Methods of forecasting electric energy consumption: A literature review
RV Klyuev, ID Morgoev, AD Morgoeva, OA Gavrina… - Energies, 2022 - mdpi.com
Balancing the production and consumption of electricity is an urgent task. Its implementation
largely depends on the means and methods of planning electricity production. Forecasting is …
largely depends on the means and methods of planning electricity production. Forecasting is …
A machine learning approach for corrosion small datasets
In this work, we developed a QSAR model using the K-Nearest Neighbor (KNN) algorithm to
predict the corrosion inhibition performance of the inhibitor compound. To overcome the …
predict the corrosion inhibition performance of the inhibitor compound. To overcome the …
Machine learning models for estimating above ground biomass of fast growing trees
W Wongchai, T Onsree, N Sukkam… - Expert Systems with …, 2022 - Elsevier
Biomass is a renewable and sustainable energy resource that can potentially be substituted
for fossil fuels, which have a negative impact on the environment including the production of …
for fossil fuels, which have a negative impact on the environment including the production of …
A methodology to determine the optimal train-set size for autoencoders applied to energy systems
P Danti, A Innocenti - Advanced Engineering Informatics, 2023 - Elsevier
In the latest years, deep learning has been massively used to face problems that have not
been solved by means of classical approaches. In particular, an autoencoder is a popular …
been solved by means of classical approaches. In particular, an autoencoder is a popular …
Effect of Training Data Ratio and Normalizing on Fatigue Lifetime Prediction of Aluminum Alloys with Machine Learning
It is critical to evaluate the estimation of the fatigue lifetimes for the piston aluminum alloys,
particularly in the automotive industry. This paper investigates the effect of different …
particularly in the automotive industry. This paper investigates the effect of different …
[HTML][HTML] Machine learning for the prediction of proteolysis in Mozzarella and Cheddar cheese
Proteolysis is a complex biochemical event during cheese storage that affects both
functionality and quality, yet there are few tools that can accurately predict proteolysis for …
functionality and quality, yet there are few tools that can accurately predict proteolysis for …
Prediction of idiopathic recurrent spontaneous miscarriage using machine learning
Recurrent spontaneous miscarriage (RSM) is defined as the spontaneous loss of two or
more clinically diagnosed pregnancies within 20 weeks of gestation. Despite extensive …
more clinically diagnosed pregnancies within 20 weeks of gestation. Despite extensive …
Generalised learning of time-series: Ornstein-Uhlenbeck processes
M Süzen, A Yegenoglu - arXiv preprint arXiv:1910.09394, 2019 - arxiv.org
In machine learning, statistics, econometrics and statistical physics, cross-validation (CV) is
used asa standard approach in quantifying the generalisation performance of a statistical …
used asa standard approach in quantifying the generalisation performance of a statistical …
[PDF][PDF] Machine learning-assisted decision support in industrial manufacturing: a case study on injection molding machine selection
F Tayalati, S Idiri, I Boukrouh, A Azmani, M Azmani - Int J Artif Intell ISSN - researchgate.net
Selecting the right injection molding machine for new products remains a challenging task
that significantly influences the profitability and flexibility of companies. The conventional …
that significantly influences the profitability and flexibility of companies. The conventional …