PERTANIKA JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

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• Abbasi, B., Jahromi, A. H. E., Arkat, J., & Hosseinkouchack, M. (2006). Estimating the parameters of Weibull distribution using a simulated annealing algorithm. Applied Mathematics and Computation, 183(1), 85-93. https://doi.org/10.1016/j.amc.2006.05.063

• Abbasi, B., Niaki, S. T. A., Khalife, M. A., & Faize, Y. (2011). A hybrid variable neighborhood search and simulated annealing algorithm to estimate the three parameters of the Weibull distribution. Expert Systems with Applications, 38(1), 700-708. https://doi.org/10.1016/j.eswa.2010.07.022

• Abubakar, H., & Danrimi, M. L. (2021). Hopfield type of artificial neural network via election algorithm as heuristic search method for random boolean ksatisfiability. International Journal of Computing and Digital System, 10(2), 660-673. http://dx.doi.org/10.12785/ijcds/100163

• Abubakar, H., Rijal, S., Sabri, S. R. M., Masanawa, S. A., & Yusuf, S. (2020a). Modified election algorithm in hopfield neural network for optimal random k satisfiability representation. International Journal for Simulation and Multidisciplinary Design Optimization, 16(11), 1–13. https://doi.org/10.1051/smdo/2020008

• Abubakar, H., M, S. A., Yusuf, S., & Abdurrahman, Y. (2020b). Discrete artificial dragonflies algorithm in agent based modelling for exact boolean k satisfiability problem. Journal of Advances in Mathematics and Computer Science, 35(4), 115-134. https://doi.org/10.9734/JAMCS/2020/v35i430275

• Abubakari, A. G., Kandza-Tadi, C. C., & Moyo, E. (2021). Modified Beta Inverse Flexible Weibull Extension Distribution. Annals of Data Science, 1-29. https://doi.org/10.1007/s40745-021-00330-3

• Almazah, M. M. A., Erbayram, T., Akdoğan, Y., AL Sobhi, M. M., & Afify, A. Z. (2021). A new extended geometric distribution: Properties, regression model, and actuarial applications. Mathematics, 9(12), 1336. https://doi.org/10.3390/math9121336

• Almetwally, E. M. (2021). Extended odd weibull inverse rayleigh distribution with application on carbon fibres. Mathematical Sciences Letters, 10(1), 5-14. https://doi.org/10.18576/msl/100102

• Alrashidi, M., Rahman, S., & Pipattanasomporn, M. (2020). Metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds. Renewable Energy, 149, 664-681. https://doi.org/10.1016/j.renene.2019.12.048

• Alzaatreh, A., Lee, C., & Famoye, F. (2013). A new method for generating families of continuous distributions. Metron, 71(1), 63-79. https://doi.org/10.1007/s40300-013-0007-y

• Alzaeemi, S. A., & Sathasivam, S. (2020). Artificial immune system in doing 2-satisfiability based reverse analysis method via a radial basis function neural network. Processes, 8(10), Article 1295. https://doi.org/10.3390/pr8101295Bidrama, H., Behboodian, J., & Towhidib, M. (2013). The beta weibull-geometric distribution. Journal of Statistical Computation and Simulation, 83(1), 52-67. https://doi.org/10.1080/00949655.2011.603089

• Boonta, S., & Boonthiem, S. (2019). An approximation of minimum initial capital of investment discrete time surplus process with Weibull distribution in a reinsurance company. Journal of Applied Mathematics, 2019, Article 2191509. https://doi.org/10.1155/2019/2191509

• Chauhan, S. K., & Malik, S. C. (2017). Evaluation of reliability and MTSF of a parallel system with Weibull failure laws. Journal of Reliability and Statistical Studies, 10(1), 137-148.

• Datsiou, K. C., & Overend, M. (2018). Weibull parameter estimation and goodness-of-fit for glass strength data. Structural Safety, 73, 29-41. https://doi.org/10.1016/j.strusafe.2018.02.002

• Freitas de Andrade, C., dos Santos, L. F., Macedo, M. V. S., Rocha, P. A. C., & Gomes, F. F. (2019). Four heuristic optimization algorithms applied to wind energy: Determination of Weibull curve parameters for three Brazilian sites. International Journal of Energy and Environmental Engineering, 10, 1-12. https://doi.org/10.1007/s40095-018-0285-5

• Dodge, Y. (2008). Kolmogorov–Smirnov test. In The concise encyclopedia of statistics (pp. 283-287). Springer. https://doi.org/10.1007/978-0-387-32833-1_214

• Elmahdy, E. E., & Aboutahoun, A. W. (2013). A new approach for parameter estimation of finite Weibull mixture distributions for reliability modeling. Applied Mathematical Modelling, 37(4), 1800-1810. http://doi.org/10.1016/j.apm.2012.04.023

• Guedes, K. S., de Andrade, C. F., Rocha, P. A., Mangueira, R. D. S., & de Moura, E. P. (2020). Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions. Applied Energy, 268, Article 114952. https://doi.org/10.1016/j.apenergy.2020.114952

• Guerra, R. R., Peña-Ramírez, F. A., & Bourguignon, M. (2020). The unit extended Weibull families of distributions and its applications. Journal of Applied Statistics, 1-19. https://doi.org/10.1080/02664763.2020.1796936

• Hashmi, S., Ahsan, M., Haq, U., Muhammad, R., & Ozel, G. (2019). The Weibull-Moment Exponential Distribution: Properties, Characterizations & applications. Journal of Reliability and Statistical Studies, 12(1), 1-22.

