AIMC Topic: Neural Networks, Computer

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The use of artificial intelligence-based Siamese neural network in personalized guidance for sports dance teaching.

Scientific reports
This work aims to explore an accurate and effective method for recognizing dance movement features, providing precise personalized guidance for sports dance teaching. First, a human skeletal graph is constructed. A graph convolutional network (GCN) i...

A lightweight deep learning model for multi-plant biotic stress classification and detection for sustainable agriculture.

Scientific reports
Plant pathogens and pests hinder general plant health, resulting in poor agricultural yields and production. These threaten global food security and cause environmental and economic shortages. Amidst the available existing heavy deep learning (DL) mo...

Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification.

Scientific reports
Breast cancer detection remains one of the most challenging problems in medical imaging. We propose a novel hybrid model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and EfficientNet-B...

A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Postoperative acute kidney injury (AKI) is a significant risk associated with surgeries under general anesthesia, often leading to increased mortality and morbidity. Existing predictive models for postoperative AKI are usually limited to ...

Automated spectral decomposition and reconstruction of optical properties using a mixed autoencoder approach.

Journal of biomedical optics
SIGNIFICANCE: Investigating optical properties (OPs) is crucial in the field of biophotonics, as it has a broad impact on understanding light-tissue interactions. However, current techniques, such as inverse Monte Carlo simulations (IMCS), have limit...

Quantized Convolutional Neural Networks Robustness under Perturbation.

F1000Research
Contemporary machine learning models are increasingly becoming restricted by size and subsequent operations per forward pass, demanding increasing compute requirements. Quantization has emerged as a convenient approach to addressing this, in which we...

Optimizing CNN for pavement distress detection via edge-enhanced multi-scale feature fusion.

PloS one
Traditional crack detection methods initially relied on manual observation, followed by instrument-assisted techniques. Today, road surface inspection leverages deep learning to achieve automated crack detection. However, in the domain of deep learni...

A knowledge tracing approach with dual graph convolutional networks and positive/negative feature enhancement network.

PloS one
Knowledge tracing models predict students' mastery of specific knowledge points by analyzing their historical learning performance. However, existing methods struggle with handling a large number of skills, data sparsity, learning differences, and co...

Deep learning-based improved side-channel attacks using data denoising and feature fusion.

PloS one
Deep learning, as a high-performance data analysis method, has demonstrated superior efficiency and accuracy in side-channel attacks compared to traditional methods. However, many existing models enhance accuracy by stacking network layers, leading t...

Utilizing a deep learning model based on BERT for identifying enhancers and their strength.

PloS one
An enhancer is a specific DNA sequence typically located within a gene at upstream or downstream position and serves as a pivotal element in the regulation of eukaryotic gene transcription. Therefore, the recognition of enhancers is highly significan...