A Performance Appraisal of Neural Networks Developed for Response Prediction across Heterogeneous Domains
Deciding the numerous parameters involved in
designing a competent artificial neural network is a complicated task.
The existence of several options for selecting an appropriate
architecture for neural network adds to this complexity, especially
when different applications of heterogeneous natures are concerned.
Two completely different applications in engineering and medical
science were selected in the present study including prediction of
workpiece's surface roughness in ultrasonic-vibration assisted turning
and papilloma viruses oncogenicity. Several neural network
architectures with different parameters were developed for each
application and the results were compared. It was illustrated in this
paper that some applications such as the first one mentioned above
are apt to be modeled by a single network with sufficient accuracy,
whereas others such as the second application can be best modeled
by different expert networks for different ranges of output.
Development of knowledge about the essentials of neural networks
for different applications is regarded as the cornerstone of
multidisciplinary network design programs to be developed as a
means of reducing inconsistencies and the burden of the user
intervention.
[1] J. M. Fines, and A. Aghah, "Machine tool positioning error
compensation using artificial neural networks," Engineering
Applications of Artificial Intelligence, vol. 21, pp. 1013-1026, 2008.
[2] S. Alam, S. C. Kaushik, and S. N. Garg, "Assesment of diffuse solar
energy under general sky condition using artificial neural network,"
Applied Energy, vol. 86, pp. 554-564, 2009.
[3] Z. Yuhong, and H. Wenxin, "Application of artificial neural network to
predict the friction factor of open channel flow," Commun Nonlinear Sci
Simulat vol. 14, pp 2373-2378, 2009.
[4] H. Soleimanimehr, M. J. Nategh, and S. Amini, "Prediction of
machining force and surface roughness in ultrasonic vibration-assisted
turning using neural networks," in Proc. The Int. Conf. Advances in
Materials & processing technologies, Manama, Bahrain, 2-5 Nov. 2008.
[5] A. R. Yildiz, "A novel hybrid immune algorithm for global optimization
in design and manufacturing," Robotics and Computer-Integrated
Manufacturing, vol. 25, pp. 261-270, 2009.
[6] A. L. Huyet, "Optimization and analysis aid via data-mining for
simulated production systems," European J. Operational Research, vol.
173, pp. 827-838, 2006.
[7] C. Arbib, and F. Marinelli, "Integrating process optimization and
inventory planning in cutting-stock with skiving option: An optimization
model and its application," European J. Operational Research, vol. 163,
pp. 617-630, 2005.
[8] M. Chambers, and C. A. Mount-Campbell, "Process optimization via
neural network metamodeling," Int. J. Production Economics, vol. 79,
pp. 93-100, 2002.
[9] Chi-Sheng Tsai, "Evaluation and optimisation of integrated
manufacturing system operations using Taguch-s experiment design in
computer simulation," Computers & Industrial Engineering, vol. 43, pp.
591-604, 2002.
[10] L. R. Khan, and R. A. Sarker, "An optimal batch size for a JIT
manufacturing system," Computers & Industrial Engineering, vol. 42,
pp. 127-136, 2002.
[11] J. A. Hernández, "Optimum operating conditions for heat and mass
transfer in foodstuffs drying by means of neural network inverse," Food
Control, vol. 20, pp. 435-438, 209.
[12] F. Marini, "The artificial neural networks in foodstuff analysis: Trends
and perspectives. A review, Analytica Chimica Acta, DOI:
10.1016/j.aca.2009.01.009.
[13] A. M. Viale, J. W. Kolari, and D. R. Fraser, "Common risk factors in
bank stocks,"J. Banking & Finance, vol. 33, pp. 464-472, 2009.
[14] Chun-Lang Chang, and Chih-Hao Chen, "Applying decision tree and
neural network to increase quality of dermatologic diagnosis, " Expert
Systems with Applications, vol. 36, pp. 4035-4041, 2009.
[15] I. Babaoglu, O. Kaan Baykan, N. Aygul, K. Ozdemir, and M. Bayrak,
"Assessment of exercise stress testing with artificial neural network in
determining coronary artery disease and predicting lesion localization,"
Expert Systems with Applications, vol. 36, pp. 2562-2566, 2009.
[16] P. Gerlee, and A. R. A. Anderson, "Modelling evolutionary cell behavior
using neural networks: Application to tumour growth," BioSystems, vol.
