AIMC Topic: Neural Networks, Computer

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Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AI.

Sensors (Basel, Switzerland)
For people with diabetes, controlling blood glucose level (BGL) is a significant issue since the disease affects how the body metabolizes food, which makes careful insulin regulation necessary. Patients have to manually check their blood sugar levels...

Multi-Biometric Feature Extraction from Multiple Pose Estimation Algorithms for Cross-View Gait Recognition.

Sensors (Basel, Switzerland)
Gait recognition is a behavioral biometric technique that identifies individuals based on their unique walking patterns, enabling long-distance identification. Traditional gait recognition methods rely on appearance-based approaches that utilize back...

Predicting cortical-thalamic functional connectivity using functional near-infrared spectroscopy and graph convolutional networks.

Scientific reports
Functional near-infrared spectroscopy (fNIRS) measures cortical hemodynamic changes, yet it cannot collect this information from subcortical structures, such as the thalamus, which is involved in several key functional networks. To address this drawb...

Lightweight convolutional neural network for chest X-ray images classification.

Scientific reports
In this study, we developed a lightweight and rapid convolutional neural network (CNN) architecture for chest X-ray images; it primarily consists of a redesigned feature extraction (FE) module and multiscale feature (MF) module and validated using pu...

MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways.

Medical & biological engineering & computing
The precise segmentation and three-dimensional reconstruction of the nasopharyngeal airway are crucial for the diagnosis and treatment of adenoid hypertrophy in children. However, traditional methods face challenges such as information loss and low c...

Hybrid contrastive multi-scenario learning for multi-task sequential-dependence recommendation.

Neural networks : the official journal of the International Neural Network Society
Multi-scenario and multi-task learning are crucial in industrial recommendation systems to deliver high-quality recommendations across diverse scenarios with minimal computational overhead. However, conventional models often fail to effectively lever...

Illumination-Guided progressive unsupervised domain adaptation for low-light instance segmentation.

Neural networks : the official journal of the International Neural Network Society
Due to limited photons, low-light environments pose significant challenges for computer vision tasks. Unsupervised domain adaptation offers a potential solution, but struggles with domain misalignment caused by inadequate utilization of features at d...

CDCGAN: Class Distribution-aware Conditional GAN-based minority augmentation for imbalanced node classification.

Neural networks : the official journal of the International Neural Network Society
Node classification is a fundamental task of Graph Neural Networks (GNNs). However, GNN models tend to suffer from the class imbalance problem which deteriorates the representation ability of minority classes, thus leading to unappealing classificati...

Large Language Model Enhanced Logic Tensor Network for Stance Detection.

Neural networks : the official journal of the International Neural Network Society
Social media platforms, rich in user-generated content, offer a unique perspective on public opinion, making stance detection an essential task in opinion mining. However, traditional deep neural networks for stance detection often suffer from limita...

Interpretable time-series neural turing machine for prognostic prediction of patients with type 2 diabetes in physician-pharmacist collaborative clinics.

International journal of medical informatics
BACKGROUND: Type 2 diabetes (T2D) has become a serious health threat globally. However, the existing approaches for diabetes prediction mainly had difficulty in addressing multiple time-series features. This study aims to provide an adjunctive tool f...