AIMC Topic: Neural Networks, Computer

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CTNN: A Convolutional Tensor-Train Neural Network for Multi-Task Brainprint Recognition.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Brainprint is a new type of biometric in the form of EEG, directly linking to intrinsic identity. Currently, most methods for brainprint recognition are based on traditional machine learning and only focus on a single brain cognition task. Due to the...

Deep neural networks with promising diagnostic accuracy for the classification of atypical femoral fractures.

Acta orthopaedica
Background and purpose - A correct diagnosis is essential for the appropriate treatment of patients with atypical femoral fractures (AFFs). The diagnostic accuracy of radiographs with standard radiology reports is very poor. We derived a diagnostic a...

Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Unsupervised anomaly discovery in stream data is a research topic with many practical applications. However, in many cases, it is not easy to collect enough training data with labeled anomalies for supervised learning of an anomaly detector in order ...

Dense Residual Network: Enhancing global dense feature flow for character recognition.

Neural networks : the official journal of the International Neural Network Society
Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Network (DenseNet), have achieved great success for image representation learning by capturing deep hierarchical features. However, most existing network architectures of simply s...

Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort.

Diabetes & metabolism journal
BACKGROUND: Previously developed prediction models for type 2 diabetes mellitus (T2DM) have limited performance. We developed a deep learning (DL) based model using a cohort representative of the Korean population.

Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran.

Biomedical journal
BACKGROUND: COVID-19 is an infectious disease that started spreading globally at the end of 2019. Due to differences in patient characteristics and symptoms in different regions, in this research, a comparative study was performed on COVID-19 patient...

Ultrasound image reconstruction from plane wave radio-frequency data by self-supervised deep neural network.

Medical image analysis
Image reconstruction from radio-frequency (RF) data is crucial for ultrafast plane wave ultrasound (PWUS) imaging. Compared with the traditional delay-and-sum (DAS) method based on relatively imprecise assumptions, sparse regularization (SR) method d...

Propagation source identification of infectious diseases with graph convolutional networks.

Journal of biomedical informatics
Source identification in networks has drawn considerable interest to understand and control the infectious disease propagation processes. It is usually difficult to achieve both high accuracy and short error distance when we try to solve the problem....

R-JaunLab: Automatic Multi-Class Recognition of Jaundice on Photos of Subjects with Region Annotation Networks.

Journal of digital imaging
Jaundice occurs as a symptom of various diseases, such as hepatitis, the liver cancer, gallbladder or pancreas. Therefore, clinical measurement with special equipment is a common method that is used to identify the total serum bilirubin level in pati...