Improved Barnacle Mating Optimizer-Based Least Square Support Vector Machine to Predict COVID-19 Confirmed Cases with Total Vaccination

Ahmed, Marzia; Sulaiman, Mohd Herwan; Mohamad, Ahmad Johari

doi:10.2478/cait-2023-0007

References

Landi, F., et al. The New Challenge of Geriatrics: Saving Frail Older People from the SARS-COV-2 Pandemic Infection. – Journal of Nutrition, Health and Aging, Vol. 24, 2020, No 5, pp. 466-470. DOI: 10.1007/s12603-020-1356-x.
Open DOI Search in Google Scholar Back to article
Landi, F., et al. Post-COVID-19 Global Health Strategies: The Need for an Interdisciplinary Approach. – Aging Clinical and Experimental Research, Vol. 32, 2020, No 8, pp. 1613-1620. DOI: 10.1007/s40520-020-01616-x.
Open DOI Search in Google Scholar Back to article
Cheng, Z. J., J. Shan. 2019 Novel Coronavirus: Where We Are and What We Know. – Infection, Vol. 48, 2020, No 2, pp. 155-163. DOI: 10.1007/s15010-020-01401-y.
Open DOI Search in Google Scholar Back to article
Guan, W. J., et al. China Medical Treatment Expert Group for COVID-19. Clinical Characteristics of Coronavirus Disease 2019 in China. – N. Engl. J. Med., Vol. 382, 30 April 2020, No 18, pp. 1708-1720. DOI: 10.1056/NEJMoa2002032. Epub 2020 Feb 28. PMID: 32109013, PMCID: PMC7092819.
Open DOI Search in Google Scholar Back to article
Quintana, V. H., M. Santos-Nieto. Reactive-Power Dispatch by Successive Quadratic Programming. – IEEE Transactions on Energy Conversion, Vol. 4, 1989, No 3, pp. 425-435. DOI: 10.1109/60.43245.
Open DOI Search in Google Scholar Back to article
Aceves-Lara, C. A., et al. Kinetic Parameters Estimation in an Anaerobic Digestion Process Using Successive Quadratic Programming. – Water Science and Technology, Vol. 52, 2005, No 1-2, pp. 419-426. DOI: 10.2166/wst.2005.0548.
Open DOI Search in Google Scholar Back to article
Xie, Y. F. Reduced Hessian Successive Quadratic Programming Algorithm. – Large-Scale Optimization with Applications, Part I: Optimization in Inverse Problems and Design, Vol. 92, p. 195.
Search in Google Scholar Back to article
Wan, C., J. Wang, J. Lin, Y. Song, Z. Y. Dong. Nonparametric Prediction Intervals of Wind Power via Linear Programming. – IEEE Transactions on Power Systems, Vol. 33, 2018, No 1, pp. 1074-1076. DOI: 10.1109/TPWRS.2017.2716658.
Open DOI Search in Google Scholar Back to article
Lee, E. K., T. L. Wu. Classification and Disease Prediction via Mathematical Programming. – Data Mining, Systems Analysis and Optimization in Biomedicine, Vol. 953. 2007. DOI: 10.1063/1.2817343.
Open DOI Search in Google Scholar Back to article
Cuéllar, M. P., M. Delgado, M. C. Pegalajar. An Application of Non-Linear Programming to Train Recurrent Neural Networks in Time Series Prediction Problems. – In: Proc. of 7th International Conference on Enterprise Information Systems (ICEIS’2005), 2005, No Lm, pp. 35-42. DOI: 10.1007/978-1-4020-5347-4_11.
Open DOI Search in Google Scholar Back to article
Mangasarian, O. L., W. N. Street, W. H. Wolberg. Breast Cancer Diagnosis and Prognosis via Linear Programming. – Operations Research, Vol. 43, 1995, No 4, pp. 570-577. DOI: 10.1287/opre.43.4.570.
Open DOI Search in Google Scholar Back to article
Zarrin, P., M. Maleki, Z. Khodadai, R. B. Arellano-Valle. Time Series Models Based on the Unrestricted Skew-Normal Process. – Journal of Statistical Computation and Simulation, Vol. 89, 2019, No 1, pp. 38-51. DOI: 10.1080/00949655.2018.1533962.
