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The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks.

PLoS computational biology
Learning in neuronal networks has developed in many directions, in particular to reproduce cognitive tasks like image recognition and speech processing. Implementations have been inspired by stereotypical neuronal responses like tuning curves in the ...

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks.

Scientific reports
The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult...

Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.

Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification.

BMC bioinformatics
BACKGROUND: As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, ...

The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning.

PloS one
To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this ...

Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network.

Sensors (Basel, Switzerland)
Devices and systems secured by biometric factors became a part of our lives because they are convenient, easy to use, reliable, and secure. They use information about unique features of our bodies in order to authenticate a user. It is possible to en...

A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability.

The Lancet. Digital health
Health data that are publicly available are valuable resources for digital health research. Several public datasets containing ophthalmological imaging have been frequently used in machine learning research; however, the total number of datasets cont...

Cautionary Guidelines for Machine Learning Studies with Combinatorial Datasets.

ACS combinatorial science
Regression modeling is becoming increasingly prevalent in organic chemistry as a tool for reaction outcome prediction and mechanistic interrogation. Frequently, to acquire the requisite amount of data for such studies, researchers employ combinatoria...

Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients.

Annals of clinical and translational neurology
OBJECTIVE: Subarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral ischemia (DCI). The ability to accurately predict survival for SAH patients during the hospital course ...