پروپوزال کامپیوتر- هوش مصنوعی - 21 صفحه
((فایل pdf و غیر قابل ویرایش می باشد.))
بعد از پرداخت به راحتی همان لحظه می توانید آن را دانلود کنید.
پروپوزال کامپیوتر هوش مصنوعی- استفاده از شبکه های عصبی فازی با الهام از الگوریتم ایمنی جهت پیش بینی زایمان زودرس
Using fuzzy neural networks inspired by an immunity algorithm to predict preterm delivery
قیمت انجام پروپوزال از 250 هزار تومان تا 500 متغیر است که پروپوزال های آماده قیمت ناچیزی دارند.
پس منصف باشید و قیمت ها را با هم مقایسه کنید.
((((پروپوزال های سایت حاصل زحمت محققین سایت می باشد و اینترنتی نیست.))))
فهرست مطالب:
تعریف واژه ها و اصطلاحات فنی و تخصصی
بیان مسأله
روش بررسی
شبکه عصبی فازی
شبکه ایمنی مصنوعی
PSO
سوالات
فرضیه ها
مرور ادبیات و سوابق مربوطه
طراحی شبكه عصبی مصنوعی برای پیش بینی
جنبه جدید بودن و نوآوری در تحقیق
شرح کامل روش
جامعه آماری، روش نمونه گیری و حجم نمونه
منابع
منابع
[1] Keles A, Samet Hasiloglu A, Keles A, Aksoy Y. Neuro-fuzzy classification of prostate cancer using NEFCLASS-J. Comput Biol Med 2007;37:1617–28.
[2] Ecke TH, Schlechte HH, Schiemenz K, Sachs MD, Lenk SV, Rudolph BD, et al. TP53 gene mutations in prostate cancer progression. Anticancer Res 2010;30:1579–86.
[3] Benecchi L. Neuro-fuzzy system for prostate cancer diagnosis. Urology 2006;68:357–61.
[4] Hart E, Timmis J. Application areas of AIS: the past, the present and the future. Appl Soft Comput 2008;8:191–201.
[5] Parkin DM. Global cancer statistics in the year 2000. Lancet Oncol 2001;2:533–43.
[6] Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin 2011;61:69–90.
[7] Saritas I, Ozkan IA, Sert IU. Prognosis of prostate cancer by artificial neural networks. Expert Syst Appl 2010;37:6646–50.
[8] Catalona WJ, Smith DS, Ratliff TL, Basler JW. Detection of organ-confined prostate cancer is increased through prostate-specific antigen-based screening. JAMA 1993;270:948–54.
[9] C¸ ınar M, Engin M, Engin EZ, Ziya Ates¸ c¸ i Y. Early prostate cancer diagnosis by using artificial neural networks and support vector machines. Expert Syst Appl 2009;36:6357–61.
[10] Rumelhart D, Hinton G, Williams R. Learning internal representations by error propagation. In: Parallel distributed processing: explorations in the microstruc-ture of cognition. Cambridge, MA: Foundations MIT Press; 1986. p. 318–62.
[11] Mitra S, Hayashi Y. Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans Neural Netw 2000;11:748–68.
[12] Zadeh LA. Fuzzy sets. Inf Control 1965;8:338–53.
[13] Shibata T, Fukuda T, Kosuge K, Arai F, Tokita M, Mitsuoka T. Skill based control by using fuzzy neural network for hierarchical intelligent control. In: International joint conference on neural networks, 1992 IJCNN. Baltimore, MD: IEEE; 1992. p. 81–6.
[14] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1985:116–32.
[15] Lin C-T, Lee CSG. Neural-network-based fuzzy logic control and decision sys-tem. IEEE Trans Comput 1991;40:1320–36.
[16] Jang J-S. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 1993;23:665–85.
[17] Shoorehdeli MA, Teshnehlab M, Sedigh AK. Identification using ANFIS with intelligent hybrid stable learning algorithm approaches. Neural Comput Appl 2009;18:157–74.
[18] Kuo RJ, Cohen PH. Manufacturing process control through integration of neural networks and fuzzy model. Fuzzy Sets Syst 1998;98:15–31.
[19] Ishigami H, Fukuda T, Shibata T, Arai F. Structure optimization of fuzzy neural network by genetic algorithm. Fuzzy Sets Syst 1995;71:257–64.
