AIMC Topic: Neural Networks, Computer

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Leakage Current Sensor and Neural Network for  MOA Monitoring.

Computational intelligence and neuroscience
Metal-oxide arrester (MOA) has been widely used in electric power systems. The leakage current monitoring of MOA can not only detect the MOA's running state continuously and intelligently but also reduce the unexpected outage of the equipment, which ...

A Model for Analyzing Teaching Quality Data of Sports Faculties Based on Particle Swarm Optimization Neural Network.

Computational intelligence and neuroscience
In this paper, we use a particle swarm optimization neural network algorithm to analyze the teaching data of physical education faculties and evaluate the quality of teaching in physical education faculties. By studying and analyzing the optimization...

China's GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model.

PloS one
This paper presents a Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) to predict China's Gross Domestic Product (GDP) fluctuation state within a rolling time window. We compare the predictive power of LSTM-HMM with ...

Artificial intelligence-based technology for semi-automated segmentation of rectal cancer using high-resolution MRI.

PloS one
AIM: Although MRI has a substantial role in directing treatment decisions for locally advanced rectal cancer, precise interpretation of the findings is not necessarily available at every institution. In this study, we aimed to develop artificial inte...

Recognition and Segmentation of Individual Bone Fragments with a Deep Learning Approach in CT Scans of Complex Intertrochanteric Fractures: A Retrospective Study.

Journal of digital imaging
The characteristics of bone fragments are the main influencing factors for the choice of treatment in intertrochanteric fractures. This study aimed to develop a deep learning algorithm for recognizing and segmenting individual fragments in CT images ...

Identification of upper GI diseases during screening gastroscopy using a deep convolutional neural network algorithm.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: The clinical application of GI endoscopy for the diagnosis of multiple diseases using artificial intelligence (AI) has been limited by its high false-positive rates. There is an unmet need to develop a GI endoscopy AI-assisted di...

A universal adversarial policy for text classifiers.

Neural networks : the official journal of the International Neural Network Society
Discovering the existence of universal adversarial perturbations had large theoretical and practical impacts on the field of adversarial learning. In the text domain, most universal studies focused on adversarial prefixes which are added to all texts...

Approximation in shift-invariant spaces with deep ReLU neural networks.

Neural networks : the official journal of the International Neural Network Society
We study the expressive power of deep ReLU neural networks for approximating functions in dilated shift-invariant spaces, which are widely used in signal processing, image processing, communications and so on. Approximation error bounds are estimated...

Evaluation of artificial neural network designs for Gafchromic™ film calibration with Tc-99m and digital photos.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine

AMB-Wnet: Embedding attention model in multi-bridge Wnet for exploring the mechanics of disease.

Gene expression patterns : GEP
In recent years, progressive application of convolutional neural networks in image processing has successfully filtered into medical diagnosis. As a prerequisite for images detection and classification, object segmentation in medical images has attra...