AIMC Topic: Neural Networks, Computer

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Synchronization of recurrent neural networks with unbounded delays and time-varying coefficients via generalized differential inequalities.

Neural networks : the official journal of the International Neural Network Society
In this paper, we revisit the drive-response synchronization of a class of recurrent neural networks with unbounded delays and time-varying coefficients, contrary to usual in the literature about time-varying neural networks, the signs of self-feedba...

Deep learning-based identification of acute ischemic core and deficit from non-contrast CT and CTA.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
The accurate identification of irreversible infarction and salvageable tissue is important in planning the treatments for acute ischemic stroke (AIS) patients. Computed tomographic perfusion (CTP) can be used to evaluate the ischemic core and deficit...

A machine learning approach to predicting risk of myelodysplastic syndrome.

Leukemia research
BACKGROUND: Early myelodysplastic syndrome (MDS) diagnosis can allow physicians to provide early treatment, which may delay advancement of MDS and improve quality of life. However, MDS often goes unrecognized and is difficult to distinguish from othe...

Measuring and modeling the motor system with machine learning.

Current opinion in neurobiology
The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experime...

A deep network construction that adapts to intrinsic dimensionality beyond the domain.

Neural networks : the official journal of the International Neural Network Society
We study the approximation of two-layer compositions f(x)=g(ϕ(x)) via deep networks with ReLU activation, where ϕ is a geometrically intuitive, dimensionality reducing feature map. We focus on two intuitive and practically relevant choices for ϕ: the...

Deep probabilistic tracking of particles in fluorescence microscopy images.

Medical image analysis
Tracking of particles in temporal fluorescence microscopy image sequences is of fundamental importance to quantify dynamic processes of intracellular structures as well as virus structures. We introduce a probabilistic deep learning approach for fluo...

A novel stacking technique for prediction of diabetes.

Computers in biology and medicine
BACKGROUND: Machine Learning (ML) represents a rapidly growing technology that supplies the most effective solutions for solving complex problems. The application of ML techniques in healthcare is gaining more attention because of ML-associated autom...

Non-line-of-Sight Imaging via Neural Transient Fields.

IEEE transactions on pattern analysis and machine intelligence
We present a neural modeling framework for non-line-of-sight (NLOS) imaging. Previous solutions have sought to explicitly recover the 3D geometry (e.g., as point clouds) or voxel density (e.g., within a pre-defined volume) of the hidden scene. In con...

BlockQNN: Efficient Block-Wise Neural Network Architecture Generation.

IEEE transactions on pattern analysis and machine intelligence
Convolutional neural networks have gained a remarkable success in computer vision. However, most popular network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generat...

Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks.

Biosensors
Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient's condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians wi...