95, pp. 166-174, 2009.
[17]R. Das, I. Turkoglu, and A. Sengur, "Diagnosis of valvular heart disease
through neural networks ensembles," Computer Methods and Programs
in Biomedicine, vol. 93, pp. 185-191, 2009.
[18] F.X. Bosch, M. M. Manos, N. Munoz, M. Sherman, A. M. Jansen, J.
Peto, M. H. Schiffma, V. Moreno, R. Kurman, and K. V. Shah,
"Prevalence of human papillomavirus in cervical cancer: a worldwide
perspective," J Natl Cancer Inst , vol.87, pp. 796-802, 1995.
[19] S. Lu, J. Fang, A. Guo, and Y. Peng, "Impact of network topology on
decision-making," Neural Networks, vol. 22, pp. 30-40, 2009.
[20] D. Gil, M. Johnsson, J. M. G. Chamizo, A. S. Paya, and D. R.
Fernandez, "Application of artificial neural networks in the diagnosis of
urological dysfunctions," Expert Systems with Applications, vol. 36, pp.
5754-5760, 2009.
[21] K. Friston, "Learning and inference in the brain," Neural Networks, vol.
16, pp. 1325-1352, 2003.
[22] J. Lampinen, and A. Vehtari, "Bayesian approach for neural networksreview
and case studies, Neural Networks, vol. 14, pp. 257-274, 2001.
[23] B. Horwitz, K. J. Friston, and J. G. Taylor, "Neural modeling and
functional brain imaging: an overview, Neural Networks, vol. 13, pp.
829-846, 2000.
[24] A.R. McIntosh, "Towards a network theory of cognition," Neural
Networks, vol. 13, pp. 861-870, 2000.
[25] J. G. Taylor, "Towards the networks of the brain: from brain imaging to
consciousness," Neural Networks, vol. 12., pp. 943-959, 1999.
[26] M. Lehtokangas, "Modelling with constructive backpropagation,"
Neural Networks, vol. 12., pp. 707-716, 1999.
[27] K. Funahashi, "Multilayer neural networks and Bayes decision theory,"
Neural Networks, vol. 11., pp. 209-213, 1998.
[28] J. E.Dayhoff,, Neural Network Architectures. New York, Van Nostrand
1990.
[29] S. Amini, H. Soleimanimehr, M.J. Nategh, A. Abudollahb, and M. H.
Sadeghi, "FEM analysis of ultrasonic-vibration-assisted turning and the
vibratory tool," journal of materials processing technology, vol. 2 0 1,
pp. 43-47, 2008.
[30] K. M├╝nger, A. Baldwin, K. M. Edwards, H. Hayakawa, C. L. Nguyen ,
M. Owens, et al., "Mechanisms of human papillomavirus-induced
oncogenesis," J Virol , vol. 78, pp. 11451-11460, 2004.
[31] R. E. Streeck, "A short introduction to papillomavirus biology,"
Intervirology, vol. 45, pp. 287-289, 2002.
[32] K. Kawana, H. Yoshikawa H, Y. Taketani, K., Yoshiike, and T. Kanda,
"Common neutralization epitope in minor capsid protein L2 of human
papillomavirus types 16 and 6," J Virol, vol. 73, pp. 6188-6190, 1999.
[33] K. K. Thomas, J. P. Hughes, J. M. Kuypers, N. B. Kiviat, S. K. Lee, D.
E. Adam, and L. A. Koutsky, "Concurrent and sequential acquisition of
different genital human papillomavirus types," J Infectious Disease, vol.
182, pp. 1097-1102, 2000.
[34] K. Sotlar, D. Diemer, A. Dethleffs, Y. Hack, A. Stubner, N. Vollmer, et
al., "Detection and typing of human papillomavirus by E6 nested
multiplex PCR," J Clin Microbiol, vol. 42, pp. 3176-3184, 2004.
[35] D. K. Heck, C. L. Yee, P. M. Howley, and K. M├╝nger, " Efficiency of
binding to the retinoblastoma protein correlates with the transforming
capacity of the E7 oncoproteins of the human papillomaviruses," Proc
Natl Acad Sci, pp. 4442-4446, 1992.
[36] J. Doorbar, "The papillomavirus life cycle," J clinical virology, vol. 32,
pp. 7-15, 2005.
[37] L. A. Koutsky, K. A. Ault, C. M. Wheeler, D. R. Brown, E. Barr, F. B.
