AIMC Topic: Neural Networks, Computer

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New Insights Into Drug Repurposing for COVID-19 Using Deep Learning.

IEEE transactions on neural networks and learning systems
The coronavirus disease 2019 (COVID-19) has continued to spread worldwide since late 2019. To expedite the process of providing treatment to those who have contracted the disease and to ensure the accessibility of effective drugs, numerous strategies...

Patch-Based U-Net Model for Isotropic Quantitative Differential Phase Contrast Imaging.

IEEE transactions on medical imaging
Quantitative differential phase-contrast (qDPC) imaging is a label-free phase retrieval method for weak phase objects using asymmetric illumination. However, qDPC imaging with fewer intensity measurements leads to anisotropic phase distribution in re...

Downsampled Imaging Geometric Modeling for Accurate CT Reconstruction via Deep Learning.

IEEE transactions on medical imaging
X-ray computed tomography (CT) is widely used clinically to diagnose a variety of diseases by reconstructing the tomographic images of a living subject using penetrating X-rays. For accurate CT image reconstruction, a precise imaging geometric model ...

Laplacian Pyramid Neural Network for Dense Continuous-Value Regression for Complex Scenes.

IEEE transactions on neural networks and learning systems
Many computer vision tasks, such as monocular depth estimation and height estimation from a satellite orthophoto, have a common underlying goal, which is regression of dense continuous values for the pixels given a single image. We define them as den...

Accuracy Versus Simplification in an Approximate Logic Neural Model.

IEEE transactions on neural networks and learning systems
An approximate logic neural model (ALNM) is a novel single-neuron model with plastic dendritic morphology. During the training process, the model can eliminate unnecessary synapses and useless branches of dendrites. It will produce a specific dendrit...

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI.

IEEE transactions on neural networks and learning systems
Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research pro...

Finite- and Fixed-Time Cluster Synchronization of Nonlinearly Coupled Delayed Neural Networks via Pinning Control.

IEEE transactions on neural networks and learning systems
In this article, the cluster synchronization problem for a class of the nonlinearly coupled delayed neural networks (NNs) in both finite- and fixed-time cases are investigated. Based on the Lyapunov stability theory and pinning control strategy, some...

A Shape-Constrained Neural Data Fusion Network for Health Index Construction and Residual Life Prediction.

IEEE transactions on neural networks and learning systems
With the rapid development of sensor technologies, multisensor signals are now readily available for health condition monitoring and remaining useful life (RUL) prediction. To fully utilize these signals for a better health condition assessment and R...

Incremental Unsupervised Domain-Adversarial Training of Neural Networks.

IEEE transactions on neural networks and learning systems
In the context of supervised statistical learning, it is typically assumed that the training set comes from the same distribution that draws the test samples. When this is not the case, the behavior of the learned model is unpredictable and becomes d...

Interval-Based Least Squares for Uncertainty-Aware Learning in Human-Centric Multimedia Systems.

IEEE transactions on neural networks and learning systems
Machine learning (ML) methods are popular in several application areas of multimedia signal processing. However, most existing solutions in the said area, including the popular least squares, rely on penalizing predictions that deviate from the targe...