[20] Buckley JJ, Hayashi Y. Can fuzzy neural nets approximate continuous fuzzy functions? Fuzzy Sets Syst 1994;61:43–51.
[21] Feng G. A survey on analysis and design of model-based fuzzy control systems. IEEE Trans Fuzzy Syst 2006;14:676–97.
[22] Jerne NK. Towards a network theory of the immune system. Ann Immunol 1974;125:373–89.
[23] de Castro LN, Timmis J. An artificial immune network for multimodal function optimization. In: Proceedings of the 2002 congress on evolutionary computa-tion, CEC’02. Honolulu, HI: IEEE; 2002. p. 699–704.
[24] Shi Y, Eberhart R. Parameter selection in particle swarm optimization. In: Porto VW, Saravanan N, Waagen D, Eiben AE, editors. Evolutionary programming VII. Berlin/Heidelberg: Springer; 1998. p. 591–600.
[25] Shimojima K, Fukuda T, Hasegawa Y. RBF-fuzzy system with GA based unsuper-vised/supervised learning method. In: Fuzzy systems, 1995. International joint conference of the fourth IEEE international conference on fuzzy systems and the second international fuzzy engineering symposium. Proceedings of 1995 IEEE international, vol. 1. Yokohama: IEEE; 1995. p. 253–8.
[26] Kumar M, Garg DP. Intelligent learning of fuzzy logic controllers via neural network and genetic algorithm. In: Proceedings of 2004 JUSFA. 2004 Japan–USA symposium on flexible automation denve. 2004. p. 1–8.
[27] Kumar S. Neural networks: a classroom approach. Singapore: Tata McGraw-Hill Education; 2005.
[28] Hsiao S-W, Tsai H-C. Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. Int J Ind Ergon 2005;35:411–28.
[29] Östermark R. A hybrid genetic fuzzy neural network algorithm designed for classification problems involving several groups. Fuzzy Sets Syst 2000;114:311–24.
[30] He Z, Wei C, Yang L, Gao X, Yao S, Eberhart RC, et al. Extracting rules from fuzzy neural network by particle swarm optimisation. In: The 1998 IEEE interna-tional conference on evolutionary computation proceedings. 1998 IEEE world congress on computational intelligence. Anchorage, AK: IEEE; 1998. p. 74–7.
[31] Lin C, Liu Y, Lee C. An efficient neural fuzzy network based on immune particle swarm optimization for prediction and control applications. Int J Innov Comput Inf Control 2008;4:1711–22. [32] Izadinia H, Sadeghi F, Ebadzadeh MM. A novel multi-epitopic immune net-work model hybridized with neural theory and fuzzy concept. Neural Netw 2009;22:633–41.
[33] Cai G-R, Chen S-L, Guo W-Z. Fuzzy neural network structure of linguistic dynamic systems based on nonlinear particle swarm optimization. In: 2008 3rd international conference on intelligent system and knowledge engineering, 2008 ISKE. Xiamen: IEEE; 2008. p. 886–91.
[34] Bao S, Lin C. Ant colony optimization control for fuzzy neural network in free-way entrance ramp. J Transp Inf Saf 2009;5:173–6.
[35] Kuo RJ, Chen SS, Cheng WC, Tsai CY. Integration of artificial immune network and K-means for cluster analysis. Knowl Inf Syst 2013:1–17.
[36] Dasgupta D. Artificial immune systems and their applications. Berlin/Heidelberg: Springer; 1999.
[37] Mackey MC, Glass L. Oscillation and chaos in physiological control systems. Science 1977;197:287–9.
[38] Guillaume S. Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Trans Fuzzy Syst 2001;9:426–43.
[39] Zhou S-M, Gan JQ. Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling. Fuzzy Sets Syst 2008;159:3091–131.
