129
Bibliography
1. Adedimeji et. al., 2012. A Qualitative Study of Barriers to Effectiveness of Interventions to Prevent Mother-to-Child Transmission of HIV in Arba Minch, Ethiopia. International Journal of Population Research Volume, Article ID 532154, 7 pages doi:10.1155/2012/532154.
2. Alison P.O. 1999. Logistic Regression using SAS: Theory and Application. Cary, N.C.: SAS Institute Inc.
3. Alison, P.D. Logistic Regression using SAS: Theory and Application. SAS Institute Inc, (2009).
4. Archer N.P., Wang S. Application of the back-propagation neural network algorithm with monotonicity constraints for two group classification problems. Dec.Sci. 24 (1): 60- 75, 1993.
5. Atkinson A.C., Donev A.N., Tobias R.D. Optimum experimental designs with SAS. Oxford 6. Azam 2000. Biologically Inspired Modular Neural Networks. PhD Dissertation. Virginial
Polytechnic Institute and State University.
7. Bailey R.A. Design of comparative experiments. Cambridge University Press. ISBN 978- 0-
8. Bassey E.A. et. al., 2007. Attitude of antenatal attendees to people living with HIV/AIDS in Uyo, south-south Nigeria. Afr Health Sci. December; 7(4): 239–243.
9. Beal D.J., Information Criteria Methods in SAS for Multiple Linear Regression Models, SESUG Proceedings © SESUG Inc.
10. Benferoni M.C., Rossi S., Ferrari F., Stavik E., Pena- Romero A. and Caramella C. 2000.
Factorial analysis of the influence of dissolution medium on drug release from carra- geenan-diltiazem complexes’, AAPS PharmSciTech, Vol. 1, No. 2, Article 15.
11. Betechuoh B.L. et. al. 2007. Using Inverse Neural Networks for HIV Adaptive Control.
International Journal of Computational Intelligence Research. vol.3, No. 1. 11-15.
12. Bettebghor D. et. al. 2011. Surrogate Modeling Approximation using a Mixture of Ex- perts based on EM joint Estimation. Structural and Multidisciplinary Optimization. vol.
43, No. 2, 243-259.
13. Bitzer S. and Kiebel J. 2012. Recognizing Recurrent Neural Networks (rRNN): Bayesian Inference for Recurrent Neural Networks. arXiv: 1201, 4339v1.
14. Burke H.B. Artificial neural networks for cancer research: Outcome prediction. Sem- insurg. Oneal. 1994; 10 (1); 73-79.
15. Calcagno G. and Staiano Antonino. 2010. A Multilayer Neural Network-based approach for the identification of responsiveness to interferon therapy in multiple scle rosis pa- tients. Information Sciences. Vol. 180, Issue 21, 4153-4163.
16. Cassels S, Goodreau S. Interaction of mathematical modeling and social and behavioral HIV/AIDS research. Current Opinion in HIV and AIDS. 2011; 6 (2); 119-123. 12
17. Clin. Infect Dis 2002 Oct 15; 35:S135-7.
18. Deeb O. and Jawabreh M. 2012. Exploring QSARs for Inhibitory Activity of Cyclic Urea and Nonpeptide-Cyclic Cyanoguanidine Derivatives HIV-1 Protease Inhibitors by Artifi- cial Neural Network. Advances in Chemical Engineering and Science, vol. 2, 82-100.
19. Department of Health 2010. National Antenatal Sentinel HIV and Syphilis Prevalence Survey in South Africa, 2009.
20. Department of Health 2010. Protocol for implementing the National Antenatal Sen- tinel HIV and Syphilis Prevalence Survey, South Africa.
130 21. Department of Health, South Africa. Annual antenatal seroprevalence survey data,
2009.
22. Design Expert 8.0.71. StatEase software.
23. Dong Z. et. al. 2011. Injection Material Selection Method based on Optimizing Neural Network. Advances in Intelligent and Soft Computing. vol. 104, 339-344.
24. Ebell M.H. Artificial neural networks for prediction failure to survive following in- hospital cardio-pulmonary resuscitation. J. Fam. Pract. 36(3):297-303, 1993.
25. Efron B., Gong G. 1983. A leisurely look at the bootsrap, the jackknife and cross- validation. American Statistician. vol. 37, 36-48.
