AIMC Topic: Neural Networks, Computer

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Deep Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control Task Success From Video.

Neurosurgery
BACKGROUND: Deep neural networks (DNNs) have not been proven to detect blood loss (BL) or predict surgeon performance from video.

Artificial Neural Network vs. Pharmacometric Model for Population Prediction of Plasma Concentration in Real-World Data: A Case Study on Valproic Acid.

Clinical pharmacology and therapeutics
We compared the predictive performance of an artificial neural network to traditional pharmacometric modeling for population prediction of plasma concentrations of valproate in real-world data. We included individuals aged 65 years or older with epil...

Generative adversarial networks for biomedical time series forecasting and imputation.

Journal of biomedical informatics
In the present systematic review we identified and summarised current research activities in the field of time series forecasting and imputation with the help of generative adversarial networks (GANs). We differentiate between imputation which descri...

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network.

Sensors (Basel, Switzerland)
Large-scale functional connectivity is an important indicator of the brain's normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the ...

Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior.

Sensors (Basel, Switzerland)
5G technologies provide ubiquitous connectivity. However, 5G security is a particularly important issue. Moreover, because public datasets are outdated, we need to create a self-generated dataset on the virtual platform. Therefore, we propose a two-s...

IoT and AI-Based Application for Automatic Interpretation of the Affective State of Children Diagnosed with Autism.

Sensors (Basel, Switzerland)
In the context in which it was demonstrated that humanoid robots are efficient in helping children diagnosed with autism in exploring their affective state, this paper underlines and proves the efficiency of a previously developed machine learning-ba...

An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation.

Sensors (Basel, Switzerland)
A lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estima...

MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region.

Environmental monitoring and assessment
Hydrological analyses based on precipitation records in the Amazon are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, there are limitations related to data series with short periods a...

Generation of microbial colonies dataset with deep learning style transfer.

Scientific reports
We introduce an effective strategy to generate an annotated synthetic dataset of microbiological images of Petri dishes that can be used to train deep learning models in a fully supervised fashion. The developed generator employs traditional computer...

Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts.

Nature communications
Drug discovery for diseases such as Parkinson's disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and dee...