AIMC Topic: Neural Networks, Computer

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Protocol to explain graph neural network predictions using an edge-centric Shapley value-based approach.

STAR protocols
Here we present EdgeSHAPer, a workflow for explaining graph neural networks by approximating Shapley values using Monte Carlo sampling. In this protocol, we describe steps to execute Python scripts for a chemical dataset from the original publication...

Ultrasound Imaging With a Flexible Probe Based on Element Array Geometry Estimation Using Deep Neural Network.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Conventionally, ultrasound (US) diagnosis is performed using hand-held rigid probes. Such devices are difficult to be used for long-term monitoring because they need to be continuously pressed against the body to remove the air between the probe and ...

Fuzzy Control of Pressure in a Water Supply Network Based on Neural Network System Modeling and IoT Measurements.

Sensors (Basel, Switzerland)
As hydroenergetic losses are inherent to water supply systems, they are a frequent issue which water utilities deal with every day. The control of network pressure is essential to reducing these losses, providing a quality supply to consumers, saving...

Detection of Image Level Forgery with Various Constraints Using DFDC Full and Sample Datasets.

Sensors (Basel, Switzerland)
The emergence of advanced machine learning or deep learning techniques such as autoencoders and generative adversarial networks, can generate images known as deepfakes, which astonishingly resemble the realistic images. These deepfake images are hard...

MLWAN: Multi-Scale Learning Wavelet Attention Module Network for Image Super Resolution.

Sensors (Basel, Switzerland)
Image super resolution (SR) is an important image processing technique in computer vision to improve the resolution of images and videos. In recent years, deep convolutional neural network (CNN) has made significant progress in the field of image SR;...

Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China.

BMC public health
BACKGROUND: This study aims to explore appropriate model for predicting the disease burden of pneumoconiosis in Tianjin by comparing the prediction effects of Autoregressive Integrated Moving Average (ARIMA) model, Deep Neural Networks (DNN) model an...

AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics.

Nature communications
Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid ...

Path sampling of recurrent neural networks by incorporating known physics.

Nature communications
Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledg...

Identification of agricultural quarantine materials in passenger's luggage using ion mobility spectroscopy combined with a convolutional neural network.

Analytical methods : advancing methods and applications
As economic globalization intensifies, the recent increase in agricultural products and travelers from abroad has led to an increase in the probability of invasive alien species. A major pathway for invasive alien species is agricultural quarantine m...

Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data.

Computational intelligence and neuroscience
The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant prot...