AIMC Topic: Neural Networks, Computer

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Self-Attention-Based Deep Learning Network for Regional Influenza Forecasting.

IEEE journal of biomedical and health informatics
Early prediction of influenza plays an important role in minimizing the damage caused, as it provides the resources and time needed to formulate preventive measures. Compared to traditional mechanistic approach, deep/machine learning-based models hav...

End-to-End Automatic Morphological Classification of Intracranial Pressure Pulse Waveforms Using Deep Learning.

IEEE journal of biomedical and health informatics
OBJECTIVE: Mean intracranial pressure (ICP) is commonly used in the management of patients with intracranial pathologies. However, the shape of the ICP signal over a single cardiac cycle, called ICP pulse waveform, also contains information on the st...

Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model.

Journal of healthcare engineering
According to statistics, stroke is the second or third leading cause of death and adult disability. Stroke causes losing control of the motor function, paralysis of body parts, and severe back pain for which a physiotherapist employs many therapies t...

Using multiple linear regression and BP neural network to predict critical meteorological conditions of expressway bridge pavement icing.

PloS one
Icy bridge deck in winter has tremendous consequences for expressway traffic safety, which is closely related to the bridge pavement temperature. In this paper, the critical meteorological conditions of icy bridge deck were predicted by multiple line...

Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection.

PloS one
With the development of recent years, the field of deep learning has made great progress. Compared with the traditional machine learning algorithm, deep learning can better find the rules in the data and achieve better fitting effect. In this paper, ...

Anomalous diffusion dynamics of learning in deep neural networks.

Neural networks : the official journal of the International Neural Network Society
Learning in deep neural networks (DNNs) is implemented through minimizing a highly non-convex loss function, typically by a stochastic gradient descent (SGD) method. This learning process can effectively find generalizable solutions at flat minima. I...

Quantitative features to assist in the diagnostic assessment of chronic lymphocytic leukemia progression.

The Journal of pathology
The use of artificial intelligence methods in the image-based diagnostic assessment of hematological diseases is a growing trend in recent years. In these methods, the selection of quantitative features that describe cytological characteristics plays...

Feature-based intelligent models for optimisation of percussive drilling.

Neural networks : the official journal of the International Neural Network Society
As a rotary-percussion system, the vibro-impact drilling (VID) system utilises resonantly induced high frequency periodic impacts alongside existing drill-string rotation to cut through downhole rock layers. Due to the inhomogeneous nature of the roc...

Quantifying the reproducibility of graph neural networks using multigraph data representation.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have witnessed an unprecedented proliferation in tackling several problems in computer vision, computer-aided diagnosis and related fields. While prior studies have focused on boosting the model accuracy, quantifying the ...

Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition.

IEEE transactions on pattern analysis and machine intelligence
Recognizing multiple labels of an image is a practical yet challenging task, and remarkable progress has been achieved by searching for semantic regions and exploiting label dependencies. However, current works utilize RNN/LSTM to implicitly capture ...