AIMC Topic: Neural Networks, Computer

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Designing Deep Neural Networks Robust to Sensor Failure in Mobile Health Environments.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Missing data is a very common challenge in health monitoring systems and one reason for that is that they are largely dependent on different types of sensors. A critical characteristic of the sensor-based prediction systems is their dependency on har...

Stress Inference from Abdominal Sounds using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Stress is often considered the 21 century's epidemic, affecting more than a third of the globe's population. Long-term exposure to stress has significant side effects on physical and mental health. In this work we propose a methodology for detecting ...

Complex dynamics in a Hopfield neural network under electromagnetic induction and electromagnetic radiation.

Chaos (Woodbury, N.Y.)
Due to the potential difference between two neurons and that between the inner and outer membranes of an individual neuron, the neural network is always exposed to complex electromagnetic environments. In this paper, we utilize a hyperbolic-type memr...

Data-driven reduced-order modeling of spatiotemporal chaos with neural ordinary differential equations.

Chaos (Woodbury, N.Y.)
Dissipative partial differential equations that exhibit chaotic dynamics tend to evolve to attractors that exist on finite-dimensional manifolds. We present a data-driven reduced-order modeling method that capitalizes on this fact by finding a coordi...

Reducing echo state network size with controllability matrices.

Chaos (Woodbury, N.Y.)
Echo state networks are a fast training variant of recurrent neural networks excelling at approximating nonlinear dynamical systems and time series prediction. These machine learning models act as nonlinear fading memory filters. While these models b...

Dark soliton detection using persistent homology.

Chaos (Woodbury, N.Y.)
Classifying images often requires manual identification of qualitative features. Machine learning approaches including convolutional neural networks can achieve accuracy comparable to human classifiers but require extensive data and computational res...

Detection of live breast cancer cells in bright-field microscopy images containing white blood cells by image analysis and deep learning.

Journal of biomedical optics
SIGNIFICANCE: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug tes...

Mitigating the ambiguity problem in the CNN-based wavefront correction.

Optics letters
In this work, we propose an attention-based adaptive optics method that uses a non-local block to integrate phase diversity with a convolutional neural network (CNN). The simulation results showcase the effectiveness of the proposed method to mitigat...

Long short-term memory neural network for directly inverse design of nanofin metasurface.

Optics letters
In this Letter, the neural network long short-term memory (LSTM) is used to quickly and accurately predict the polarization sensitivity of a nanofin metasurface. In the forward prediction, we construct a deep neural network (DNN) with the same struct...