On the benefits of using metaheuristics in the hyperparameter tuning of deep learning models for energy load forecasting
An effective energy oversight represents a major concern throughout the world, and the
problem has become even more stringent recently. The prediction of energy load and …
problem has become even more stringent recently. The prediction of energy load and …
A survey of fractional calculus applications in artificial neural networks
M Joshi, S Bhosale, VA Vyawahare - Artificial Intelligence Review, 2023 - Springer
Artificial neural network (ANN) is the backbone of machine learning, specifically deep
learning. The interpolating and learning ability of an ANN makes it an ideal tool for …
learning. The interpolating and learning ability of an ANN makes it an ideal tool for …
HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting
The forecasting and estimation of wind power is a challenging problem in renewable energy
generation due to the high volatility of wind power resources, inevitable intermittency, and …
generation due to the high volatility of wind power resources, inevitable intermittency, and …
Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector
Managing energy demand and reducing greenhouse gas emissions are among the most
significant challenges ahead for many countries. Accurate prediction of energy demand and …
significant challenges ahead for many countries. Accurate prediction of energy demand and …
A survey on machine learning models for financial time series forecasting
Financial time series (FTS) are nonlinear, dynamic and chaotic. The search for models to
facilitate FTS forecasting has been highly pursued for decades. Despite major related …
facilitate FTS forecasting has been highly pursued for decades. Despite major related …
A stochastic computational intelligent solver for numerical treatment of mosquito dispersal model in a heterogeneous environment
In this paper, the design of stochastic computational intelligent solver is presented for the
solution of mathematical model representing the dynamics of mosquito dispersal in a …
solution of mathematical model representing the dynamics of mosquito dispersal in a …
A stochastic intelligent computing with neuro-evolution heuristics for nonlinear SITR system of novel COVID-19 dynamics
The present study aims to design stochastic intelligent computational heuristics for the
numerical treatment of a nonlinear SITR system representing the dynamics of novel …
numerical treatment of a nonlinear SITR system representing the dynamics of novel …
Intelligent computing with Levenberg–Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control
The aim of this work is to design an intelligent computing paradigm through Levenberg–
Marquardt artificial neural networks (LMANNs) for solving the mathematical model of Corona …
Marquardt artificial neural networks (LMANNs) for solving the mathematical model of Corona …
Robust recurrent neural networks for time series forecasting
X Zhang, C Zhong, J Zhang, T Wang, WWY Ng - Neurocomputing, 2023 - Elsevier
Recurrent neural networks (RNNs) are widely utilized in time series forecasting tasks. In
practical applications, there are noises in real-life time series data. A model's generalization …
practical applications, there are noises in real-life time series data. A model's generalization …
Metaheuristic-based hyperparameter tuning for recurrent deep learning: application to the prediction of solar energy generation
As solar energy generation has become more and more important for the economies of
numerous countries in the last couple of decades, it is highly important to build accurate …
numerous countries in the last couple of decades, it is highly important to build accurate …