[40].Cunningham FG, Leveno KJ, Bloom SL, Hauth JC, Gilstrap LC,Wenstrom KD, editors. Williams obstetrics 22st ed. New York: Mc Graw-Hill.2005; (36):995-1025
[41]. Goldenberg RL, Culhan JF ,Lams JD, Romero R. Epidemiology and causes of preterm birth. Lancet 2008;371:75-84
[42]. Hussain A.J, P. Fergus, H. Al-Askar, D. Al-Jumeily, F. Jager. Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women. Neurocomputing 151 2015: 963–974
.[43] اکبریان،م.پایدار،خ.رستم نیاکان کلهری،ش.شیخ طاهری،ع. طراحی شبكه عصبی مصنوعی برای پیش بینی نتایج حاملگی در مادران باردار لوپوسی در ایران. مجله دانشكده پزشكی، دانشگاه علوم پزشكی تهران، تیر 1394 ، 73 ،(4)، 251 تا 259
[44]. Ren-Jieh Kuo,∗, Man-Hsin Huang, Wei-Che Cheng, Chih-Chieh Linc, , Yung-Hung Wu. Application of a two- stage fuzzy neural network to a prostate cancer prognosis system. Artificial Intelligence in Medicine2015:15.
[1] Keles A, Samet Hasiloglu A, Keles A, Aksoy Y. Neuro-fuzzy classification of prostate cancer using NEFCLASS-J. Comput Biol Med 2007;37:1617–28.
[2] Ecke TH, Schlechte HH, Schiemenz K, Sachs MD, Lenk SV, Rudolph BD, et al. TP53 gene mutations in prostate cancer progression. Anticancer Res 2010;30:1579–86.
[3] Benecchi L. Neuro-fuzzy system for prostate cancer diagnosis. Urology 2006;68:357–61.
[4] Hart E, Timmis J. Application areas of AIS: the past, the present and the future. Appl Soft Comput 2008;8:191–201.
[5] Parkin DM. Global cancer statistics in the year 2000. Lancet Oncol 2001;2:533–43.
[6] Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin 2011;61:69–90.
[7] Saritas I, Ozkan IA, Sert IU. Prognosis of prostate cancer by artificial neural networks. Expert Syst Appl 2010;37:6646–50.
[8] Catalona WJ, Smith DS, Ratliff TL, Basler JW. Detection of organ-confined prostate cancer is increased through prostate-specific antigen-based screening. JAMA 1993;270:948–54.
[9] C¸ ınar M, Engin M, Engin EZ, Ziya Ates¸ c¸ i Y. Early prostate cancer diagnosis by using artificial neural networks and support vector machines. Expert Syst Appl 2009;36:6357–61.
[10] Rumelhart D, Hinton G, Williams R. Learning internal representations by error propagation. In: Parallel distributed processing: explorations in the microstruc-ture of cognition. Cambridge, MA: Foundations MIT Press; 1986. p. 318–62.
[11] Mitra S, Hayashi Y. Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans Neural Netw 2000;11:748–68.
[12] Zadeh LA. Fuzzy sets. Inf Control 1965;8:338–53.
[13] Shibata T, Fukuda T, Kosuge K, Arai F, Tokita M, Mitsuoka T. Skill based control by using fuzzy neural network for hierarchical intelligent control. In: International joint conference on neural networks, 1992 IJCNN. Baltimore, MD: IEEE; 1992. p. 81–6.
[14] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1985:116–32.
[15] Lin C-T, Lee CSG. Neural-network-based fuzzy logic control and decision sys-tem. IEEE Trans Comput 1991;40:1320–36.
[16] Jang J-S. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 1993;23:665–85.
[17] Shoorehdeli MA, Teshnehlab M, Sedigh AK. Identification using ANFIS with intelligent hybrid stable learning algorithm approaches. Neural Comput Appl 2009;18:157–74.
[18] Kuo RJ, Cohen PH. Manufacturing process control through integration of neural networks and fuzzy model. Fuzzy Sets Syst 1998;98:15–31.
[19] Ishigami H, Fukuda T, Shibata T, Arai F. Structure optimization of fuzzy neural network by genetic algorithm. Fuzzy Sets Syst 1995;71:257–64.
[20] Buckley JJ, Hayashi Y. Can fuzzy neural nets approximate continuous fuzzy functions? Fuzzy Sets Syst 1994;61:43–51.
[21] Feng G. A survey on analysis and design of model-based fuzzy control systems. IEEE Trans Fuzzy Syst 2006;14:676–97.
[22] Jerne NK. Towards a network theory of the immune system. Ann Immunol 1974;125:373–89.
[23] de Castro LN, Timmis J. An artificial immune network for multimodal function optimization. In: Proceedings of the 2002 congress on evolutionary computa-tion, CEC’02. Honolulu, HI: IEEE; 2002. p. 699–704.
