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Porous Silica Templated Nanomaterials for Artificial Intelligence and IT Technologies

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Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10246))

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Abstract

This paper focuses on two types of novel nanomaterials based on ordered mesoporous silica designed for applications in artificial intelligence and IT technologies: molecular neural network and super dense magnetic memories. There’s no doubt that electronics needs new solutions for the further development. Nanotechnology comes here with the help. Especially nanostructured functional materials can help solve the problem of miniaturization.

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References

  1. Aghdam, M.H., Heidari, S.: Feature selection using particle swarm optimization in text categorization. J. Artif. Intell. Soft Comput. Res. 5(4), 231–238 (2015)

    Article  Google Scholar 

  2. Bas, E.: The training of multiplicative neuron model based artificial neural networks with differential evolution algorithm for forecasting. J. Artif. Intell. Soft Comput. Res. 6(1), 5–11 (2016)

    Article  Google Scholar 

  3. Bello, O., Holzmann, J., Yaqoob, T., Teodoriu, C.: Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art. J. Artif. Intell. Soft Comput. Res. 5(2), 121–139 (2015)

    Article  Google Scholar 

  4. Bertini Junior, J.R., Nicoletti, M.D.C.: Enhancing constructive neural network performance using functionally expanded input data. J. Artif. Intell. Soft Comput. Res. 6(2), 119–131 (2016)

    Article  Google Scholar 

  5. Cpalka, K.: A method for designing flexible neuro-fuzzy systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS, vol. 4029, pp. 212–219. Springer, Heidelberg (2006). doi:10.1007/11785231_23

    Chapter  Google Scholar 

  6. Cpalka, K.: Design of Interpretable Fuzzy Systems, vol. 684. Springer, Heidelberg (2017)

    Google Scholar 

  7. Cpałka, K., Zalasiński, M., Rutkowski, L.: A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. Appl. Soft Comput. 43, 47–56 (2016)

    Article  Google Scholar 

  8. Datta, S., Das, B.: Electronic analog of the electro-optic modulator. Appl. Phys. Lett. 56(7), 665–667 (1990)

    Article  Google Scholar 

  9. Drexler, K.E., Minsky, M.: Engines of Creation. Fourth Estate, London (1990)

    Google Scholar 

  10. El-Samak, A.F., Ashour, W.: Optimization of traveling salesman problem using affinity propagation clustering and genetic algorithm. J. Artif. Intell. Soft Comput. Res. 5(4), 239–245 (2015)

    Article  Google Scholar 

  11. Giraldo, L., López, B., Pérez, L., Urrego, S., Sierra, L., Mesa, M.: Mesoporous silica applications. In: Macromolecular symposia, vol. 258, pp. 129–141. Wiley Online Library (2007)

    Google Scholar 

  12. Hopfield, J.J., Feinstein, D., Palmer, R.: ‘unlearning’ has a stabilizing effect in collective memories (1983)

    Google Scholar 

  13. Hopfield, J.J., Tank, D.W., et al.: Computing with neural circuits - a model. Science 233(4764), 625–633 (1986)

    Article  MATH  Google Scholar 

  14. Hueso, L.E., Pruneda, J.M., Ferrari, V., Burnell, G., Valdés-Herrera, J.P., Simons, B.D., Littlewood, P.B., Artacho, E., Fert, A., Mathur, N.D.: Transformation of spin information into large electrical signals using carbon nanotubes. Nature 445(7126), 410–413 (2007)

    Article  Google Scholar 

  15. Lan, K., Sekiyama, K.: Autonomous viewpoint selection of robot based on aesthetic evaluation of a scene. J. Artif. Intell. Soft Comput. Res. 6(4), 255–265 (2016)

    Article  Google Scholar 

  16. Łapa, K., Cpałka, K., Wang, L.: New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 217–232. Springer, Cham (2014). doi:10.1007/978-3-319-07173-2_20

    Chapter  Google Scholar 

  17. Łapa, K., Przybył, A., Cpałka, K.: A new approach to designing interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS, vol. 7895, pp. 523–534. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38610-7_48

    Chapter  Google Scholar 

  18. Łapa, K., Szczypta, J., Venkatesan, R.: Aspects of structure and parameters selection of control systems using selected multi-population algorithms. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9120, pp. 247–260. Springer, Cham (2015). doi:10.1007/978-3-319-19369-4_23

    Chapter  Google Scholar 

  19. Leon, M., Xiong, N.: Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters. J. Artif. Intell. Soft Comput. Res. 6(2), 103–118 (2016)

    Article  Google Scholar 

  20. Lis, T.: Preparation, structure, and magnetic properties of a dodecanuclear mixed-valence manganese carboxylate. Acta Crystallogr. Sect. B Struct. Crystallogr. Crystal Chem. 36(9), 2042–2046 (1980)

    Article  Google Scholar 

  21. Nobukawa, S., Nishimura, H., Yamanishi, T., Liu, J.Q.: Chaotic states induced by resetting process in izhikevich neuron model. J. Artif. Intell. Soft Comput. Res. 5(2), 109–119 (2015)

