AIMC Topic: Neural Networks, Computer

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Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance.

Nature communications
Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circu...

Residual networks without pooling layers improve the accuracy of genomic predictions.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Residual neural network genomic selection is the first GS algorithm to reach 35 layers, and its prediction accuracy surpasses previous algorithms. With the decrease in DNA sequencing costs and the development of deep learning, phenotype prediction ac...

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration.

Physiological measurement
Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, wh...

Detecting the corneal neovascularisation area using artificial intelligence.

The British journal of ophthalmology
AIMS: To create and assess the performance of an artificial intelligence-based image analysis tool for the measurement and quantification of the corneal neovascularisation (CoNV) area.

Improving plant miRNA-target prediction with self-supervised k-mer embedding and spectral graph convolutional neural network.

PeerJ
Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utiliz...

Philosophy of cognitive science in the age of deep learning.

Wiley interdisciplinary reviews. Cognitive science
Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural...

Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer's disease classification based on gradient-weighted class activation mapping.

PloS one
Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurological disorders such as Alzheimer's Disease (AD), yet only a handful have provided results regarding explainability. We address this gap by applying sev...

Authentication with a one-dimensional CNN model using EEG-based brain-computer interface.

Computer methods in biomechanics and biomedical engineering
Brain-computer interface (BCI) technology uses electroencephalogram (EEG) signals to create a direct interaction between the human body and its surroundings. Motor imagery (MI) classification using EEG signals is an important application that can hel...

Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.

International journal of paediatric dentistry
BACKGROUND: Primary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise.

Prototype-based sample-weighted distillation unified framework adapted to missing modality sentiment analysis.

Neural networks : the official journal of the International Neural Network Society
Missing modality sentiment analysis is a prevalent and challenging issue in real life. Furthermore, the heterogeneity of multimodality often leads to an imbalance in optimization when attempting to optimize the same objective across all modalities in...