AIMC Topic: Neural Networks, Computer

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Model metamers reveal divergent invariances between biological and artificial neural networks.

Nature neuroscience
Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated 'model metamers', stimuli whose activations within a model ...

KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.

Journal of translational medicine
This paper addresses the crucial task of identifying DNA/RNA binding sites, which has implications in drug/vaccine design, protein engineering, and cancer research. Existing methods utilize complex neural network structures, diverse input types, and ...

Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model.

BMC medical informatics and decision making
This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model...

Detection and classification of adult epilepsy using hybrid deep learning approach.

Scientific reports
The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinc...

Development and validation of a convolutional neural network to identify blepharoptosis.

Scientific reports
Blepharoptosis is a recognized cause of reversible vision loss and a non-specific indicator of neurological issues, occasionally heralding life-threatening conditions. Currently, diagnosis relies on human expertise and eyelid examination, with most e...

A multi-stage neural network approach for coronary 3D reconstruction from uncalibrated X-ray angiography images.

Scientific reports
We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This method uses several binarized images from different angles to reconstruct a 3D coronary tree without a...

Super-resolution biomedical imaging via reference-free statistical implicit neural representation.

Physics in medicine and biology
Supervised deep learning for image super-resolution (SR) has limitations in biomedical imaging due to the lack of large amounts of low- and high-resolution image pairs for model training. In this work, we propose a reference-free statistical implicit...

Attentive transformer deep learning algorithm for intrusion detection on IoT systems using automatic Xplainable feature selection.

PloS one
Recent years have witnessed an in-depth proliferation of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems linked to Industry 4.0 technology. The increasing rate of IoT device usage is associated with rising security risks...

Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks.

Biophysical journal
Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA s...

Automatic selection of spoken language biomarkers for dementia detection.

Neural networks : the official journal of the International Neural Network Society
This paper analyzes diverse features extracted from spoken language to select the most discriminative ones for dementia detection. We present a two-step feature selection (FS) approach: Step 1 utilizes filter methods to pre-screen features, and Step ...