Open DOI Search in Google Scholar Back to article
Ghasami, S., M. Maleki, Z. Khodadadi. Leptokurtic and Platykurtic Class of Robust Symmetrical and Asymmetrical Time Series Models. – Journal of Computational and Applied Mathematics, Vol. 376, 2020, p. 112806. DOI: 10.1016/j.cam.2020.112806.
Open DOI Search in Google Scholar Back to article
Maleki, M., A. R. Nematollahi. Autoregressive Models with Mixture of Scale Mixtures of Gaussian Innovations. – Iranian Journal of Science and Technology, Transaction A: Science, Vol. 41, 2017, No 4, pp. 1099-1107. DOI: 10.1007/s40995-017-0237-6.
Open DOI Search in Google Scholar Back to article
Mustaffa, Z., M. H. Sulaiman. COVID-19 Confirmed Cases Prediction in China Based on Barnacles Mating Optimizer-Least Squares Support Vector Machines. – Cybernetics and Information Technologies, Vol. 21, 2021, No 4, pp. 62-76.
Search in Google Scholar Back to article
Mustaffa, Z., H. Sulaiman, K. A. M. Rosli, M. F. M. Mohsin, Y. Yusof. Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm. – Cybernetics and Information Technologies, Vol. 20, 2020, No 4, pp. 156-169.
Search in Google Scholar Back to article
Li, G., K. Chen, H. Yang. A New Hybrid Prediction Model of Cumulative COVID-19 Confirmed Data. – Process Safety and Environmental Protection, Vol. 157, 2022, pp. 1-19. DOI: 10.1016/j.psep.2021.10.047.
Open DOI Search in Google Scholar Back to article
Maleki, M., M. R. Mahmoudi, D. Wraith, K. H. Pho. Time Series Modelling to Forecast the Confirmed and Recovered Cases of COVID-19. – Travel Medicine and Infectious Disease, Vol. 37, 2020, No March, p. 101742. DOI: 10.1016/j.tmaid.2020.101742.
Open DOI Search in Google Scholar Back to article
Talkhi, N., N. Akhavan Fatemi, Z. Ataei, M. Jabbari Nooghabi. Modeling and Forecasting Number of Confirmed and Death Caused COVID-19 in IRAN: A Comparison of Time Series Forecasting Methods. – Biomedical Signal Processing and Control, Vol. 66, 2021, No November 2020, p. 102494. DOI: 10.1016/j.bspc.2021.102494.
Open DOI Search in Google Scholar Back to article
Chimmula, V. K. R., L. Zhang. Time Series Forecasting of COVID-19 Transmission in Canada Using LSTM Networks. – Chaos, Solitons and Fractals, Vol. 135, 2020. DOI: 10.1016/j.chaos.2020.109864.
Open DOI Search in Google Scholar Back to article
Zeroual, A., F. Harrou, A. Dairi, Y. Sun. Deep Learning Methods for Forecasting COVID-19 Time-Series Data: A Comparative Study. – Chaos, Solitons and Fractals, Vol. 140, 2020, p. 110121. DOI: 10.1016/j.chaos.2020.110121.
Open DOI Search in Google Scholar Back to article
Shastri, S., K. Singh, S. Kumar, P. Kour, V. Mansotra. Time Series Forecasting of COVID-19 Using Deep Learning Models: India-USA Comparative Case study. – Chaos, Solitons and Fractals, Vol. 140, 2020, p. 110227. DOI: 10.1016/j.chaos.2020.110227.
Open DOI Search in Google Scholar Back to article
Kumar, N., S. Susan. COVID-19 Pandemic Prediction Using Time Series Forecasting Models. – In: Proc. of 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT’20), 2020. DOI: 10.1109/ICCCNT49239.2020.9225319.
Open DOI Search in Google Scholar Back to article
Alassafi, M. O., M. Jarrah, R. Alotaibi. Time Series Predicting of COVID-19 Based on Deep Learning. – Neurocomputing, Vol. 468, 2022, pp. 335-344. DOI: 10.1016/j.neucom.2021.10.035.
