AIMC Topic: Algorithms

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Genetic Artificial Hummingbird Algorithm-Support Vector Machine for Timely Power Theft Detection.

TheScientificWorldJournal
Utilities face serious obstacles from power theft, which calls for creative ways to maintain income and improve operational effectiveness. This study presents a novel hybrid genetic artificial hummingbird algorithm-support vector machine classifier t...

Artificial Intelligence-Based Facial Palsy Evaluation: A Survey.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Facial palsy evaluation (FPE) aims to assess facial palsy severity of patients, which plays a vital role in facial functional treatment and rehabilitation. The traditional manners of FPE are based on subjective judgment by clinicians, which may ultim...

Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.

Magnetic resonance in medicine
PURPOSE: Ultrashort echo time (UTE) MRI can be a radiation-free alternative to CT for craniofacial imaging of pediatric patients. However, unlike CT, bone-specific MR imaging is limited by long scan times, relatively low spatial resolution, and a tim...

Validity and Reliability of OpenPose-Based Motion Analysis in Measuring Knee Valgus during Drop Vertical Jump Test.

Journal of sports science & medicine
OpenPose-based motion analysis (OpenPose-MA), utilizing deep learning methods, has emerged as a compelling technique for estimating human motion. It addresses the drawbacks associated with conventional three-dimensional motion analysis (3D-MA) and hu...

Exploration of the prediction and generation patterns of heterocyclic aromatic amines in roast beef based on Genetic Algorithm combined with Support Vector Regression.

Food chemistry
Heterocyclic aromatic amines (HAAs) are harmful byproducts in food heating. Therefore, exploring the prediction and generation patterns of HAAs is of great significance. In this study, genetic algorithm (GA) and support vector regression (SVR) are us...

Deep unfolding network with spatial alignment for multi-modal MRI reconstruction.

Medical image analysis
Multi-modal Magnetic Resonance Imaging (MRI) offers complementary diagnostic information, but some modalities are limited by the long scanning time. To accelerate the whole acquisition process, MRI reconstruction of one modality from highly under-sam...

GlobalSR: Global context network for single image super-resolution via deformable convolution attention and fast Fourier convolution.

Neural networks : the official journal of the International Neural Network Society
Vision Transformer have achieved impressive performance in image super-resolution. However, they suffer from low inference speed mainly because of the quadratic complexity of multi-head self-attention (MHSA), which is the key to learning long-range d...

Delay learning based on temporal coding in Spiking Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) hold great potential for mimicking the brain's efficient processing of information. Although biological evidence suggests that precise spike timing is crucial for effective information encoding, contemporary SNN researc...

A dual-region speech enhancement method based on voiceprint segmentation.

Neural networks : the official journal of the International Neural Network Society
Single-channel speech enhancement primarily relies on deep learning models to recover clean speech signals from noise-contaminated speech. These models establish a mapping relationship between noisy and clean speech. However, considering the sparse d...

Cross-domain zero-shot learning for enhanced fault diagnosis in high-voltage circuit breakers.

Neural networks : the official journal of the International Neural Network Society
Ensuring the stability of high-voltage circuit breakers (HVCBs) is crucial for maintaining an uninterrupted supply of electricity. Existing fault diagnosis methods typically rely on extensive labeled datasets, which are challenging to obtain due to t...