A Comprehensive Analysis Using Maximum Likelihood Estimation and Artificial Neural Networks for Modeling Arthritic Pain Relief Data

References

[1] M. A. Ariana, B. Vaferi and G. Karimi, Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks, Powder Technol. 278 (2015), 1–10. 10.1016/j.powtec.2015.03.005Suche in Google Scholar

[2] H. M. Almongy, E. M. Almetwally, H. M. Aljohani, A. S. Alghamdi and E. H. Hafez, A new extended Rayleigh distribution with applications of COVID-19 data, Results Phys. 23 (2021), Article ID 104012. 10.1016/j.rinp.2021.104012Suche in Google Scholar PubMed PubMed Central

[3] M. Bebbington, C. D. Lai and R. Zitikis, A flexible Weibull extension, Reliab. Eng. Syst. Safety 92 (2007), no. 6, 719–726. 10.1016/j.ress.2006.03.004Suche in Google Scholar

[4] H. W. Block, T. H. Savits and H. Singh, The reversed hazard rate function, Probab. Engrg. Inform. Sci. 12 (1998), no. 1, 69–90. 10.1017/S0269964800005064Suche in Google Scholar

[5] N. K. Chandra and D. Roy, Some results on reversed hazard rate, Probab. Engrg. Inform. Sci. 15 (2001), no. 1, 95–102. 10.1017/S0269964801151077Suche in Google Scholar

[6] A. B. Çolak, T. N. Sindhu, S. A. Lone, M. T. Akhtar and A. Shafiq, A comparative analysis of maximum likelihood estimation and artificial neural network modeling to assess electrical component reliability, Qual. Reliab. Eng. Int. 40 (2024), no. 1, 91–114. 10.1002/qre.3233Suche in Google Scholar

[7] G. M. Cordeiro, E. M. M. Ortega and S. Nadarajah, The Kumaraswamy Weibull distribution with application to failure data, J. Franklin Inst. 347 (2010), no. 8, 1399–1429. 10.1016/j.jfranklin.2010.06.010Suche in Google Scholar

[8] J. Ferrero Bermejo, J. F. Gómez Fernández, F. Olivencia Polo and A. Crespo Márquez, A review of the use of artificial neural network models for energy and reliability prediction, Appl. Sci. 9 (2019), no. 9, Article ID 1844. 10.3390/app9091844Suche in Google Scholar

[9] M. Finkelstein, On the reversed hazard rate, Reliab. Eng. Syst. Safety 78 (2002), 71–75. 10.1016/S0951-8320(02)00113-8Suche in Google Scholar

[10] M. C. Liu, W. Kuo and T. Sastri, An exploratory study of a neural network approach for reliability data analysis, Qual. Reliab. Eng. Int 11 (1995), no. 2, 107–112. 10.1002/qre.4680110206Suche in Google Scholar

[11] J. P. Mills, Table of the ratio: Area to bounding ordinate, for any portion of normal curve, Biometrika 18 (1926), no. 3–4, 395–400. 10.1093/biomet/18.3-4.395Suche in Google Scholar

[12] A. Shafiq, A. B. Çolak, S. A. Lone, T. N. Sindhu and T. Muhammad, Reliability modeling and analysis of mixture of exponential distributions using artificial neural network, Math. Methods Appl. Sci. 47 (2024), no. 5, 3308–3328. 10.1002/mma.8178Suche in Google Scholar

[13] A. Shafiq, A. B. Çolak and T. Naz Sindhu, Designing artificial neural network of nanoparticle diameter and solid-fluid interfacial layer on single-walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles, Internat. J. Numer. Methods Fluids 93 (2021), no. 12, 3384–3404. 10.1002/fld.5038Suche in Google Scholar

[14] A. Shafiq, A. B. Çolak, T. N. Sindhu, S. A. Lone and T. A. Abushal, Modeling and survival exploration of breast carcinoma: A statistical, maximum likelihood estimation, and artificial neural network perspective, Artif. Intell. Life Sci., 4 (2023), Article ID 100082. 10.1016/j.ailsci.2023.100082Suche in Google Scholar

[15] A. Shafiq, A. B. Çolak, T. N. Sindhu, S. A. Lone, A. Alsubie and F. Jarad, Comparative study of artificial neural network versus parametric method in COVID-19 data analysis, Results Phys. 38 (2022), Article ID 105613. 10.1016/j.rinp.2022.105613Suche in Google Scholar PubMed PubMed Central

[16] A. Shafiq, A. B. Çolak, T. N. Sindhu and T. Muhammad, Optimization of Darcy–Forchheimer squeezing flow in nonlinear stratified fluid under convective conditions with artificial neural network, Heat Transf. Res. 53 (2022), no. 3, 67–89. 10.1615/HeatTransRes.2021041018Suche in Google Scholar

[17] A. Shafiq, A. B. Çolak, C. Swarup, T. N. Sindhu and S. A. Lone, Reliability analysis based on mixture of Lindley distributions with artificial neural network, Adv. Theory Simul. 5 (2022), no. 8, Article ID 2200100. 10.1002/adts.202200100Suche in Google Scholar

[18] T. N. Sindhu, A. B. Çolak, S. A. Lone, A. Shafiq and T. A. Abushal, A decreasing failure rate model with a novel approach to enhance the artificial neural network’s structure for engineering and disease data analysis, Tribology Internat 192 (2024), Article ID 109231. 10.1016/j.triboint.2023.109231Suche in Google Scholar

[19] T. N. Sindhu, A. Shafiq and Z. Huassian, Generalized exponentiated unit Gompertz distribution for modeling arthritic pain relief times data: Classical approach to statistical inference, J. Biopharmaceutical Stat. 34 (2024), no. 3, 323–348. 10.1080/10543406.2023.2210681Suche in Google Scholar PubMed

[20] R. M. Usman and M. A. U. Haq, Some remarks on odd Burr III Weibull distribution, Ann. Data Sci. 6 (2019), 21–38. 10.1007/s40745-019-00191-xSuche in Google Scholar

[21] I. Waini, A. Ishak and I. Pop, Dufour and Soret effects on Al 2 O 3 -water nanofluid flow over a moving thin needle: Tiwari and Das model, Int. J. Numer. Methods Heat Fluid Flow 31 (2021), no. 3, 766–782. 10.1108/HFF-03-2020-0177Suche in Google Scholar

[22] D. R. Wingo, Maximum likelihood methods for fitting the Burr type XII distribution to life test data, Biometrical J. 25 (1983), no. 1, 77–84. 10.1002/bimj.19830250109Suche in Google Scholar