26. Faraggi D., Simon R. A neural network model for survival data. Stat.Med.14:73-82, 1992.
27. Fonner D.E., Buck J.R. and Banker G.S. 1970. Mathematical optimization techniques in drug product design and process analysis, J. Pharm Sci, Vol. 59, pp. 587-1596.
28. Furlanetto S., Maestrelli F., Orlandini S., Pinzauti S. and Mura P. (2003). Optimization of dissolution test precision for a ketoprofen extended-release product, J. Pharm. Bio- med Anal. Vol. 32, pp. 159-165.
29. Goodarzi M. and Freitas M.P. 2010. MIA–QSAR coupled to principal component analy- sis-adaptive neuro-fuzzy inference systems (PCA–ANFIS) for the modeling of the anti- HIV reverse transcriptase activities of TIBO derivatives. European Journal of Medicinal Chemistry vol. 45, 1352–1358.
30. Graben P.B. and Wright J. 2011. From McCulloch–Pitts Neurons Toward Biology. Socie- ty for Mathematical Biology.
31. Hart A. Using neural networks for classification tasks. Some experiments on datasets and practical advice.J.Opera. Res. Soc. 44: 1129-1145, 1992.
32. Hatzakis G.E. and Tsoukas C.M. 2002. Neural networks morbidity and mortality model- ing during loss of HIV T-cell homeostasis. Proc AMIA Symp. 320–324.
33. Hatzakis G.E. et al. 2001. Neural networks in the assessment of HIV immunopathology.
Proc AMIA Symp. 249–253.
34. Hatzakis G.E. et. al. 2005. Neural Network-longitudinal assessment of the Electronic Anti-Retroviral THerapy (EARTH) Cohort to follow response to HIV-treatment. AMIA Annu Symp Proc. 301–305.
35. Herman R.A. et. al. 1999. Comparison of a Neural Network Approach with Five Tradi- tional Methods for Predicting Creatinine Clearance in Patients with Human Immunode- ficiency Virus Infection. PHARMACOTHERAPY vol. 19, No. 6.
36. Hinkelmann et.al. Design and analysis of experiments. 2nd edition.
37. Karawita D.A. 2013. Prevalence of HIV among Antenatal Clinic Attendees in De Soysa Maternity Hospital (DMH) in Colombo, Sri Lanka. Academia © 2013.04.10.
38. Kwak N. K. and Lee C. 1997. A neural network application to classification of health sta- tus of HIV/AIDS patients. J. Med. Syst. 21(2):87–97.
39. Kwak N.K. A neural network application to classification of health status of HIV/AIDS patients. Journal of medical sciences,21(2):87-97,1997.
40. Lamers S.L. et. al. 2008. Prediction of R5, X4, and R5X4 HIV-1 Coreceptor Usage with Evolved Neural Networks. IEEE/ACM transactions on computational biology and bioin- formatics. vol. 5. No. 2.
41. Larder et. al. 2009. Application of Artificial Neural Networks for Decision Support in Medicine. Methods in Molecular Biology. Vol. 458, 119-132.
42. Lee C.W. and Park J.A. 2000. Assessment of HIV/AIDS-related health performance using an artificial neural network. Information & Management, vol. 38, 231-238
131 43. Montgomery D.C. 2008. Design and Analysis of Experiments. John Wiley and Sons.
New York, 2008.
44. Montgomery D.C. Design and Analysis of Experiments. Wiley, New York, 2008.
45. Mutnury B. Modeling and Characterization of High Speed Interfaces in Blade and Rack Servers Using Response Surface Model. Electronic Components and Technology Con- ference (ECTC), IEEE 61st.
46. Mutnury, B et. al. 2011. Modeling and Characterization of High Speed Interfaces in Blade and Rack Servers Using Response Surface Model. Electronic Components and Technology Conference (ECTC), IEEE 61st.
47. Myers, R. H. and Montgomery, D. C. 2002. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. A Wiley-Interscience publica- tion, 2nd edition, New York, 2002.
48. Myers, R.H. 2002. Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 2nd Edition, John Wiley and Sons.
49. NIST/SEMATECHe-HandbookofStatisticalMethods, http://www.itl.nist.gov/div898/handbook/, 2012/10/10.
50. Patel J.L, Goyal R.K. 2007. Applications of artificial neural networks in medical science.
Curr Clin Pharmacol. (Sep. 2007), 2(3), 217-26.
51. Paturo E., HuM.H., Hung M.S. Two group classification using neural networks.
Dec.Sci.24 (4):825-845,1993.
