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Electronic Data Processing

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Phase Oscillatory Network and Visual Pattern Recognition.

IEEE transactions on neural networks and learning systems
We explore a properly interconnected set of Kuramoto type oscillators that results in a new associative-memory network configuration, which includes second- and third-order additional terms in the Fourier expansion of the network's coupling. Investig...

Detecting tactical patterns in basketball: comparison of merge self-organising maps and dynamic controlled neural networks.

European journal of sport science
The soaring amount of data, especially spatial-temporal data, recorded in recent years demands for advanced analysis methods. Neural networks derived from self-organizing maps established themselves as a useful tool to analyse static and temporal dat...

Neural Activity and Information Processing Capacity of Neuronal Culture.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Whether artificial or living, neural networks perform tremendously diverse kinds of information processing, such as visual feature extraction, speech recognition, motor control, and so on. Some studies have evaluated the computational ability of livi...

High-speed serial deep learning through temporal optical neurons.

Optics express
Deep learning is able to functionally mimic the human brain and thus, it has attracted considerable recent interest. Optics-assisted deep learning is a promising approach to improve forward-propagation speed and reduce the power consumption of electr...

Identification of spectral signature for in situ real-time monitoring of smoltification.

Applied optics
We describe the use of an optical hyperspectral sensing technique to identify the smoltification status of Atlantic salmon (Salmo salar) based on spectral signatures, thus potentially providing smolt producers with an additional tool to verify the os...

Introduction to machine and deep learning for medical physicists.

Medical physics
Recent years have witnessed tremendous growth in the application of machine learning (ML) and deep learning (DL) techniques in medical physics. Embracing the current big data era, medical physicists equipped with these state-of-the-art tools should b...

Cohort selection for clinical trials using hierarchical neural network.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Cohort selection for clinical trials is a key step for clinical research. We proposed a hierarchical neural network to determine whether a patient satisfied selection criteria or not.

[Automatic keyword retrieval from clinical texts: an application of natural language processing to massive data of Chilean suspected diagnosis].

Revista medica de Chile
BACKGROUND: Free-text imposes a challenge in health data analysis since the lack of structure makes the extraction and integration of information difficult, particularly in the case of massive data. An appropriate machine-interpretation of electronic...

Global firing rate contrast enhancement in E/I neuronal networks by recurrent synchronized inhibition.

Chaos (Woodbury, N.Y.)
Inhibitory synchronization is commonly observed and may play some important functional roles in excitatory/inhibitory (E/I) neuronal networks. The firing rate contrast enhancement is a general feature of information processing in sensory pathways, an...

Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analy...