Forecasting carbon price trends based on an interpretable light gradient boosting machine and Bayesian optimization
The future carbon price is crucial to relevant companies, investors, and carbon
policymakers, and the significance of carbon price prediction research is self-evident …
policymakers, and the significance of carbon price prediction research is self-evident …
Hodrick–Prescott filter-based hybrid ARIMA–SLFNs model with residual decomposition scheme for carbon price forecasting
Accurate carbon pricing guidance is of great importance for the inhibition of excessive
carbon dioxide emissions. Aiming at improving forecast performance, a number of carbon …
carbon dioxide emissions. Aiming at improving forecast performance, a number of carbon …
A hybrid sparrow search algorithm of the hyperparameter optimization in deep learning
Deep learning has been widely used in different fields such as computer vision and speech
processing. The performance of deep learning algorithms is greatly affected by their …
processing. The performance of deep learning algorithms is greatly affected by their …
Synergy of small antiviral molecules on a black-phosphorus nanocarrier: machine learning and quantum chemical simulation insights
Favipiravir (FP) and Ebselen (EB) belong to a broad range of antiviral drugs that have
shown active potential as medications against many viruses. Employing molecular dynamics …
shown active potential as medications against many viruses. Employing molecular dynamics …
Seepage behavior assessment of earth-rock dams based on Bayesian network
L He, S Wang, Y Gu, Q Pang, Y Wu… - … of Distributed Sensor …, 2021 - journals.sagepub.com
Seepage behavior assessment is an important part of the safety operation assessment of
earth-rock dams, because of insufficient intelligent analysis of monitoring information …
earth-rock dams, because of insufficient intelligent analysis of monitoring information …
Bayesian optimisation of functions on graphs
The increasing availability of graph-structured data motivates the task of optimising over
functions defined on the node set of graphs. Traditional graph search algorithms can be …
functions defined on the node set of graphs. Traditional graph search algorithms can be …
Optimizing slope safety factor prediction via stacking using sparrow search algorithm for multi-layer machine learning regression models
K Shui, K Hou, W Hou, J Sun, H Sun - Journal of Mountain Science, 2023 - Springer
The safety factor is a crucial quantitative index for evaluating slope stability. However, the
traditional calculation methods suffer from unreasonable assumptions, complex soil …
traditional calculation methods suffer from unreasonable assumptions, complex soil …
Fossil energy market price prediction by using machine learning with optimal hyper-parameters: A comparative study
S Lahmiri - Resources Policy, 2024 - Elsevier
Fossil energy markets are important commodities, and their price fluctuations impact
worldwide economy and financial markets. Hence, it is essential to forecast the prices of …
worldwide economy and financial markets. Hence, it is essential to forecast the prices of …
[HTML][HTML] Development of an expert system for assessing failures in oil and gas pipelines due to microbiologically influenced corrosion (MIC)
AA Abilio, JD Wolodko, RB Eckert… - Engineering Failure …, 2024 - Elsevier
This study highlights the modeling of an expert system for the classification of internal
microbiologically influenced corrosion (MIC) failures related to pipelines in the upstream oil …
microbiologically influenced corrosion (MIC) failures related to pipelines in the upstream oil …
Bayesian Optimization of Functions over Node Subsets in Graphs
We address the problem of optimizing over functions defined on node subsets in a graph.
The optimization of such functions is often a non-trivial task given their combinatorial, black …
The optimization of such functions is often a non-trivial task given their combinatorial, black …