ArticleSustainable cities and society2021
COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach.
Article in Sustainable cities and society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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57 citing papers in PubMed.
- Unlocking data elements potential for enhanced urban public health emergency governance: Configuration analysis based on 23 mega-cities in China.Global health & medicine · 2026Article
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- Analyzing the causes of sports injuries in college sports activities and research on the recovery strategy using an intelligent approach.Scientific reports · 2025Article
- An improved beetle antennae search algorithm and its application in coverage of wireless sensor networks.Scientific reports · 2024Article
- Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN.Scientific reports · 2024Article
- Machine learning-based prediction of sertraline concentration in patients with depression through therapeutic drug monitoring.Frontiers in pharmacology · 2024Article
- Intolerance of uncertainty and mental health in China "Post-pandemic" age: The mediating role of difficulties in emotion regulation.PloS one · 2024Article
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- Short-term forecasting of vegetable prices based on LSTM model-Evidence from Beijing's vegetable data.PloS one · 2024Article
- Artificial Intelligence in Evaluation of Permanent Impairment: New Operational Frontiers.Healthcare (Basel, Switzerland) · 2023Article
- Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach.Engineering applications of artificial intelligence · 2023Article
- A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate.Toxics · 2023Article
- A novel firefly algorithm approach for efficient feature selection with COVID-19 dataset.Microprocessors and microsystems · 2023Article
- Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection.Heliyon · 2023Article
- Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020-2022.Diagnostics (Basel, Switzerland) · 2023Article
- A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms.Socio-economic planning sciences · 2023Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The main objective of this paper is to further improve the current time-series prediction (forecasting) algorithms based on hybrids between machine learning and nature-inspired algorithms. After the recent COVID-19 outbreak, almost all countries were forced to impose strict measures and regulations in order to control the virus spread. Predicting the number of new cases is crucial when evaluating which measures should be implemented. The improved forecasting approach was then used to predict the number of the COVID-19 cases. The proposed prediction model represents a hybridized approach between machine learning, adaptive neuro-fuzzy inference system and enhanced beetle antennae search swarm intelligence metaheuristics. The enhanced beetle antennae search is utilized to determine the parameters of the adaptive neuro-fuzzy inference system and to improve the overall performance of the prediction model. First, an enhanced beetle antennae search algorithm has been implemented that overcomes deficiencies of its original version. The enhanced algorithm was tested and validated against a wider set of benchmark functions and proved that it substantially outperforms original implementation. Afterwards, the proposed hybrid method for COVID-19 cases prediction was then evaluated using the World Health Organization's official data on the COVID-19 outbreak in China. The proposed method has been compared against several existing state-of-the-art approaches that were tested on the same datasets. The proposed CESBAS-ANFIS achieved
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