A direction of arrival (DOA) estimation method based on a convolutional neural network (CNN) using an acoustic vector sensor is proposed to distinguish multiple surface ships in a selected frequency band. The cross-spectrum of the pressure and partic...
As complex systems, dynamic networks have obvious nonlinear features. Detecting communities in dynamic networks is of great importance for understanding the functions of networks and mining evolving relationships. Recently, some network embedding-bas...
SIGNIFICANCE: The Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resu...
Indian journal of pathology & microbiology
May 1, 2022
Machine learning and artificial intelligence (AI) have become a part of our daily routine. There are very few of us who are not influenced by this technology. There are a lot of misconceptions about the scope, utility, and fallacies of AI. Digital ne...
PURPOSE: Microbial keratitis is an urgent condition in ophthalmology that requires prompt treatment. This study aimed to apply deep learning algorithms for rapidly discriminating between fungal keratitis (FK) and bacterial keratitis (BK).
The affordable, accurate, and generalizable prediction of spectroscopic observables plays a key role in the analysis of increasingly complex experiments. In this article, we develop and deploy a deep neural network-XANESNET-for predicting the linesha...
MOTIVATION: Exploring drug-protein interactions (DPIs) provides a rapid and precise approach to assist in laboratory experiments for discovering new drugs. Network-based methods usually utilize a drug-protein association network and predict DPIs by t...
MOTIVATION: Medical care is becoming more and more specific to patients' needs due to the increased availability of omics data. The application to these data of sophisticated machine learning models, in particular deep learning (DL), can improve the ...
MOTIVATION: Deep-learning models, such as convolutional neural networks, are able to accurately map biological sequences to associated functional readouts and properties by learning predictive de novo representations. In silico saturation mutagenesis...
MOTIVATION: The development of microscopic imaging techniques enables us to study protein subcellular locations from the tissue level down to the cell level, contributing to the rapid development of image-based protein subcellular location prediction...
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