ArticleChaos, solitons, and fractals2020
Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed.
- COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal.Sensors (Basel, Switzerland) · 2024Article
- Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data.Frontiers in public health · 2024Article
- A novel bidirectional LSTM deep learning approach for COVID-19 forecasting.Scientific reports · 2023Article
- Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.Applied soft computing · 2023Article
- A comprehensive review of COVID-19 detection with machine learning and deep learning techniques.Health and technology · 2023Article
- A survey of machine learning-based methods for COVID-19 medical image analysis.Medical & biological engineering & computing · 2023Review
- A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases.Computers in biology and medicine · 2023Article
- EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats.Sensors (Basel, Switzerland) · 2023Article
- Multi-Attention Segmentation Networks Combined with the Sobel Operator for Medical Images.Sensors (Basel, Switzerland) · 2023Article
- Analysis of computational intelligence approaches for predicting disease severity in humans: Challenges and research guidelines.Journal of education and health promotion · 2023Review
- Analysis of COVID-19 Death Cases Using Machine Learning.SN computer science · 2023Article
- A novel approach for COVID-19 Infection forecasting based on multi-source deep transfer learning.Computers in biology and medicine · 2022Article
- AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions.Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research · 2022Review
- A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images.Biomedical signal processing and control · 2022Article
- ISW-LM: An intensive symptom weight learning mechanism for early COVID-19 diagnosis.Computers in biology and medicine · 2022Article
- Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Transfer Learning for COVID-19 cases and deaths forecast using LSTM network.ISA transactions · 2022Article
- An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown.Healthcare (Basel, Switzerland) · 2022Article
- Objective evaluation of deep uncertainty predictions for COVID-19 detection.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The World Health Organization (WHO) declared novel coronavirus 2019 (COVID-19), an infectious epidemic caused by SARS-CoV-2, as Pandemic in March 2020. It has affected more than 40 million people in 216 countries. Almost in all the affected countries, the number of infected and deceased patients has been enhancing at a distressing rate. As the early prediction can reduce the spread of the virus, it is highly desirable to have intelligent prediction and diagnosis tools. The inculcation of efficient forecasting and prediction models may assist the government in implementing better design strategies to prevent the spread of virus. In this paper, a state-of-the-art analysis of the ongoing machine learning (ML) and deep learning (DL) methods in the diagnosis and prediction of COVID-19 has been done. Moreover, a comparative analysis on the impact of machine learning and other competitive approaches like mathematical and statistical models on COVID-19 problem has been conducted. In this study, some factors such as type of methods(machine learning, deep learning, statistical & mathematical) and the impact of COVID research on the nature of data used for the forecasting and prediction of pandemic using computing approaches has been presented. Finally some important research directions for further research on COVID-19 are highlighted which may facilitate the researchers and technocrats to develop competent intelligent models for the prediction and forecasting of COVID-19 real time data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.