AIMC Topic: Neural Networks, Computer

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An energy-aware heart disease prediction system using ESMO and optimal deep learning model for healthcare monitoring in IoT.

Journal of biomolecular structure & dynamics
The Internet of Things (IoT), which provides seamless connectivity between people and things, improves our quality of life. In the medical field, predictive analytics can help transform a reactive healthcare (HC) strategy into a proactive one. The HC...

EEG-BCI-based motor imagery classification using double attention convolutional network.

Computer methods in biomechanics and biomedical engineering
This article aims to improve and diversify signal processing techniques to execute a brain-computer interface (BCI) based on neurological phenomena observed when performing motor tasks using motor imagery (MI). The noise present in the original data,...

Physics-informed kernel function neural networks for solving partial differential equations.

Neural networks : the official journal of the International Neural Network Society
This paper proposes an improved version of physics-informed neural networks (PINNs), the physics-informed kernel function neural networks (PIKFNNs), to solve various linear and some specific nonlinear partial differential equations (PDEs). It can als...

Memristor-Based Neuromorphic Chips.

Advanced materials (Deerfield Beach, Fla.)
In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain-like chips, which are known for their robust processing power and energy-efficie...

Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques.

Applied spectroscopy
Chylous blood is the main cause of unqualified and scrapped blood among volunteer blood donors. Therefore, a diagnostic method that can quickly and accurately identify chylous blood before donation is needed. In this study, the GaiaSorter "Gaia" hype...

ML-FGAT: Identification of multi-label protein subcellular localization by interpretable graph attention networks and feature-generative adversarial networks.

Computers in biology and medicine
The prediction of multi-label protein subcellular localization (SCL) is a pivotal area in bioinformatics research. Recent advancements in protein structure research have facilitated the application of graph neural networks. This paper introduces a no...

Development of an individual display optimization system based on deep convolutional neural network transition learning for somatostatin receptor scintigraphy.

Radiological physics and technology
Somatostatin receptor scintigraphy (SRS) is an essential examination for the diagnosis of neuroendocrine tumors (NETs). This study developed a method to individually optimize the display of whole-body SRS images using a deep convolutional neural netw...

ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.

Journal of chemical information and modeling
Protein thermodynamic stability is essential to clarify the relationships among structure, function, and interaction. Therefore, developing a faster and more accurate method to predict the impact of the mutations on protein stability is helpful for p...

Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.

Scientific reports
Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with de...

Potential diagnostic application of a novel deep learning- based approach for COVID-19.

Scientific reports
COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilitie...