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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 …
[HTML][HTML] Multivariate energy forecasting via metaheuristic tuned long-short term memory and gated recurrent unit neural networks
Energy forecasting plays an important role in effective power grid management. The
widespread adoption of emerging technologies and the increased reliance on renewable …
widespread adoption of emerging technologies and the increased reliance on renewable …
Software defects prediction by metaheuristics tuned extreme gradient boosting and analysis based on shapley additive explanations
Software testing represents a crucial component of software development, and it is usually
making the difference between successful and failed projects. Although it is extremely …
making the difference between successful and failed projects. Although it is extremely …
Cloud-load forecasting via decomposition-aided attention recurrent neural network tuned by modified particle swarm optimization
Recent improvements in networking technologies have led to a significant shift towards
distributed cloud-based services. However, adequate management of computation …
distributed cloud-based services. However, adequate management of computation …
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 …
The explainable potential of coupling metaheuristics-optimized-xgboost and shap in revealing vocs' environmental fate
In this paper, we explore the computational capabilities of advanced modeling tools to
reveal the factors that shape the observed benzene levels and behavior under different …
reveal the factors that shape the observed benzene levels and behavior under different …
Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting
Power supply from renewable energy is an important part of modern power grids. Robust
methods for predicting production are required to balance production and demand to avoid …
methods for predicting production are required to balance production and demand to avoid …
[HTML][HTML] Forecasting bitcoin: Decomposition aided long short-term memory based time series modeling and its explanation with Shapley values
Bitcoin price volatility fascinates both researchers and investors, studying features that
influence its movement. This paper expends on previous research and examines time series …
influence its movement. This paper expends on previous research and examines time series …
Improving audit opinion prediction accuracy using metaheuristics-tuned XGBoost algorithm with interpretable results through SHAP value analysis
This study aims to create a machine learning model that can predict opinions in external
audits and surpass the benchmark set in a prior study from the literature. This tool could …
audits and surpass the benchmark set in a prior study from the literature. This tool could …
[HTML][HTML] Enhancing internet of things network security using hybrid cnn and xgboost model tuned via modified reptile search algorithm
This paper addresses the critical security challenges in the internet of things (IoT) landscape
by implementing an innovative solution that combines convolutional neural networks …
by implementing an innovative solution that combines convolutional neural networks …