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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] Hybrid CNN and XGBoost model tuned by modified arithmetic optimization algorithm for COVID-19 early diagnostics from X-ray images
Develo** countries have had numerous obstacles in diagnosing the COVID-19 worldwide
pandemic since its emergence. One of the most important ways to control the spread of this …
pandemic since its emergence. One of the most important ways to control the spread of this …
Modified firefly algorithm for workflow scheduling in cloud-edge environment
Edge computing is a novel technology, which is closely related to the concept of Internet of
Things. This technology brings computing resources closer to the location where they are …
Things. This technology brings computing resources closer to the location where they are …
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 …
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 …
[HTML][HTML] Hybrid fruit-fly optimization algorithm with k-means for text document clustering
The fast-growing Internet results in massive amounts of text data. Due to the large volume of
the unstructured format of text data, extracting relevant information and its analysis becomes …
the unstructured format of text data, extracting relevant information and its analysis becomes …
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 …