    Article  Google Scholar 

  22. Patgiri, C., Sarma, M., Sarma, K.K.: A class of neuro-computational methods for assamese fricative classification. J. Artif. Intell. Soft Comput. Res. 5(1), 59–70 (2015)

    Article  Google Scholar 

  23. Prasad, M., Liu, Y.T., Li, D.L., Lin, C.T., Shah, R.R., Kaiwartya, O.P.: A new mechanism for data visualization with tsk-type preprocessed collaborative fuzzy rule based system. J. Artif. Intell. Soft Comput. Res. 7(1), 33–46 (2017)

    Article  Google Scholar 

  24. Rutkowski, L., Cpalka, K.: Flexible weighted neuro-fuzzy systems. In: Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP 2002, vol. 4, pp. 1857–1861. IEEE (2002)

    Google Scholar 

  25. Rutkowski, L., Cpalka, K.: Neuro-fuzzy systems derived from quasi-triangular norms. In: Proceedings of the 2004 IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1031–1036. IEEE (2004)

    Google Scholar 

  26. Sugiyama, H.: Pulsed power network based on decentralized intelligence for reliable and lowloss electrical power distribution. J. Artif. Intell. Soft Comput. Res. 5(2), 97–108 (2015)

    Article  Google Scholar 

  27. Szczypta, J., Łapa, K., Shao, Z.: Aspects of the selection of the structure and parameters of controllers using selected population based algorithms. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 440–454. Springer, Cham (2014). doi:10.1007/978-3-319-07173-2_38

    Chapter  Google Scholar 

  28. Walcarius, A., Sibottier, E., Etienne, M., Ghanbaja, J.: Electrochemically assisted self-assembly of mesoporous silica thin films. Nat. Mater. 6(8), 602–608 (2007)

    Article  Google Scholar 

  29. Zalasiński, M., Cpałka, K.: A new method of on-line signature verification using a flexible fuzzy one-class classifier. In: Selected Topics in Computer Science Applications, pp. 38–53 (2011)

    Google Scholar 

  30. Zalasiński, M.: New algorithm for on-line signature verification using characteristic global features. In: Wilimowska, Z., Borzemski, L., Grzech, A., Świątek, J. (eds.) Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part IV. AISC, vol. 432, pp. 137–146. Springer, Cham (2016). doi:10.1007/978-3-319-28567-2_12

    Google Scholar 

  31. Zalasiński, M., Cpałka, K.: New approach for the on-line signature verification based on method of horizontal partitioning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS, vol. 7895, pp. 342–350. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38610-7_32

    Chapter  Google Scholar 

  32. Zalasiński, M., Cpałka, K.: Novel algorithm for the on-line signature verification using selected discretization points groups. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS, vol. 7894, pp. 493–502. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38658-9_44

    Chapter  Google Scholar 

  33. Zalasiński, M., Cpałka, K., Er, M.J.: New method for dynamic signature verification using hybrid partitioning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8468, pp. 216–230. Springer, Cham (2014). doi:10.1007/978-3-319-07176-3_20

    Chapter  Google Scholar 

  34. Zalasiński, M., Cpałka, K., Rakus-Andersson, E.: An idea of the dynamic signature verification based on a hybrid approach. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 232–246. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_21

    Google Scholar 

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Aknowledgement

Financial support for this investigation has been provided by the National Centre of Science (Grant-No: 2015/17/N/ST5/03328).

Author information

Authors and Affiliations

  1. Institute of Nuclear Physics Polish Academy of Sciences, 31342, Krakow, Poland

    Magdalena Laskowska

  2. Department of Microelectronics and Nanotechnology, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-201, Czestochowa, Poland

    Łukasz Laskowski, Jerzy Jelonkiewicz & Tomasz Galkowski

  3. Institute of Computer Science, Czestochowa University of Technology, Ul. Dabrowskiego 69, 42-201, Czestochowa, Poland

    Henryk Piech

  4. Université Montpellier II, Chimie Moléculaire et Organisation du Solide, Institut Charles Gerhardt, UMR 5253 CC 1701, 2 Place E. Bataillon, 34095, Montpellier Cedex 5, France

    Arnaud Boullanger

Authors
  1. Magdalena Laskowska
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  2. Łukasz Laskowski
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  3. Jerzy Jelonkiewicz
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  4. Henryk Piech
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  5. Tomasz Galkowski
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  6. Arnaud Boullanger
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Corresponding author

Correspondence to Łukasz Laskowski .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Laskowska, M., Laskowski, Ł., Jelonkiewicz, J., Piech, H., Galkowski, T., Boullanger, A. (2017). Porous Silica Templated Nanomaterials for Artificial Intelligence and IT Technologies. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_46

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  • DOI: https://doi.org/10.1007/978-3-319-59060-8_46

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  • Online ISBN: 978-3-319-59060-8

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