AIMC Topic: Neural Networks, Computer

Clear Filters Showing 20111 to 20120 of 31376 articles

Memristor networks for real-time neural activity analysis.

Nature communications
The ability to efficiently analyze the activities of biological neural networks can significantly promote our understanding of neural communications and functionalities. However, conventional neural signal analysis approaches need to transmit and sto...

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses.

Scientific reports
Spiking neural networks (SNN) are computational models inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the con...

Deep neural model with self-training for scientific keyphrase extraction.

PloS one
Scientific information extraction is a crucial step for understanding scientific publications. In this paper, we focus on scientific keyphrase extraction, which aims to identify keyphrases from scientific articles and classify them into predefined ca...

Neural networks for open and closed Literature-based Discovery.

PloS one
Literature-based Discovery (LBD) aims to discover new knowledge automatically from large collections of literature. Scientific literature is growing at an exponential rate, making it difficult for researchers to stay current in their discipline and e...

Innovative machine learning approach and evaluation campaign for predicting the subjective feeling of work-life balance among employees.

PloS one
At present, many researchers see hope that artificial intelligence, machine learning in particular, will improve several aspects of the everyday life for individuals, cities and whole nations alike. For example, it has been speculated that the so-cal...

Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning.

International journal of radiation oncology, biology, physics
PURPOSE: This study aims to evaluate the impact of key parameters on the pseudo computed tomography (pCT) quality generated from magnetic resonance imaging (MRI) with a 3-dimensional (3D) convolutional neural network.

Inspection of visible components in urine based on deep learning.

Medical physics
PURPOSE: Urinary particles are particularly important parameters in clinical urinalysis, especially for the diagnosis of nephropathy. Therefore, it is highly important to precisely detect urinary particles in the clinical setting. However, artificial...

3D-MCN: A 3D Multi-scale Capsule Network for Lung Nodule Malignancy Prediction.

Scientific reports
Despite the advances in automatic lung cancer malignancy prediction, achieving high accuracy remains challenging. Existing solutions are mostly based on Convolutional Neural Networks (CNNs), which require a large amount of training data. Most of the ...

"Ring Breaker": Neural Network Driven Synthesis Prediction of the Ring System Chemical Space.

Journal of medicinal chemistry
Ring systems in pharmaceuticals, agrochemicals, and dyes are ubiquitous chemical motifs. While the synthesis of common ring systems is well described and novel ring systems can be readily and computationally enumerated, the synthetic accessibility of...