AIMC Topic: Neural Networks, Computer

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High-resolution 3T to 7T ADC map synthesis with a hybrid CNN-transformer model.

Medical physics
BACKGROUND: 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) curren...

Multitask Adversarial Networks Based on Extensive Nonlinear Spiking Neuron Models.

International journal of neural systems
Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower ima...

Protein Engineering with Lightweight Graph Denoising Neural Networks.

Journal of chemical information and modeling
Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establish...

Thermogravimetric experiments based prediction of biomass pyrolysis behavior: A comparison of typical machine learning regression models in Scikit-learn.

Marine pollution bulletin
A variety of machine learning (ML) models have been extensively utilized in predicting biomass pyrolysis owing to their prowess in deciphering complex non-linear relationships between inputs and outputs, but there is still a lack of consensus on the ...

Deep neural network uncertainty estimation for early oral cancer diagnosis.

Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
BACKGROUND: Early diagnosis in oral cancer is essential to reduce both morbidity and mortality. This study explores the use of uncertainty estimation in deep learning for early oral cancer diagnosis.

Antimicrobial Activity Classification of Imidazolium Derivatives Predicted by Artificial Neural Networks.

Pharmaceutical research
PURPOSE: This study assesses the Multilayer Perceptron (MLP) neural network, complemented by other Machine Learning techniques (CART, PCA), in predicting the antimicrobial activity of 140 newly designed imidazolium chlorides against Klebsiella pneumo...

Neuromorphic one-shot learning utilizing a phase-transition material.

Proceedings of the National Academy of Sciences of the United States of America
Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building ...

Adopting Graph Neural Networks to Analyze Human-Object Interactions for Inferring Activities of Daily Living.

Sensors (Basel, Switzerland)
Human Activity Recognition (HAR) refers to a field that aims to identify human activities by adopting multiple techniques. In this field, different applications, such as smart homes and assistive robots, are introduced to support individuals in their...

CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction.

International journal of molecular sciences
CRISPR/Cas9 is a powerful genome-editing tool in biology, but its wide applications are challenged by a lack of knowledge governing single-guide RNA (sgRNA) activity. Several deep-learning-based methods have been developed for the prediction of on-ta...

Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram.

Scientific reports
Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition o...