ArticleSoft computing2022
India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability.
Article in Soft computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Hybrid feature selection with novel deep learning model for COVID-19 risk prediction.Scientific reports · 2026Article
- Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model.Scientific reports · 2025Article
- FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease.Internet of things (Amsterdam, Netherlands) · 2023Article
- An adaptive multi-modal hybrid model for classifying thyroid nodules by combining ultrasound and infrared thermal images.BMC bioinformatics · 2023Article
- Multi-weight susceptible-infected model for predicting COVID-19 in China.Neurocomputing · 2023Article
- COVID-19 forecasting using shifted Gaussian Mixture Model with similarity-based estimation.Expert systems with applications · 2023Article
- Trends in using deep learning algorithms in biomedical prediction systems.Frontiers in neuroscience · 2023Article
- An Intelligent ABM-based Framework for Developing Pandemic-Resilient Urban Spaces in Post-COVID Smart Cities.Procedia computer science · 2023Article
- COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control.Frontiers in artificial intelligence · 2023Review
- Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images.Pattern recognition · 2022Article
- Weather Conditions and COVID-19 Cases: Insights from the GCC Countries.Intelligent systems with applications · 2022Article
- VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants.Computer methods and programs in biomedicine · 2022Article
- COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level.Sensors (Basel, Switzerland) · 2022Article
- COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization.Frontiers in public health · 2022Article
- Machine learning applications for COVID-19 outbreak management.Neural computing & applications · 2022Review
- Wushu Routine Movement and Diagnosis Based on Deep Learning and Symmetric Difference Algorithm.Computational intelligence and neuroscience · 2022Article
- COVID-19 Outbreak Forecasting Based on Vaccine Rates and Tweets Classification.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Retraction · 2023-05-22Concerns/Issues about Referencing/Attributions · Compromised Peer Review · Investigation by Journal/Publisher · Rogue Editor · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The epidemic situation may cause severe social and economic impacts on a country. So, there is a need for a trustworthy prediction model that can offer better prediction results. The forecasting result will help in making the prevention policies and remedial action in time, and thus, we can reduce the overall social and economic impacts on the country. This article introduces a CNN-LSTM hybrid deep learning prediction model, which can correctly forecast the COVID-19 epidemic across India. The proposed model uses convolutional layers, to extract meaningful information and learn from a given time series dataset. It is also enriched with the LSTM layer's capability, which means it can identify long-term and short-term dependencies. The experimental evaluation has been performed to gauge the performance and suitability of our proposed model among the other well-established time series forecasting models. From the empirical analysis, it is also clear that the use of extra convolutional layers with the LSTM layer may increase the forecasting model's performance. Apart from this, the deep insides of the current situation of medical resource availability across India have been discussed.
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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.