Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data
- Title
- Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data
- Creator
- Mohanraj G.; Mohanraj V.; Marimuthu M.; Sathiyamoorthi V.; Luhach A.K.; Kumar S.
- Description
- A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values. 2022 Informa UK Limited, trading as Taylor & Francis Group.
- Source
- Journal of Experimental and Theoretical Artificial Intelligence, Vol-35, No. 3, pp. 377-393.
- Date
- 2023-01-01
- Publisher
- Taylor and Francis Ltd.
- Subject
- COVID-19; deep learning model; machine learning model; time series forecast
- Coverage
- Mohanraj G., Department of Computer Science and Engineering, Sona College of Technology, TN, Salem, India; Mohanraj V., Department of Computer Science and Engineering, Sona College of Technology, TN, Salem, India; Marimuthu M., Department of Computer Science and Engineering, Sona College of Technology, TN, Salem, India; Sathiyamoorthi V., Department of Computer Science and Engineering, Sona College of Technology, TN, Salem, India; Luhach A.K., Department of Electrical and Communication Engineering, The PNG University of Technology, Papua New Guinea; Kumar S., Department of Computer Science and Engineering, Christ (Deemed to Be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 0952813X
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Mohanraj G.; Mohanraj V.; Marimuthu M.; Sathiyamoorthi V.; Luhach A.K.; Kumar S., “Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/14741.