AIMC Topic: Neural Networks, Computer

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A novel approach of brain-computer interfacing (BCI) and Grad-CAM based explainable artificial intelligence: Use case scenario for smart healthcare.

Journal of neuroscience methods
BACKGROUND: In order to push the frontiers of brain-computer interface (BCI) and neuron-electronics, this research presents a novel framework that combines cutting-edge technologies for improved brain-related diagnostics in smart healthcare. This res...

GNN-based structural information to improve DNN-based basal ganglia segmentation in children following early brain lesion.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Analyzing the basal ganglia following an early brain lesion is crucial due to their noteworthy role in sensory-motor functions. However, the segmentation of these subcortical structures on MRI is challenging in children and is further complicated by ...

EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation.

Computer methods and programs in biomedicine
BACKGROUND: Virtual reality motion sickness (VRMS) is a key issue hindering the development of virtual reality technology, and accurate detection of its occurrence is the first prerequisite for solving the issue.

Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network.

BMC medical informatics and decision making
In recent times, time-to-event data such as time to failure or death is routinely collected alongside high-throughput covariates. These high-dimensional bioinformatics data often challenge classical survival models, which are either infeasible to fit...

Precise and automated lung cancer cell classification using deep neural network with multiscale features and model distillation.

Scientific reports
Lung diseases globally impose a significant pathological burden and mortality rate, particularly the differential diagnosis between adenocarcinoma, squamous cell carcinoma, and small cell lung carcinoma, which is paramount in determining optimal trea...

Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study.

Biomedical physics & engineering express
. Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters...

Comparing the advantages and disadvantages of physics-based and neural network-based modelling for predicting cycling power.

Journal of biomechanics
Models of physical phenomena can be developed using two distinct approaches: using expert knowledge of the underlying physical principles or using experimental data to train a neural network. Here, our aim was to better understand the advantages and ...

GELT: A graph embeddings based lite-transformer for knowledge tracing.

PloS one
The development of intelligent education has led to the emergence of knowledge tracing as a fundamental task in the learning process. Traditionally, the knowledge state of each student has been determined by assessing their performance in previous le...

Neuromorphic computing spiking neural network edge detection model for content based image retrieval.

Network (Bristol, England)
In contemporary times, content-based image retrieval (CBIR) techniques have gained widespread acceptance as a means for end-users to discern and extract specific image content from vast repositories. However, it is noteworthy that a substantial major...

Multi-level feature interaction image super-resolution network based on convolutional nonlinear spiking neural model.

Neural networks : the official journal of the International Neural Network Society
Image super-resolution (ISR) is designed to recover lost detail information from low-resolution images, resulting in high-quality and high-definition high-resolution images. In the existing single ISR (SISR) methods based on convolutional neural netw...