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

1 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 DOISearch in Google Scholar

2 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 DOISearch in Google Scholar

3 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 DOISearch in Google Scholar

4 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 DOISearch in Google Scholar

5 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 DOISearch in Google Scholar

6 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 DOISearch in Google Scholar

7 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

8 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 DOISearch in Google Scholar

9 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 DOISearch in Google Scholar

10 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 DOISearch in Google Scholar

11 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 DOISearch in Google Scholar

12 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 DOISearch in Google Scholar

13 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 DOISearch in Google Scholar

14 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 DOISearch in Google Scholar

15 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

16 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

17 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 DOISearch in Google Scholar

18 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 DOISearch in Google Scholar

19 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 DOISearch in Google Scholar

20 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 DOISearch in Google Scholar

21 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 DOISearch in Google Scholar

22 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 DOISearch in Google Scholar

23 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 DOISearch in Google Scholar

24 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 DOISearch in Google Scholar

25 Clerc, M. Particle Swarm Optimization. – Particle Swarm Optimization, 2010, pp. 1942-1948. DOI: 10.1002/9780470612163. Open DOISearch in Google Scholar

26 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 DOISearch in Google Scholar

27 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 DOISearch in Google Scholar

28 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 DOISearch in Google Scholar

29 COVID Live – Coronavirus Statistics – Worldometer. (Accessed 14 June 2022). https://www.worldometers.info/coronavirus/ Search in Google Scholar

30 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 DOISearch in Google Scholar

31 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 DOISearch in Google Scholar

32 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 DOISearch in Google Scholar

33 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

34 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

35 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

36 Mirjaliliab, S., A. Lewisa. The Whale Optimization Algorithm. – Adv. Eng. Softw., Vol. 95, May 2016, pp. 51-67. Search in Google Scholar

37 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

38 Mirjalili, A., S. Mirjalili. Seyedali Mirjalili. 2015 (Accessed 01 January 2019). http://www.alimirjalili.com/Projects.html Search in Google Scholar