Abstract Spiking Neural Networks (SNN) are recognised as well-suited for processing
spatiotemporal information with ultra-low energy consumption. However, proposals based …

This study compares the results of two completely different models: statistical one (ARIMA)
and deep learning one (LSTM) based on a chosen set of NASDAQ data. Both models are …

In recent years, artificial neural networks have been found successful applications in
process monitoring within metallurgy, chemical engineering and mechanical manufacturing …

When a fluid flows over a solid surface, it creates a thin boundary layer where the flow
velocity is influenced by the surface through viscosity, and can transition from laminar to …

The growing game's popularity is caused by their possibilities in decision-making training.
Management games allow avoiding potential mistakes related to the business choices, but …

The usage of machine learning methods in the financial sector, regarding repayment
prediction or forecasting, is quite a new topic, constantly gaining in importance. The concept …

On-device computing, or edge computing, is becoming increasingly important for remote
sensing, particularly in applications like deep network-based perception on on-orbit …

脉冲神经网络(SNN) 是更具生物可解释性的新一代人工神经网络, 具有独特的信息编码处理方式
, 丰富的时空动力学特性, 低功耗事件驱动工作模式等优势, 近年来受到广泛关注并在医疗健康 …

The potential of neuromorphic (NM) solutions often lies in their low-SWaP (Size, Weight, and
Power) capabilities, which often drive their application to domains that could benefit from …

This paper delivers up-to-date bibliometric research dealing with deep learning used in
forecasting alternative costs in leasing. The study presents a theoretical approach that is …