AIMC Topic: Neural Networks, Computer

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Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm.

PloS one
Copper is an important mineral and fluctuations in copper prices can affect the stable functioning of some countries' economies. Policy makers, futures traders and individual investors are very concerned about copper prices. In a recent paper, we use...

Significant wave height prediction from X-band marine radar images using deep learning with 3D convolutions.

PloS one
This research introduces a deep learning method for ocean wave height estimation utilizing a Convolutional Neural Network (CNN) based on the VGGNet. The model is trained on a dataset comprising buoy wave heights and radar images, both critical for ma...

GRAND: GAN-based software runtime anomaly detection method using trace information.

Neural networks : the official journal of the International Neural Network Society
Software runtime anomaly detection can detect manifestations (known as anomalies) caused by faults in complex systems before they lead to failure. Whereas most existing methods use external performance indicators, this study uses internal execution t...

Reservoir computing models based on spiking neural P systems for time series classification.

Neural networks : the official journal of the International Neural Network Society
Nonlinear spiking neural P (NSNP) systems are neural-like membrane computing models with nonlinear spiking mechanisms. Because of this nonlinear spiking mechanism, NSNP systems can show rich nonlinear dynamics. Reservoir computing (RC) is a novel rec...

Predicting mortality in brain stroke patients using neural networks: outcomes analysis in a longitudinal study.

Scientific reports
In this study, Neural Networks (NN) modelling has emerged as a promising tool for predicting outcomes in patients with Brain Stroke (BS) by identifying key risk factors. In this longitudinal study, we enrolled 332 patients form Imam hospital in Ardab...

VC dimensions of group convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society
We study the generalization capacity of group convolutional neural networks. We identify precise estimates for the VC dimensions of simple sets of group convolutional neural networks. In particular, we find that for infinite groups and appropriately ...

Boosting fine-tuning via Conditional Online Knowledge Transfer.

Neural networks : the official journal of the International Neural Network Society
Fine-tuning is an effective technique to enhance network performance in scenarios with limited labeled data. To achieve this, recent methods exploit the knowledge mined in the source model (e.g., feature maps) to construct an extra regularization sig...

Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Food research international (Ottawa, Ont.)
Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypert...

How deep is the brain? The shallow brain hypothesis.

Nature reviews. Neuroscience
Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical...

Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction.

ACS synthetic biology
Predicting properties of proteins is of interest for basic biological understanding and protein engineering alike. Increasingly, machine learning (ML) approaches are being used for this task. However, the accuracy of such ML models typically degrades...