AIMC Topic: Neural Networks, Computer

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Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network.

Sensors (Basel, Switzerland)
As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. However, there are still two probl...

Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks.

Journal of healthcare engineering
Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce ...

Anti-synchronization of complex-valued memristor-based delayed neural networks.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the anti-synchronization of complex-valued memristor-based neural networks with time delays via designed external controllers. By constructing appropriate Lyapunov functions and using inequality technique, two different types ...

Computational Analysis of Cell Dynamics in Videos with Hierarchical-Pooled Deep-Convolutional Features.

Journal of computational biology : a journal of computational molecular cell biology
Computational analysis of cellular appearance and its dynamics is used to investigate physiological properties of cells in biomedical research. In consideration of the great success of deep learning in video analysis, we first introduce two-stream co...

Predicting net joint moments during a weightlifting exercise with a neural network model.

Journal of biomechanics
The purpose of this study was to develop and train a Neural Network (NN) that uses barbell mass and motions to predict hip, knee, and ankle Net Joint Moments (NJM) during a weightlifting exercise. Seven weightlifters performed two cleans at 85% of th...

Assessing Breast Cancer Risk with an Artificial Neural Network.

Asian Pacific journal of cancer prevention : APJCP
Objectives: Radiologists face uncertainty in making decisions based on their judgment of breast cancer risk. Artificial intelligence and machine learning techniques have been widely applied in detection/recognition of cancer. This study aimed to esta...

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

Bone
INTRODUCTION: The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis.

Electrical resistivity imaging inversion: An ISFLA trained kernel principal component wavelet neural network approach.

Neural networks : the official journal of the International Neural Network Society
The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve ab...

Depth Reconstruction from Single Images Using a Convolutional Neural Network and a Condition Random Field Model.

Sensors (Basel, Switzerland)
This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Conv...

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

eNeuro
Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is...