Recent advances and application of machine learning in food flavor prediction and regulation
H Ji, D Pu, W Yan, Q Zhang, M Zuo, Y Zhang - Trends in Food Science & …, 2023 - Elsevier
Background Food flavor is a key factor affecting sensory quality. Predicting and regulating
flavor can result in exceptional flavor characteristics and improve consumer preferences and …
flavor can result in exceptional flavor characteristics and improve consumer preferences and …
Machine learning in energy economics and finance: A review
Abstract Machine learning (ML) is generating new opportunities for innovative research in
energy economics and finance. We critically review the burgeoning literature dedicated to …
energy economics and finance. We critically review the burgeoning literature dedicated to …
Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN
The vibration signal of the axle box bearing of the train is affected by the track excitation and
the random noise of the environment. The vibration signal is nonlinear and non-stationary …
the random noise of the environment. The vibration signal is nonlinear and non-stationary …
Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization
Estimating the price of crude oil, which is seen as an important resource for economic
development and stability in the world, is a topic of great interest by policy makers and …
development and stability in the world, is a topic of great interest by policy makers and …
A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series
Forecasting the future price of crude oil, which has an important role in the global economy,
is considered as a hot matter for both investment companies and governments. However …
is considered as a hot matter for both investment companies and governments. However …
Oil price forecasting: A hybrid GRU neural network based on decomposition–reconstruction methods
Significant fluctuations in the price of crude oil in recent years make accurate price
estimations of critical importance. A reliable method for crude oil price forecasting is …
estimations of critical importance. A reliable method for crude oil price forecasting is …
Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series
The investigation of the accuracy of methods employed to forecast agricultural commodities
prices is an important area of study. In this context, the development of effective models is …
prices is an important area of study. In this context, the development of effective models is …
A hybrid model for carbon price forecasting using GARCH and long short-term memory network
The reform of the EU ETS markets in 2017 has induced new carbon price forecasting
challenges. This study proposes a novel decomposition-ensemble paradigm VMD …
challenges. This study proposes a novel decomposition-ensemble paradigm VMD …
Forecasting crude oil prices based on variational mode decomposition and random sparse Bayesian learning
Accurately forecasting crude oil prices has drawn much attention from researchers,
investors, producers, and consumers. However, the complexity of crude oil prices makes it a …
investors, producers, and consumers. However, the complexity of crude oil prices makes it a …
Forecasting the US oil markets based on social media information during the COVID-19 pandemic
Accurate oil market forecasting plays an important role in the theory and application of oil
supply chain management for profit maximization and risk minimization. However, the …
supply chain management for profit maximization and risk minimization. However, the …