AIMC Topic: Neural Networks, Computer

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Attention module incorporated transfer learning empowered deep learning-based models for classification of phenotypically similar tropical cattle breeds (Bos indicus).

Tropical animal health and production
Accurate breed identification in dairy cattle is essential for optimizing herd management and improving genetic standards. A smart method for correctly identifying phenotypically similar breeds can empower farmers to enhance herd productivity. A conv...

Enhanced classification of left ventricular hypertrophy in cardiac patients using extended Siamese CNN.

Physics in medicine and biology
Left ventricular hypertrophy (LVH) is the thickening of the left ventricle wall of the heart. The objective of this study is to develop a novel approach for the accurate assessment of LVH) severity, addressing the limitations of traditional manual gr...

HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement.

IEEE journal of biomedical and health informatics
Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address t...

MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition.

IEEE journal of biomedical and health informatics
Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical research with applications ranging from mental disorder regulation to human-computer interaction. In this paper, we address two fundamental aspects of EEG ...

A Residual U-Net Neural Network for Seismocardiogram Denoising and Analysis During Physical Activity.

IEEE journal of biomedical and health informatics
Seismocardiogram (SCG) signals are noninvasively obtained cardiomechanical signals containing important features for cardiovascular health monitoring. However, these signals are prone to contamination by motion noise, which can significantly impact a...

Subgraph-Aware Graph Kernel Neural Network for Link Prediction in Biological Networks.

IEEE journal of biomedical and health informatics
Identifying links within biological networks is important in various biomedical applications. Recent studies have revealed that each node in a network may play a unique role in different links, but most link prediction methods overlook distinctive no...

Dual-Channel Prototype Network for Few-Shot Pathology Image Classification.

IEEE journal of biomedical and health informatics
In the field of pathology, the scarcity of certain diseases and the difficulty of annotating images hinder the development of large, high-quality datasets, which in turn affects the advancement of deep learning-assisted diagnostics. Few-shot learning...

MAMLCDA: A Meta-Learning Model for Predicting circRNA-Disease Association Based on MAML Combined With CNN.

IEEE journal of biomedical and health informatics
Circular RNAs (circRNAs) exist in vivo and are a class of noncoding RNA molecules. They have a single-stranded, closed, annular structure. Many studies have shown that circRNAs and diseases are linked. Therefore, it is critical to build a reliable an...

A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators With Functional Group Information and Hypergraph Structure.

IEEE journal of biomedical and health informatics
Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-sp...

Estimating the Severity of Obstructive Sleep Apnea Using ECG, Respiratory Effort and Neural Networks.

IEEE journal of biomedical and health informatics
OBJECTIVE: wearable sensor technology has progressed significantly in the last decade, but its clinical usability for the assessment of obstructive sleep apnea (OSA) is limited by the lack of large and representative datasets simultaneously acquired ...