AIMC Topic: Neural Networks, Computer

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Effect of conductance linearity and multi-level cell characteristics of TaO-based synapse device on pattern recognition accuracy of neuromorphic system.

Nanotechnology
To improve the classification accuracy of an image data set (CIFAR-10) by using analog input voltage, synapse devices with excellent conductance linearity (CL) and multi-level cell (MLC) characteristics are required. We analyze the CL and MLC charact...

Artificial neural network detects human uncertainty.

Chaos (Woodbury, N.Y.)
Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allo...

Modelling the effects of meteorological parameters on water temperature using artificial neural networks.

Water science and technology : a journal of the International Association on Water Pollution Research
Water temperature affects all biological and chemical processes in water; therefore, it is an extremely important water quality parameter. Meteorological factors are among the most important factors that affect water temperatures. The aim of this stu...

A Hybrid Network for ERP Detection and Analysis Based on Restricted Boltzmann Machine.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Detecting and Please provide the correct one analyzing the event-related potential (ERP) remains an important problem in neuroscience. Due to the low signal-to-noise ratio and complex spatio-temporal patterns of ERP signals, conventional methods usua...

Hybrid gray wolf optimizer-artificial neural network classification approach for magnetic resonance brain images.

Applied optics
Automated and accurate classification of magnetic resonance images (MRIs) of the brain has great importance for medical analysis and interpretation. This paper presents a hybrid optimized classification method to classify the brain tumor by classifyi...

Decision Support in Diabetes Care: The Challenge of Supporting Patients in Their Daily Living Using a Mobile Glucose Predictor.

Journal of diabetes science and technology
BACKGROUND: In type 1 diabetes mellitus (T1DM), patients play an active role in their own care and need to have the knowledge to adapt decisions to their daily living conditions. Artificial intelligence applications can help people with type 1 diabet...

A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring.

Journal of diabetes science and technology
BACKGROUND: In type 1 diabetes (T1D) therapy, the calculation of the meal insulin bolus is performed according to a standard formula (SF) exploiting carbohydrate intake, carbohydrate-to-insulin ratio, correction factor, insulin on board, and target g...

Feature-Based Representation Improves Color Decomposition and Nuclear Detection Using a Convolutional Neural Network.

IEEE transactions on bio-medical engineering
Detection of nuclei is an important step in phenotypic profiling of 1) histology sections imaged in bright field; and 2) colony formation of the 3-D cell culture models that are imaged using confocal microscopy. It is shown that feature-based represe...

Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential.

Current opinion in pulmonary medicine
PURPOSE OF REVIEW: The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be us...

Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths.

Bioinformatics (Oxford, England)
MOTIVATION: Adverse events resulting from drug-drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs described in the biomedical literature could further efforts for ongoing pharmacovigilance. Most of neural n...