Open DOI Search in Google Scholar Back to article
Clerc, M. Particle Swarm Optimization. – Particle Swarm Optimization, 2010, pp. 1942-1948. DOI: 10.1002/9780470612163.
Open DOI Search in Google Scholar Back to article
Gold, J. E., R. A. Okyay, W. E. Licht, D. J. Hurley. Investigation of Long Covid Prevalence and Its Relationship to Epstein-Barr Virus Reactivation. – Pathogens, Vol. 10, 2021, No 6, pp. 1-15. DOI: 10.3390/pathogens10060763.
Open DOI Search in Google Scholar Back to article
Mirjalili, S., S. M. Mirjalili, A. Lewis. Grey Wolf Optimizer. – Advances in Engineering Software, Vol. 69, 2014, pp. 46-61. DOI: 10.1016/j.advengsoft.2013.12.007.
Open DOI Search in Google Scholar Back to article
Mirjalili, S. Moth-Flame Optimization Algorithm: A Novel Nature-Inspired Heuristic Paradigm. – Knowledge-Based Systems, Vol. 89, 2015, pp. 228-249. DOI: 10.1016/j.knosys.2015.07.006.
Open DOI Search in Google Scholar Back to article
COVID Live – Coronavirus Statistics – Worldometer. (Accessed 14 June 2022). https://www.worldometers.info/coronavirus/
Search in Google Scholar Back to article
Sulaiman, M. H., Z. Mustaffa, M. M. Saari, H. Daniyal. Barnacles Mating Optimizer: A New Bio-Inspired Algorithm for Solving Engineering Optimization Problems. – Engineering Applications of Artificial Intelligence, Vol. 87, 2020, pp. 265-270. DOI: 10.1016/j.engappai.2019.103330.
Open DOI Search in Google Scholar Back to article
Sulaiman, M. H., Z. Mustaffa, M. M. Saari, H. Daniyal, I. Musirin, M. R. Daud. Barnacles Mating Optimizer: An Evolutionary Algorithm for Solving Optimization. – In: Proc. of IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS’18), October 2018, pp. 99-104. DOI: 10.1109/I2CACIS.2018.8603703.
Open DOI Search in Google Scholar Back to article
Sulaiman, M. H., et al. Barnacles Mating Optimizer: A Bio-Inspired Algorithm for Solving Optimization Problems. – In: Proc. of 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD’18), Vol. 87, June 2018, No September 2019, pp. 265-270. DOI: 10.1109/SNPD.2018.8441097.
Open DOI Search in Google Scholar Back to article
Abdullah, J. M., T. Ahmed. Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process. – IEEE Access, Vol. 7, 2019, pp. 43473-43486.
Search in Google Scholar Back to article
Price, K. V., N. H. Awad, M. Z. Ali, P. N. Suganthan. The 100-Digit Challenge: Problem de_nitions and Evaluation Criteria for the 100-Digit Challenge Special Session and Competition on Single Objective Numerical Optimization. – School Elect. Electron. Eng., Nanyang Technol. Univ. of Singapore, Tech. Rep., November 2018.
Search in Google Scholar Back to article
Mirjalili, S. Dragon_y Algorithm: A New Meta-Heuristic Optimization Technique for Solving Single-Objective, Discrete, and Multi-Objective Problems. – Neural Comput. Appl., Vol. 27, May 2015, No 4, pp. 1053-1073.
Search in Google Scholar Back to article
Mirjaliliab, S., A. Lewisa. The Whale Optimization Algorithm. – Adv. Eng. Softw., Vol. 95, May 2016, pp. 51-67.
Search in Google Scholar Back to article
Mirjalilia, S., A. H. Gandomibf, S. Z. Mirjalili, C. Saremia, H. Farisd, S. M. Mirjalilie. Salp Swarm Algorithm: A Bio-Inspired Optimizer for Engineering Design Problems. – Adv. Eng. Softw., Vol. 114, December 2017, pp. 163-191.
Search in Google Scholar Back to article
Mirjalili, A., S. Mirjalili. Seyedali Mirjalili. 2015 (Accessed 01 January 2019). http://www.alimirjalili.com/Projects.html
Search in Google Scholar Back to article