AIMC Topic: Neural Networks, Computer

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An effective vessel segmentation method using SLOA-HGC.

Scientific reports
Accurate segmentation of retinal blood vessels from retinal images is crucial for detecting and diagnosing a wide range of ophthalmic diseases. Our retinal blood vessel segmentation algorithm enhances microfine vessel extraction, improves edge textur...

Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning.

Scientific reports
Model optimization is a problem of great concern and challenge for developing an image classification model. In image classification, selecting the appropriate hyperparameters can substantially boost the model's ability to learn intricate patterns an...

Alzheimer's disease recognition using graph neural network by leveraging image-text similarity from vision language model.

Scientific reports
Alzheimer's disease (AD), a progressive neurodegenerative condition, notably impacts cognitive functions and daily activity. One method of detecting dementia involves a task where participants describe a given picture, and extensive research has been...

Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study.

Acta odontologica Scandinavica
OBJECTIVES: Approximal caries diagnosis in children is difficult, and artificial intelligence-based research in pediatric dentistry is scarce. To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identif...

Estimating body weight in Sujiang pigs using artificial neural network, nearest neighbor, and CART algorithms: a comparative study using morphological measurements.

Tropical animal health and production
The objectives of this study were to evaluate different machine learning algorithms for predicting body weight (BW) in Sujiang pigs using the following morphological traits: age, body length (BL), backfat thickness (BFT), chest circumference (CC), bo...

A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.

Journal of neural engineering
. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient...

Feasibility of reconstructingpatient 3D dose distributions from 2D EPID image data using convolutional neural networks.

Physics in medicine and biology
. The primary purpose of this work is to demonstrate the feasibility of a deep convolutional neural network (dCNN) based algorithm that uses two-dimensional (2D) electronic portal imaging device (EPID) images and CT images as input to reconstruct 3D ...

Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators.

Ecotoxicology and environmental safety
The long-term presence of antibiotics in the aquatic environment will affect ecology and human health. Techniques for determining antibiotics are often time-consuming, labor-intensive and costly, and it is desirable to seek new methods to achieve rap...

DeepQA: A Unified Transcriptome-Based Aging Clock Using Deep Neural Networks.

Aging cell
Understanding the complex biological process of aging is of great value, especially as it can help develop therapeutics to prolong healthy life. Predicting biological age from gene expression data has shown to be an effective means to quantify aging ...

A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors.

Sensors (Basel, Switzerland)
Abnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual's safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different ...