• Hirose, H. (2002). Maximum likelihood parameter estimation in the extended Weibull distribution and its applications to breakdown voltage estimation. IEEE Transactions on Dielectrics and Electrical Insulation, 9(4), 524–536. https://doi.org/10.1109/TDEI.2002.1024429

• Kaba, A., & Suzer, A. E. (2021). Metaheuristic data fitting methods to estimate Weibull parameters for wind speed data: A case study of Hasan Polatkan Airport. The Aeronautical Journal, 125(1287), 916-948. https://doi.org/10.1017/aer.2020.136

• Kellison, S. G. (2009). The theory of interest (3rd Ed.). McGraw-Hill Education.

• Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680. https://doi.org/10.1126/science.220.4598.671

• Lee, C., Famoye, F., & Alzaatreh, A. Y. (2013). Methods for generating families of univariate continuous distributions in the recent decades. Wiley Interdisciplinary Reviews: Computational Statistics, 5(3), 219-238. https://doi.org/10.1002/wics.1255

• Liao, Q., Ahmad, Z., Mahmoudi, E., & Hamedani, G. G. (2020). A new flexible bathtub-shaped modification of the Weibull model: Properties and applications. Mathematical Problems in Engineering, 2020, Article 3206257. https://doi.org/10.1155/2020/3206257

• Okafor, E. G., Ezugwu, O. E., Jemitola, P. O., Sun, Y., & Lu, Z. (2018). Weibull parameter estimation using particle swarm optimization algorithm. International Journal of Engineering and Technology (UAE), 7(3), 7-10. https://doi.org/10.14419/ijet.v7i3.32.18380

• Okasha, H. M., & Basheer, A. M. (2020). On marshall-olkin extended inverse weibull distribution: Properties and estimation using type-II censoring data. Journal of Statistics Applications & Probability Letters, 7(1), 9-21. https://doi.org/10.18576/jsapl/070102

• Phani, K.K. (1987). A New Modified Weibull Distribution. Communications of the American Ceramic Society, 184(August), 182-184. https://doi.org/10.1111/j.1151-2916.1987.tb05719.x

• Pobočíková, I., Sedliačková, Z., & Michalková, M. (2018). Transmuted Weibull distribution and its applications. MATEC Web of Conferences, 157, 1-11. https://doi.org/10.1051/matecconf/201815708007

• Sabri, S. R. M., & Sarsour, W. M. (2019). Modelling on stock investment valuation for long-term strategy. Journal of Investment and Management, 8(3), 60-66. https://doi.org/10.11648/j.jim.20190803.11

• Sarhan, A. M., & Zaindin, M. (2009). Modified Weibull distribution. Applied Sciences, 11(January 2000), 123-136. https://doi.org/10.1051/matecconf/201815708007

• Sarsour, W. M., & Sabri, S. R. M. (2020a). Evaluating the investment in the Malaysian construction sector in the long-run using the modified internal rate of return: A Markov chain approach. The Journal of Asian Finance, Economics, and Business, 7(8), 281–287. https://doi.org/10.13106/jafeb.2020.vol7.no8.281

• Sarsour, W. M., & Sabri, S. R. M. (2020b). Forecasting the long-run behavior of the stock price of some selected companies in the Malaysian construction sector: A Markov chain approach. International Journal of Mathematical, Engineering and Management Sciences, 5(2), 296-308. https://doi.org/10.33889/IJMEMS.2020.5.2.024

• Sathasivam, S., Mansor, M., Kasihmuddin, M. S. M., & Abubakar, H. (2020). Election algorithm for random k satisfiability in the Hopfield neural network. Processes, 8(5), Article 568. https://doi.org/10.3390/pr8050568

• Tang, Y., Xie, M., Lai, C. D., & Goh, T. N. (2002). Statistical analysis of a Weibull extension model, communications in statistics. Theory and Methods, 32(5), 913-928. https://doi.org/10.1081/STA-120019952

• Thomas, G. M. (1995). Weibull parameter estimation using genetic algorithms and a heuristic approach to cut-set analysis (Doctoral dissertation). Ohio University, USA.

• Wang, M., & Elbatal, I. (2015). The modified Weibull geometric distribution. Metron, 73(3), 303-315. https://doi.org/10.1007/s40300-014-0052-1

• Yonar, A., & Pehlivan, N. Y. (2020). Artificial bee colony with levy flights for parameter estimation of 3-p Weibull distribution. Iranian Journal of Science and Technology, Transactions: Science, 44, 851-864. https://doi.org/10.1007/s40995-020-00886-4