AIMC Topic: Neural Networks, Computer

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Artificial intelligence based vision transformer application for grading histopathological images of oral epithelial dysplasia: a step towards AI-driven diagnosis.

BMC cancer
BACKGROUND: This study aimed to classify dysplastic and healthy oral epithelial histopathological images, according to WHO and binary grading systems, using the Vision Transformer (ViT) deep learning algorithm-a state-of-the-art Artificial Intelligen...

A population based optimization of convolutional neural networks for chronic kidney disease prediction.

Scientific reports
Chronic kidney disease (CKD) is a global public health concern, and the timely detection of the disease is priceless. Most of the classical machine learning models have the major drawbacks of being unsophisticated, non-robust, and non-accurate. This ...

Session interest model for CTR prediction based on feature co-action network.

Scientific reports
The main purpose of click-prediction models is to predict the probability of customers clicking on products and provide support for advertising decisions of businesses. However, most previous models often use deep neural networks to capture implicit ...

The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions.

Scientific data
The development of universal machine learning potentials (MLP) for small organic and drug-like molecules requires large, accurate datasets that span diverse chemical spaces. In this study, we introduce the QDπ dataset which incorporates data taken fr...

Vision transformer and deep learning based weighted ensemble model for automated spine fracture type identification with GAN generated CT images.

Scientific reports
The most common causes of spine fractures, or vertebral column fractures (VCF), are traumas like falls, injuries from sports, or accidents. CT scans are affordable and effective at detecting VCF types in an accurate manner. VCF type identification in...

FusionXNet: enhancing EEG-based seizure prediction with integrated convolutional and Transformer architectures.

Journal of neural engineering
. Effective seizure prediction can reduce patient burden, improve clinical treatment accuracy, and lower healthcare costs. However, existing deep learning-based seizure prediction methods primarily rely on single models, which have limitations in fea...

Leveraging advanced graph neural networks for the enhanced classification of post anesthesia states to aid surgical procedures.

PloS one
Anesthesia plays a pivotal role in modern surgery by facilitating controlled states of unconsciousness. Precise control is crucial for safe and pain-free surgeries. Monitoring anesthesia depth accurately is essential to guide anesthesiologists, optim...

A comparative study of neural network architectures for vital signs monitoring based on the national early warning systems (NEWS).

Health informatics journal
The study aims to assess the efficacy of various neural network architectures in predicting the National Early Warning Systems (NEWS) score, using vital signs, to enhance early warning and monitoring in clinical settings. A comparative evaluation o...

Online detection of Q-marker concentrations in the Xuefu Zhuyu oral liquid extraction process using a multi-source cross-scale NIR attention fusion neural network.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Near-infrared (NIR) spectroscopy, a pivotal tool within process analytical technology (PAT), offers significant potential for real-time monitoring of quality marker (Q-Marker) concentrations in traditional Chinese medicine (TCM) extracts to ensure ba...

Multi-Granularity Autoformer for long-term deterministic and probabilistic power load forecasting.

Neural networks : the official journal of the International Neural Network Society
Long-term power load forecasting is critical for power system planning but is constrained by intricate temporal patterns. Transformer-based models emphasize modeling long- and short-term dependencies yet encounter limitations from complexity and para...