Abstract: In the post Moore’s era, conventional electronic digital computing platforms have encountered escalating challenges to support massively parallel and energy-hungry artificial intelligence ...
Inspired by how the human brain works, neurocomputing algorithms, including deep learning, reinforcement learning, and neurodynamic optimization, have achieved tremendous success in various ...
A new approach developed at the University of Surrey takes direct inspiration from biological neural networks of the human brain University of Surrey researchers have developed a new approach to ...
In a study published in Neurocomputing, researchers from Surrey’s Nature-Inspired Computation and Engineering (NICE) group have shown that mimicking the brain’s sparse and structured neural wiring can ...
This is the PyTorch implementation of ``FCN+: Global Receptive Convolution Makes FCN Great Again'' which has been accepted to NeuroComputing. NeuroComputing version, ArXiv version. Our FCN+ is built ...
Due to the laboratory regulations, the article has not been officially published before the model source code is not allowed to be published, so the current repository is not complete, but do not ...
ABSTRACT: This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, ...
ABSTRACT: Financial anomaly detection is crucial for maintaining market order and protecting investor interests. This study explores the application of machine learning in financial anomaly detection.
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