Alvarez, et al, "A controlled trial of human Papillomavirus type 16
vaccine," N Engl j Med, vol. 347, pp. 1645-1651, 2002.
[38] L. L. Villa, K. Ault, A. R. Giuliano, R. L. Costa, C. A. Petta, R. P.
Andrade, et al., "Immunologic responses following administration of a
vaccine targeting human papillomavirus types 6, 11, 16, and 18,"
Vaccine , vol. 24, pp. 5571-5583.
[39] N. Mu├▒oz N, F. X. Bosch, S. de Sanjosé , R. Herrero, X. Castellsagué ,
K. V. Shah KV, et al., "Epidemiologic classification of human
papillomavirus types associated with cervical cancer," N. Engl. J. Med.,
vol. 48, pp. 518-27, 2003.
[40] E. J. Davidson, C. M. Boswell, P. Sehr, M. Pawlita, A. E. Tomlinson , R.
J. McVey, et al., "Immunological and Clinical Responses in Women
with Vulval Intraepithelial Neoplasia Vaccinated with a Vaccinia Virus
Encoding Human Papillomavirus 16/18 Oncoproteins," Cancer
Research, vol. 63, pp. 6032-6041, 2003.
[41] Z. Meshkat, H. Soleimanjahi, Z. M. Hassan, H. Mirshahabi, M.
Meshkat, and M. Kheirandish, "CTL Responses to a DNA Vaccine
Encoding E7 Gene of Human Papillomavirus Type 16 from an Iranian
Isolate," Iran J Immunol, vol. 5, pp. 82-91, 2008.
[42] 29-A. Teimoori, H. Soleimanjahi, F. Fotouhi, and Z. Meshkat,
"Isolation and cloning of human papillomavirus 16 L1 gene from
Iranian isolate," Saudi Med J., vol. 29, pp. 1105-1108, 2008 .
[43] http://www.ddbj.nig.ac.jp
[44] http://www.expasy.ch/sprot
[1] J. M. Fines, and A. Aghah, "Machine tool positioning error
compensation using artificial neural networks," Engineering
Applications of Artificial Intelligence, vol. 21, pp. 1013-1026, 2008.
[2] S. Alam, S. C. Kaushik, and S. N. Garg, "Assesment of diffuse solar
energy under general sky condition using artificial neural network,"
Applied Energy, vol. 86, pp. 554-564, 2009.
[3] Z. Yuhong, and H. Wenxin, "Application of artificial neural network to
predict the friction factor of open channel flow," Commun Nonlinear Sci
Simulat vol. 14, pp 2373-2378, 2009.
[4] H. Soleimanimehr, M. J. Nategh, and S. Amini, "Prediction of
machining force and surface roughness in ultrasonic vibration-assisted
turning using neural networks," in Proc. The Int. Conf. Advances in
Materials & processing technologies, Manama, Bahrain, 2-5 Nov. 2008.
[5] A. R. Yildiz, "A novel hybrid immune algorithm for global optimization
in design and manufacturing," Robotics and Computer-Integrated
Manufacturing, vol. 25, pp. 261-270, 2009.
[6] A. L. Huyet, "Optimization and analysis aid via data-mining for
simulated production systems," European J. Operational Research, vol.
173, pp. 827-838, 2006.
[7] C. Arbib, and F. Marinelli, "Integrating process optimization and
inventory planning in cutting-stock with skiving option: An optimization
model and its application," European J. Operational Research, vol. 163,
pp. 617-630, 2005.
[8] M. Chambers, and C. A. Mount-Campbell, "Process optimization via
neural network metamodeling," Int. J. Production Economics, vol. 79,
pp. 93-100, 2002.
[9] Chi-Sheng Tsai, "Evaluation and optimisation of integrated
manufacturing system operations using Taguch-s experiment design in
computer simulation," Computers & Industrial Engineering, vol. 43, pp.
591-604, 2002.
[10] L. R. Khan, and R. A. Sarker, "An optimal batch size for a JIT
manufacturing system," Computers & Industrial Engineering, vol. 42,
pp. 127-136, 2002.
[11] J. A. Hernández, "Optimum operating conditions for heat and mass
transfer in foodstuffs drying by means of neural network inverse," Food
Control, vol. 20, pp. 435-438, 209.
