ArticleSN computer science2023
DeepCov: Effective Prediction Model of COVID-19 Using CNN Algorithm.
Article in SN computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
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
1 citing paper in PubMed.
- Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery.Advanced materials (Deerfield Beach, Fla.) · 2025Review
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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 outbreak prediction is a challenging and complicated problem in a vast dataset. Several communities have proposed various methods to predict the COVID-19-positive cases. However, conventional techniques remain drawbacks to predicting the actual trend cases. In this experiment, we adopt CNN to build our model by analyzing features from the vast COVID-19 dataset to predict long-term outbreaks to present early prevention. Our model can achieve adequate accuracy with a tiny loss based on the experiment results. In this study, we calculate the function which produces RMSE 0.00070 and MAPE 0.02440 to predict new cases and get RMSE 0.00468 and MAPE 0.06446 for predicting new deaths. Therefore, our proposed method can accurately predict the trend of positive cases in the COVID-19 outbreak.
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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.