REFERENCES
[1] Hassanien, Aboul Ella, Tai-Hoon Kim, JanuszKacprzyk, and Ali Ismail Awad, eds. Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations, Springer Berlin Heidelberg, 2014.
[2] Luger, G. F., Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5/e. Pearson Education India, 1993.
[3] Chaffey, Dave. E-business and E-commerce Management: Strategy, Implementation and Practice, Pearson Education, 2007.
[4] O'Brien, James A., and George Marakas, Introduction to information systems. McGraw-Hill, Inc., 2005.
[5] Petrovic, Otto, ChristianKittl and Ryan Dain Teksten, Developing business models for ebusiness, 2001.
[6] Rainer, R. Kelly, Casey G. Cegielski, Ingrid Splettstoesser-Hogeterp, and Cristobal Sanchez-Rodriguez. Introduction to information systems, John Wiley & Sons, 2013.
[7] Turban, Efraim, David King, Jae Kyu Lee, Ting-Peng Liang, and Deborrah C. Turban. Electronic commerce: A managerial and social networks perspective. Springer, 2015.
[8] Thuraisingham, Bhavani., "Data mining and cyber security." In Quality Software, 2003. Proceedings. Third International Conference on IEEE, 2003, pp. 2.
[9] Li, Pengyu. "Research on the quantitative methods of information security risk assessment based on ahp." The degree of Master of Management Science and Engineering in the Graduated School of Jiangxi University Finance & economics 2012,pp.2-4.
[10] Wu, Ching-Tung, Kwang-Ting Cheng, Qiang Zhu, and Yi-Leh Wu., Using visual features for anti-spam filtering, In Image Processing ICIP IEEE International Conference on, 2005,Vol. 3, pp. III-509.
[11] Fumera, Giorgio,IgnazioPillai, and Fabio Roli, Spam filtering based on the analysis of text information embedded into images, Journal of Machine Learning Research 7, no. Dec (2006), pp.2699-2720.
[12] Hailing Huang, WeiqiangGuo, and Yu Zhang, "A Novel Method for Image Spam Filtering", IEEE, Ninth International Conference for Young Computer Scientists, 2008.
[13] O. Cordón, F. Herrera, and P. Villar, Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base, IEEE Transactions on Fuzzy Systems, Vol. 9, No. 4, 2001.
[14] F. Herrera, M. Lozano and J. L. Verdegay, "Fuzzy connectives based crossover operators to model genetic algorithms population diversity," Fuzzy Sets and Systems, Vol. 92, No. 1, 1997, pp. 21-30.
[15] T. P. Hong, C. H. Chen, Y. L. Wu and Y. C. Lee, Using Divide-and-Conquer GA Strategy in Fuzzy Data Mining, The Ninth IEEE Symposium on Computers and Communications, Vol.1, 2004, pp. 116-121.
[16] R. H. Hou, T. P. Hong, S. S. Tseng and S. Y. Kuo, A new probabilistic induction method , Journal of Automatic Reasoning, Vol. 18, 1997, pp. 5-24.
[17] T. P. Hong, C. S. Kuo and S. C. Chi, "Mining association rules from quantitative data", Intelligent Data Analysis, Vol. 3, No. 5,1999, pp. 363-376.
[18] J.Cardoso and A. Sheth, Adaptation and Workflow Management Systems. (In Proceedings of International Conference WWW/Internet, Portugal, 2005.
[19] V. Vapnik. Three remarks on support vector function estimation, Advanced in Kernel methods, Support Vector Learning. The MIT Press, Cambridge, Massachusetts, 1999.
[20] M. Stitson, A. Gammerman, V. Vapnik, V.Vovk, C. Watkins and J. Weston, Support vector regression with ANOVA decomposition kernels, Advanced in Kernel methods: Support Vector Learning, The MIT Press, Cambridge, Massachusetts, 1999.
[21] J. Weston, A. Gammerman, M. Stitson, V. Vapnik, V. Vovk and C. Watkins., Support vector density estimation, advanced in Kernel methods: Support Vector Learning. The MIT Press, Cambridge, Massachusetts, 1999.
[22] V. Vapnik. An overview of statistical learning theory. IEEE transactions on Neural Networks, Vol.10, No.5, 1999, pp. 988-1000.
[23] Chen yisong, Wang guoping, Dong shihai, A Progressive Transductive Inference Algorithm Based on Support Vector Machine [J]. Journal of Software, Vo14, No.3, 2003,pp. 451-46
[24] H Drucker, D. Wu, and V. Vapnik.,Support vector machines for spam categorization, IEEE transactions on Neural NetworksVol10, No.5, 1999,pp.1048-1055.
[25] M. Boddy, B. Horling, J. Phelps, R. P. Goldman, R. Vincent, A. C. Long, B. Kohout, and R. Maheswaran., C-TAEMS Language Specification, Version 2.02. DARPA, Arlington, VA, 2006.
[26] S. Brueckner. Return from the Ant: Synthetic Ecosystems for Manufacturing Control. Dr.rer.nat, Thesis at Humboldt University Berlin, Department of Computer Science, 2000.
[27] W. Chen and K. Decker. The Analysis of Coordination in an Information System Application - Emergency Medical Services. In P. Bresciani, P. Giorgini, B. Henderson-Sellers, and M. Winiko, Editors, Agent-Oriented Information Systems, LNAI, pages. Springer, New York, NY, 2005,Vol.35, No.8,pp.36-51
[28] Neumann, Frank; Witt, Carsten (2010). Bioinspired computation in combinatorial optimization. Algorithms and their computational complexity. Natural Computing Series. Berlin: Springer-Verlag. ISBN 978-3-642-16543-6. Zbl 1223.68002
[29] Brabazon, Anthony; O’Neill, Michael (2006). Biologically inspired algorithms for financial modelling. Natural Computing Series. Berlin: Springer-Verlag. ISBN 3-540-26252-0. Zbl 1117.91030
[30] Tatar, N., & Holban, S. (2012). A bio inspired alternative to Huffman Coding. In Proceedings on Development and Application Systems (Vol. 37, pp. 179–182).
[31] C-M. Pintea, 2014, Advances in Bio-inspired Computing for Combinatorial Optimization Problem, Springer ISBN 978-3-642-40178-7
[32] Baguda, Y. S., Fisal, N., Rashid, R. A., Yusof, S. K., Syed, S. H., & Shuaibu,D. S. (2012). Biologically-inspired optimal video streaming over unpredictable wireless channel.International Journal of Future Generation Communication and Networking, 5(1), 15–28.
[33] Klambauer, G¨unter, Unterthiner, Thomas, Mayr, Andreas, and Hochreiter, Sepp. Self-Normalizing Neural Networks. arXiv preprint arXiv:1706.02515, 2017.
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