AIMC Topic: Neural Networks, Computer

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A Convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets.

Sensors (Basel, Switzerland)
Deep convolutional neural networks have shown remarkable performance in the image classification domain. However, Deep Learning models are vulnerable to noise and redundant information encapsulated into the high-dimensional raw input images, leading ...

Presentation Attack Detection on Limited-Resource Devices Using Deep Neural Classifiers Trained on Consistent Spectrogram Fragments.

Sensors (Basel, Switzerland)
The presented paper is concerned with detection of presentation attacks against unsupervised remote biometric speaker verification, using a well-known challenge-response scheme. We propose a novel approach to convolutional phoneme classifier training...

A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images.

International journal of environmental research and public health
COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country's economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially....

Numerical Investigations through ANNs for Solving COVID-19 Model.

International journal of environmental research and public health
The current investigations of the COVID-19 spreading model are presented through the artificial neuron networks (ANNs) with training of the Levenberg-Marquardt backpropagation (LMB), i.e., ANNs-LMB. The ANNs-LMB scheme is used in different variations...

Inter-patient automated arrhythmia classification: A new approach of weight capsule and sequence to sequence combination.

Computer methods and programs in biomedicine
OBJECTIVE: We propose a new capsule network to compensate for the information loss in the deep convolutional networks in previous studies, and to improve the performance of arrhythmia classification.

A novel design of Gudermannian function as a neural network for the singular nonlinear delayed, prediction and pantograph differential models.

Mathematical biosciences and engineering : MBE
The present work is to solve the nonlinear singular models using the framework of the stochastic computing approaches. The purpose of these investigations is not only focused to solve the singular models, but the solution of these models will be pres...

Identification of Alzheimer associated differentially expressed gene through microarray data and transfer learning-based image analysis.

Neuroscience letters
Major factors contribute to mental stress and enhance the progression of late-onset Alzheimer's disease (AD). The factors that lead to neurodegeneration, such as tau protein hyperphosphorylation and increased amyloid-beta production, can be mimicked ...

High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy.

Analytical chemistry
Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for high...

Robust Single-Probe Quantitative Ultrasonic Imaging System With a Target-Aware Deep Neural Network.

IEEE transactions on bio-medical engineering
OBJECTIVE: The speed of sound (SoS) has great potential as a quantitative imaging biomarker since it is sensitive to pathological changes in tissues. In this paper, a target-aware deep neural (TAD) network reconstructing an SoS image quantitatively f...

Mix Contrast for COVID-19 Mild-to-Critical Prediction.

IEEE transactions on bio-medical engineering
OBJECTIVE: In a few patients with mild COVID-19, there is a possibility of the infection becoming severe or critical in the future. This work aims to identify high-risk patients who have a high probability of changing from mild to critical COVID-19 (...