AI Medical Compendium Topic:
Pattern Recognition, Automated

Clear Filters Showing 581 to 590 of 1638 articles

Hypercomplex extreme learning machine with its application in multispectral palmprint recognition.

PloS one
An extreme learning machine (ELM) is a novel training method for single-hidden layer feedforward neural networks (SLFNs) in which the hidden nodes are randomly assigned and fixed without iterative tuning. ELMs have earned widespread global interest d...

Dense networks with relative location awareness for thorax disease identification.

Medical physics
PURPOSE: Chest X-ray is one of the most common examinations for diagnosing heart and lung diseases. Due to the existing of a large number of clinical cases, many automated diagnosis algorithms based on chest X-ray images have been proposed. To our kn...

Improving the Antinoise Ability of DNNs via a Bio-Inspired Noise Adaptive Activation Function Rand Softplus.

Neural computation
Although deep neural networks (DNNs) have led to many remarkable results in cognitive tasks, they are still far from catching up with human-level cognition in antinoise capability. New research indicates how brittle and susceptible current models are...

Information Transmitted From Bioinspired Neuron-Astrocyte Network Improves Cortical Spiking Network's Pattern Recognition Performance.

IEEE transactions on neural networks and learning systems
We trained two spiking neural networks (SNNs), the cortical spiking network (CSN) and the cortical neuron-astrocyte network (CNAN), using a spike-based unsupervised method, on the MNIST and alpha-digit data sets and achieve an accuracy of 96.1% and 7...

TSE-CNN: A Two-Stage End-to-End CNN for Human Activity Recognition.

IEEE journal of biomedical and health informatics
Human activity recognition has been widely used in healthcare applications such as elderly monitoring, exercise supervision, and rehabilitation monitoring. Compared with other approaches, sensor-based wearable human activity recognition is less affec...

Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU.

Journal of clinical monitoring and computing
Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing pat...

Stability of stochastic impulsive reaction-diffusion neural networks with S-type distributed delays and its application to image encryption.

Neural networks : the official journal of the International Neural Network Society
In this paper, we study stochastic impulsive reaction-diffusion neural networks with S-type distributed delays, aiming to obtain the sufficient conditions for global exponential stability. First, an impulsive inequality involving infinite delay is in...

An unsupervised neuromorphic clustering algorithm.

Biological cybernetics
Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, hi...

Common spatial pattern and wavelet decomposition for motor imagery EEG- fTCD brain-computer interface.

Journal of neuroscience methods
BACKGROUND: Recently, hybrid brain-computer interfaces (BCIs) combining more than one modality have been investigated with the aim of boosting the performance of the existing single-modal BCIs in terms of accuracy and information transfer rate (ITR)....

QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research.

BMC medical informatics and decision making
BACKGROUND: While doctors should analyze a large amount of electronic medical record (EMR) data to conduct clinical research, the analyzing process requires information technology (IT) skills, which is difficult for most doctors in China.