An innovative numerical method based on a machine learning approach is presented in order to model the electrochemical behaviour of an Acanthocereus tetragonus aqueous extract on Aluminum in acidic and neutral media. Experimental data of an electrochemical evaluation of Aluminum in HCl (1M) and...

Accurate PV (Photovoltaic) power output forecasting is very important for making economic and reliable power dispatching plans. At present, the PV power prediction technology is still in the primary stage, especially for the forecasting cases with large forecasting time and when the weather...

Purified Terephthalic Acid (PTA) wastewater sludge generated from the treatment of PTA wastewater can generate energy through pyrolysis and gasification. In this study, the combustion and pyrolysis behavior and kinetics of the sludge produced from the thermochemical treatment PTA wastewater were...

The quantile regression neural network ( QRNN ) has shown high potential for predicting the mechanical properties of the alloy. The QRNN model and the regression model were developed to predict the mechanical properties of the low-pressure cast aluminum alloy ZL702A using the mechanical...

One barrier to more widespread adoption of differentially private neural networks is the entailed accuracy loss. To address this issue, the relationship between neural network architectures and model accuracy under differential privacy constraints needs to be better understood. As a first step,...

Using a supervised neural network (NN) trained once on a one-dimensional lattice of 200 sites, we calculate the Berezinskii–Kosterlitz–Thouless phase transitions of the two-dimensional (2D) classical XY and the 2D generalized classical XY models. In particular, both the bulk quantities...

The revolution in neural networks is a significant technological shift. It has an impact on not only all aspects of production and life, but also economic research. Neural networks have not only been a significant tool for economic study in recent years, but have also become an important topic...

With the advances in time-series prediction, several recent developments in machine learning have shown that integrating prediction methods into portfolio selection is a great opportunity. In this paper, we propose a novel approach to portfolio formation strategy based on a hybrid machine...