[12] F. Marini, "The artificial neural networks in foodstuff analysis: Trends
and perspectives. A review, Analytica Chimica Acta, DOI:
10.1016/j.aca.2009.01.009.
[13] A. M. Viale, J. W. Kolari, and D. R. Fraser, "Common risk factors in
bank stocks,"J. Banking & Finance, vol. 33, pp. 464-472, 2009.
[14] Chun-Lang Chang, and Chih-Hao Chen, "Applying decision tree and
neural network to increase quality of dermatologic diagnosis, " Expert
Systems with Applications, vol. 36, pp. 4035-4041, 2009.
[15] I. Babaoglu, O. Kaan Baykan, N. Aygul, K. Ozdemir, and M. Bayrak,
"Assessment of exercise stress testing with artificial neural network in
determining coronary artery disease and predicting lesion localization,"
Expert Systems with Applications, vol. 36, pp. 2562-2566, 2009.
[16] P. Gerlee, and A. R. A. Anderson, "Modelling evolutionary cell behavior
using neural networks: Application to tumour growth," BioSystems, vol.
95, pp. 166-174, 2009.
[17]R. Das, I. Turkoglu, and A. Sengur, "Diagnosis of valvular heart disease
through neural networks ensembles," Computer Methods and Programs
in Biomedicine, vol. 93, pp. 185-191, 2009.
[18] F.X. Bosch, M. M. Manos, N. Munoz, M. Sherman, A. M. Jansen, J.
Peto, M. H. Schiffma, V. Moreno, R. Kurman, and K. V. Shah,
"Prevalence of human papillomavirus in cervical cancer: a worldwide
perspective," J Natl Cancer Inst , vol.87, pp. 796-802, 1995.
[19] S. Lu, J. Fang, A. Guo, and Y. Peng, "Impact of network topology on
decision-making," Neural Networks, vol. 22, pp. 30-40, 2009.
[20] D. Gil, M. Johnsson, J. M. G. Chamizo, A. S. Paya, and D. R.
Fernandez, "Application of artificial neural networks in the diagnosis of
urological dysfunctions," Expert Systems with Applications, vol. 36, pp.
5754-5760, 2009.
[21] K. Friston, "Learning and inference in the brain," Neural Networks, vol.
16, pp. 1325-1352, 2003.
[22] J. Lampinen, and A. Vehtari, "Bayesian approach for neural networksreview
and case studies, Neural Networks, vol. 14, pp. 257-274, 2001.
[23] B. Horwitz, K. J. Friston, and J. G. Taylor, "Neural modeling and
functional brain imaging: an overview, Neural Networks, vol. 13, pp.
829-846, 2000.
[24] A.R. McIntosh, "Towards a network theory of cognition," Neural
Networks, vol. 13, pp. 861-870, 2000.
[25] J. G. Taylor, "Towards the networks of the brain: from brain imaging to
consciousness," Neural Networks, vol. 12., pp. 943-959, 1999.
[26] M. Lehtokangas, "Modelling with constructive backpropagation,"
Neural Networks, vol. 12., pp. 707-716, 1999.
[27] K. Funahashi, "Multilayer neural networks and Bayes decision theory,"
Neural Networks, vol. 11., pp. 209-213, 1998.
[28] J. E.Dayhoff,, Neural Network Architectures. New York, Van Nostrand
1990.
[29] S. Amini, H. Soleimanimehr, M.J. Nategh, A. Abudollahb, and M. H.
Sadeghi, "FEM analysis of ultrasonic-vibration-assisted turning and the
vibratory tool," journal of materials processing technology, vol. 2 0 1,
pp. 43-47, 2008.
[30] K. M├╝nger, A. Baldwin, K. M. Edwards, H. Hayakawa, C. L. Nguyen ,
M. Owens, et al., "Mechanisms of human papillomavirus-induced
oncogenesis," J Virol , vol. 78, pp. 11451-11460, 2004.
[31] R. E. Streeck, "A short introduction to papillomavirus biology,"
Intervirology, vol. 45, pp. 287-289, 2002.
[32] K. Kawana, H. Yoshikawa H, Y. Taketani, K., Yoshiike, and T. Kanda,
"Common neutralization epitope in minor capsid protein L2 of human
papillomavirus types 16 and 6," J Virol, vol. 73, pp. 6188-6190, 1999.
[33] K. K. Thomas, J. P. Hughes, J. M. Kuypers, N. B. Kiviat, S. K. Lee, D.
