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An Improved Optimization Algorithm-Based Prediction Approach for the Weekly Trend of COVID-19 Considering the Total Vaccination in Malaysia: A Novel Hybrid Machine Learning Approach

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Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 618))

Abstract

SARS-CoV-2 is a multi-organ disease characterized by a wide range of symptoms, which also causes the severe acute respiratory syndrome. When it initially began, it rapidly spread from its origin to adjacent nations, infecting millions of people around the globe. In order to take appropriate preventative and precautionary actions, it is necessary to anticipate positive COVID-19 instances in order to better comprehend future risks. Therefore, it is vital to building mathematical models that are resilient and have as few prediction mistakes as feasible. This research recommends an optimization-based Least Square Support Vector Machines (LSSVM) for forecasting COVID-19 confirmed cases along with the daily total vaccination frequency. In this work, a novel hybrid Barnacle Mating Optimizer (BMO) via Gauss Distribution is combined with the Least Squares Support Vector Machines algorithm for time series forecasting. The data source consists of the daily occurrences of cases and frequency of total vaccination from February 24, 2021, to July 27, 2022, in Malaysia. LSSVM will thereafter conduct the prediction job with the optimized hyper-parameter values using BMO via Gauss distribution. This study concludes, based on its experimental findings, that hybrid IBMOLSSVM outperforms cross validations, original BMO, ANN, and a few other hybrid approaches with optimally optimized parameters.

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Acknowledgements

This research study was supported by Ministry of Education Malaysia (MOE) and Universiti Malaysia Pahang under Fundamental Research Grant Scheme (FRGS/1/2019/ICT02/UMP/03/1) & (#RDU1901133).

Author information

Authors and Affiliations

  1. Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang (UMP), Pahang, Malaysia

    Marzia Ahmed, Mohd Herwan Sulaiman & Ahmad Johari Mohamad

  2. Department of Software Engineering, Daffodil Smart City, Daffodil International University, Ashulia, Dhaka, Bangladesh

    Marzia Ahmed

  3. Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh

    Mostafijur Rahman

Authors
  1. Marzia Ahmed
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  2. Mohd Herwan Sulaiman
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  3. Ahmad Johari Mohamad
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  4. Mostafijur Rahman
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Corresponding author

Correspondence to Marzia Ahmed .

Editor information

Editors and Affiliations

  1. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  2. Mawlana Bhashani Science and Technology, Dhaka, Bangladesh

    Sajjad Waheed

  3. National Institute for Materials Science, Tsukuba, Japan

    Anirban Bandyopadhyay

  4. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  5. Amity University, Jaipur, India

    Kanad Ray

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Ahmed, M., Sulaiman, M.H., Mohamad, A.J., Rahman, M. (2023). An Improved Optimization Algorithm-Based Prediction Approach for the Weekly Trend of COVID-19 Considering the Total Vaccination in Malaysia: A Novel Hybrid Machine Learning Approach. In: Kaiser, M.S., Waheed, S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-19-9483-8_18

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