52. Phafoli S.H. et. al., 2007. Variables influencing delay in antenatal clinic attendance among teenagers in Lesotho. SA Fam Pract 2007:49(9).
53. Rojas R. Neural networks. A systematic introduction. Springer. Pages 502. 1996.
54. Romoren M and Rahman M. 2006. Syphilis screening in the antenatal care: a cross- sectional study from Botswana. MC International Health and Human Rights. 6:8 doi:
10.1186/1472-698X-6-8.
55. Saphonna V. et. al., 2002. How well do antenatal clinic (ANC) attendees represent the general population? A comparison of HIV prevalence from ANC sentinel surveillance sites with a population-based survey of women aged 15–49 in Cambodia. Int. J. Epi- demiol. (2002) 31 (2): 449-455. doi: 10.1093/ije/31.2.449.
56. Sharda R. Neural networks for the MS/OR analyst: An application bibliography. Inter- faces,
57. Shintani, Mototsugi, Linton, Oliver. Nonparametric neural network estimation of Lya- punov exponents and a direct test for chaos. Econometrics.2003.
58. Sibanda, W., & Pretorius, P. (2011). Application of Two-level Fractional Factorial Design to Determine and Optimize the Effect of Demographic Characteristics on HIV Prevalence using the 2006 South African Annual Antenatal HIV and Syphilis Seroprevalence data. International Journal of Computer Applications (0975-8887) Volume, 35.
59. Sibanda, W., & Pretorius, P. (2011). Novel Application of Multi-Layer Perceptrons (MLP) Neural Networks to Model HIV in South Africa using Seroprevalence Data from Antenatal Clinics‖. International Journal of Computer Applications (0975–
8887), 35(5).
60. Sibanda, W., & Pretorius, P. (2012). Comparative Study of the Application of Box Behnken Design (BBD) and Binary Logistic Regression (BLR) to Study the Effect of Demographic Characteristics on HIV Risk in South Africa. Journal of Applied Med- ical Sciences, 1(2), 15-40.
132 61. Sibanda, W., & Pretorius, P. (2012, June). Response Surface Modeling and Optimi-
zation to Elucidate the Differential Effects of Demographic Characteristics on HIV Prevalence in South Africa. In Computers and Industrial Engineering 42.
62. Sibanda, W., & Pretorius, P. (2013). Comparative study of the application of cen- tral composite face-centred (CCF) and Box–Behnken designs (BBD) to study the effect of demographic characteristics on HIV risk in South Africa. Network Model- ing Analysis in Health Informatics and Bioinformatics, 1-10.
63. Trentin E. and Freno A. 2009. Unsupervised nonparametric density estimation: A neural network approach. Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA. (Jun. 2009), 14-19.
64. Trinca L.A. and Gilmour S.G. (1999). Dispersion Variance Dispersion Graphs for Com- paring Response Surface Designs with Applications in Food Technology. Appl. Stat- ist. Volume 48, pp. 441-455.
65. UNAIDS. Quarterly up-date on HIV epidemiology: Methods for estimating HIV inci- dence.
66. University Press. Pp. 511.
67. W. Sibanda, P. Pretorius and A. Grobler, Response Surface Modeling and Optimization to Elucidate the Differential Effects of Demographic Characteristics on HIV Preva- lence in South Africa. IEEE/ACM Interantional Conference on Advances in Social Net- works Analysis and Mining, (2011).
68. Wang S. The unpredictability of standard back-propagation neural networks in classifi- cation applications. Manage.Sci.41(3):555-559,1995.
69. Wasserstein-Robbins. A mathematical model of HIV infection: Simulating T4, T8, mac- rophages, antibody, and virus via specific anti-HIV response in the presence of adapta- tion and tropism. Bull.Math.Bioi.2010; 72(5):1208-53.
70. Wilbert Sibanda and Philip Pretorius (2012). Article: Artificial Neural Networks- A Review of Applications of Neural Networks in the Modeling of HIV Epidemic. In- ternational Journal of Computer Applications 44(16):1-4.
71. Workowski K.A. and Berman SM. CDC sexually transmitted diseases treatment guide- lines.
72. Younes B., Fotheringham A. and El-Dessouky H.M. 2010. Factorial optimization of the effects of extrusion temperature profile and polymer grade on as-spun aliphatic–
aromatic copolyester fibers. I. Birefringence and overall orientation, Journal of Applied Polymer Science, vol. 118, No. 3, pp. 1270-1277.
