission for Geographical Origin Tracing of Rice.

Analytical chemistry
Tracing the geographical origin of rice is of great significance in protecting the rights and interests of consumers and legitimate producers, as well as ensuring food safety. Here, we propose the combination of machine learning (ML) and clustering-t...

Representation meets optimization: Training PINNs and PIKANs for gray-box discovery in systems pharmacology.

Computers in biology and medicine
Physics-Informed Kolmogorov-Arnold Networks (PIKANs) have been gaining attention as an effective counterpart to the original multilayer perceptron-based Physics-Informed Neural Networks (PINNs). Both representation models can address inverse problems...

Advancing PFAS Detection through Machine Learning Prediction of F NMR Spectra.

Environmental science & technology
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants with diverse structures. To further advance the impact assessment and remediation technology for PFAS pollution, new approaches for identifying emerging PFAS are neces...

Structure-Aware Heterogeneous Information Fusion Framework for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling
Accurate prediction of protein-ligand binding affinities (PLAs) is essential for drug discovery and development. Recent advancements suggest that transforming protein-ligand complexes into heterogeneous graph representations may offer a viable soluti...

Prediction and analysis of anti-aging peptides using data augmentation and machine learning algorithms.

BMC biology
BACKGROUND: For most species, Aging is an inevitable biological process that poses significant challenges to global healthcare due to age-related diseases. Recent advances in peptide therapy have highlighted anti-aging peptides (AAPs) as a promising ...