[24] Shi Y, Eberhart R. Parameter selection in particle swarm optimization. In: Porto VW, Saravanan N, Waagen D, Eiben AE, editors. Evolutionary programming VII. Berlin/Heidelberg: Springer; 1998. p. 591–600.
[25] Shimojima K, Fukuda T, Hasegawa Y. RBF-fuzzy system with GA based unsuper-vised/supervised learning method. In: Fuzzy systems, 1995. International joint conference of the fourth IEEE international conference on fuzzy systems and the second international fuzzy engineering symposium. Proceedings of 1995 IEEE international, vol. 1. Yokohama: IEEE; 1995. p. 253–8.
[26] Kumar M, Garg DP. Intelligent learning of fuzzy logic controllers via neural network and genetic algorithm. In: Proceedings of 2004 JUSFA. 2004 Japan–USA symposium on flexible automation denve. 2004. p. 1–8.
[27] Kumar S. Neural networks: a classroom approach. Singapore: Tata McGraw-Hill Education; 2005.
[28] Hsiao S-W, Tsai H-C. Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. Int J Ind Ergon 2005;35:411–28.
[29] Östermark R. A hybrid genetic fuzzy neural network algorithm designed for classification problems involving several groups. Fuzzy Sets Syst 2000;114:311–24.
[30] He Z, Wei C, Yang L, Gao X, Yao S, Eberhart RC, et al. Extracting rules from fuzzy neural network by particle swarm optimisation. In: The 1998 IEEE interna-tional conference on evolutionary computation proceedings. 1998 IEEE world congress on computational intelligence. Anchorage, AK: IEEE; 1998. p. 74–7.
[31] Lin C, Liu Y, Lee C. An efficient neural fuzzy network based on immune particle swarm optimization for prediction and control applications. Int J Innov Comput Inf Control 2008;4:1711–22. [32] Izadinia H, Sadeghi F, Ebadzadeh MM. A novel multi-epitopic immune net-work model hybridized with neural theory and fuzzy concept. Neural Netw 2009;22:633–41.
[33] Cai G-R, Chen S-L, Guo W-Z. Fuzzy neural network structure of linguistic dynamic systems based on nonlinear particle swarm optimization. In: 2008 3rd international conference on intelligent system and knowledge engineering, 2008 ISKE. Xiamen: IEEE; 2008. p. 886–91.
[34] Bao S, Lin C. Ant colony optimization control for fuzzy neural network in free-way entrance ramp. J Transp Inf Saf 2009;5:173–6.
[35] Kuo RJ, Chen SS, Cheng WC, Tsai CY. Integration of artificial immune network and K-means for cluster analysis. Knowl Inf Syst 2013:1–17.
[36] Dasgupta D. Artificial immune systems and their applications. Berlin/Heidelberg: Springer; 1999.
[37] Mackey MC, Glass L. Oscillation and chaos in physiological control systems. Science 1977;197:287–9.
[38] Guillaume S. Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Trans Fuzzy Syst 2001;9:426–43.
[39] Zhou S-M, Gan JQ. Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling. Fuzzy Sets Syst 2008;159:3091–131.
[40].Cunningham FG, Leveno KJ, Bloom SL, Hauth JC, Gilstrap LC,Wenstrom KD, editors. Williams obstetrics 22st ed. New York: Mc Graw-Hill.2005; (36):995-1025
[41]. Goldenberg RL, Culhan JF ,Lams JD, Romero R. Epidemiology and causes of preterm birth. Lancet 2008;371:75-84
[42]. Hussain A.J, P. Fergus, H. Al-Askar, D. Al-Jumeily, F. Jager. Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women. Neurocomputing 151 2015: 963–974
[43]. Ren-Jieh Kuo,∗, Man-Hsin Huang, Wei-Che Cheng, Chih-Chieh Linc, , Yung-Hung Wu. Application of a two- stage fuzzy neural network to a prostate cancer prognosis system. Artificial Intelligence in Medicine2015:15.
.[44] اکبریان،م.پایدار،خ.رستم نیاکان کلهری،ش.شیخ طاهری،ع. طراحی شبكه عصبی مصنوعی برای پیش بینی نتایج حاملگی در مادران باردار لوپوسی در ایران. مجله دانشكده پزشكی، دانشگاه علوم پزشكی تهران، تیر 1394 ، 73 ،(4)، 251 تا 259