E. Adam, and L. A. Koutsky, "Concurrent and sequential acquisition of
different genital human papillomavirus types," J Infectious Disease, vol.
182, pp. 1097-1102, 2000.
[34] K. Sotlar, D. Diemer, A. Dethleffs, Y. Hack, A. Stubner, N. Vollmer, et
al., "Detection and typing of human papillomavirus by E6 nested
multiplex PCR," J Clin Microbiol, vol. 42, pp. 3176-3184, 2004.
[35] D. K. Heck, C. L. Yee, P. M. Howley, and K. M├╝nger, " Efficiency of
binding to the retinoblastoma protein correlates with the transforming
capacity of the E7 oncoproteins of the human papillomaviruses," Proc
Natl Acad Sci, pp. 4442-4446, 1992.
[36] J. Doorbar, "The papillomavirus life cycle," J clinical virology, vol. 32,
pp. 7-15, 2005.
[37] L. A. Koutsky, K. A. Ault, C. M. Wheeler, D. R. Brown, E. Barr, F. B.
Alvarez, et al, "A controlled trial of human Papillomavirus type 16
vaccine," N Engl j Med, vol. 347, pp. 1645-1651, 2002.
[38] L. L. Villa, K. Ault, A. R. Giuliano, R. L. Costa, C. A. Petta, R. P.
Andrade, et al., "Immunologic responses following administration of a
vaccine targeting human papillomavirus types 6, 11, 16, and 18,"
Vaccine , vol. 24, pp. 5571-5583.
[39] N. Mu├▒oz N, F. X. Bosch, S. de Sanjosé , R. Herrero, X. Castellsagué ,
K. V. Shah KV, et al., "Epidemiologic classification of human
papillomavirus types associated with cervical cancer," N. Engl. J. Med.,
vol. 48, pp. 518-27, 2003.
[40] E. J. Davidson, C. M. Boswell, P. Sehr, M. Pawlita, A. E. Tomlinson , R.
J. McVey, et al., "Immunological and Clinical Responses in Women
with Vulval Intraepithelial Neoplasia Vaccinated with a Vaccinia Virus
Encoding Human Papillomavirus 16/18 Oncoproteins," Cancer
Research, vol. 63, pp. 6032-6041, 2003.
[41] Z. Meshkat, H. Soleimanjahi, Z. M. Hassan, H. Mirshahabi, M.
Meshkat, and M. Kheirandish, "CTL Responses to a DNA Vaccine
Encoding E7 Gene of Human Papillomavirus Type 16 from an Iranian
Isolate," Iran J Immunol, vol. 5, pp. 82-91, 2008.
[42] 29-A. Teimoori, H. Soleimanjahi, F. Fotouhi, and Z. Meshkat,
"Isolation and cloning of human papillomavirus 16 L1 gene from
Iranian isolate," Saudi Med J., vol. 29, pp. 1105-1108, 2008 .
[43] http://www.ddbj.nig.ac.jp
[44] http://www.expasy.ch/sprot
@article{"International Journal of Information, Control and Computer Sciences:58652", author = "H. Soleimanjahi and M. J. Nategh and S. Falahi", title = "A Performance Appraisal of Neural Networks Developed for Response Prediction across Heterogeneous Domains", abstract = "Deciding the numerous parameters involved in
designing a competent artificial neural network is a complicated task.
The existence of several options for selecting an appropriate
architecture for neural network adds to this complexity, especially
when different applications of heterogeneous natures are concerned.
Two completely different applications in engineering and medical
science were selected in the present study including prediction of
workpiece's surface roughness in ultrasonic-vibration assisted turning
and papilloma viruses oncogenicity. Several neural network
architectures with different parameters were developed for each
application and the results were compared. It was illustrated in this
paper that some applications such as the first one mentioned above
are apt to be modeled by a single network with sufficient accuracy,
whereas others such as the second application can be best modeled
by different expert networks for different ranges of output.
Development of knowledge about the essentials of neural networks
for different applications is regarded as the cornerstone of
multidisciplinary network design programs to be developed as a
means of reducing inconsistencies and the burden of the user
intervention.", keywords = "Artificial Neural Network, Malignancy Diagnosis,Papilloma Viruses Oncogenicity, Surface Roughness, UltrasonicVibration-Assisted Turning.", volume = "3", number = "4", pages = "1112-6", }
