AIMC Topic: Neural Networks, Computer

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Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution.

Singapore medical journal
INTRODUCTION: Deep learning models can assess the quality of images and discriminate among abnormalities in small bowel capsule endoscopy (CE), reducing fatigue and the time needed for diagnosis. They serve as a decision support system, partially aut...

Dual-Path Graph Neural Network with Adaptive Auxiliary Module for Link Prediction.

Big data
Link prediction, which has important applications in many fields, predicts the possibility of the link between two nodes in a graph. Link prediction based on Graph Neural Network (GNN) obtains node representation and graph structure through GNN, whic...

Enhancing Lung Nodule Classification: A Novel CViEBi-CBGWO Approach with Integrated Image Preprocessing.

Journal of imaging informatics in medicine
Cancer detection and accurate classification pose significant challenges for medical professionals, as it is described as a lethal illness. Diagnosing the malignant lung nodules in its initial stage significantly enhances the recovery and survival ra...

Predefined-time distributed optimization and anti-disturbance control for nonlinear multi-agent system with neural network estimator: A hierarchical framework.

Neural networks : the official journal of the International Neural Network Society
This paper addresses the predefined-time distributed optimization of nonlinear multi-agent system using a hierarchical control approach. Considering unknown nonlinear functions and external disturbances, we propose a two-layer hierarchical control fr...

Prediction of systemic lupus erythematosus-related genes based on graph attention network and deep neural network.

Computers in biology and medicine
Systemic lupus erythematosus (SLE) is an autoimmune disorder intricately linked to genetic factors, with numerous approaches having identified genes linked to its development, diagnosis and prognosis. Despite genome-wide association analysis and gene...

A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm.

Scientific reports
We constructed an early prediction model for postoperative pulmonary complications after thoracoscopic surgery using machine learning and deep learning algorithms. The artificial intelligence prediction models were built in Python, primarily using ar...

Integrated image and location analysis for wound classification: a deep learning approach.

Scientific reports
The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network ...

BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.

RNA biology
The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various...

Rapid discrimination and ratio quantification of mixed antibiotics in aqueous solution through integrative analysis of SERS spectra via CNN combined with NN-EN model.

Journal of advanced research
INTRODUCTION: Abusing antibiotic residues in the natural environment has become a severe public health and ecological environmental problem. The side effects of its biochemical and physiological consequences are severe. To avoid antibiotic contaminat...

Preoperative detection of hepatocellular carcinoma's microvascular invasion on CT-scan by machine learning and radiomics: A preliminary analysis.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: Microvascular invasion (MVI) is the main risk factor for overall mortality and recurrence after surgery for hepatocellular carcinoma (HCC).The aim was to train machine-learning models to predict MVI on preoperative CT scan.