Abstract
The theoretical and mathematical substantiation of standard and selective neural network technologies is given. Layouts have been developed for visual modeling of processes in neural networks of standard McCulloch-Pitts-based neurons and selective ones based on selective neurons. The neuro-educational system allows for the effective training of neurotechnologies of senior schoolchildren, students, specialists of related professions.
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References
Aleksandrov, Y., Anokhin, K.V., Sokolov, E.N., Grechenko, T.N.: Neuron. Signal Processing. Plastic. Modeling. Fundamental Guide, p. 548. Publishing house of Tyumen State University, Tyumen (2008)
Alyautdinov, M.A., Galushkin, A.I., Kazantsev, P.A., Ostapenko, G.P.: Neurocomputers: From Software to Hardware Implementation, p. 152. Hotline – Telecom (2008)
Galushkin, A.I.: Neural Networks. Fundamentals of the Theory. Hotline - Telecom (2010)
Grechenko, T.N.: Psychophysiology, p. 572. Gardariki (2009)
Yezhov, A.A., Shumsky, S.A.: Neurocomputing and its Applications in Economics and Business. MEPI (1998)
Mayer, R.V.: Practical Electronics: From a Transistor to a Cybernetic System [Electronic Resource] - Eyes: GGPI (2010)
Khaikin, S.: Neural Networks: A Full Course, 2nd edn. Williams (2006)
Mazurov, M.E.: Neuron Modeling the Properties of a Real Neuron. The Patent for the Invention № 2597495 11.07.2014
Mazurov, M.E.: Single-Layer Perceptron Based on Selective Neurons. The Patent for the Invention № 2597497 01.13.2015
Mazurov, M.E.: Intelligent recognition of electrocardiograms using selective neuron networks and deep learning. In: International Conference of Artificial Intelligence, Medical Engineering, Education, Moscow, Russia, pp. 182–198 (2017)
Mazurov, M.E.: Synchronization of relaxation self-oscillatory systems, synchronization in neural networks. Izv. RAS. Ser. Phys. 82(1), 83–87 (2018)
Mazurov, M.E.: Nonlinear dynamics, almost periodic summation, auto-oscillatory processes, information coding in selective impulse neural networks. Izv. RAS. Phys. Ser. 82(11), 1564–1570 (2018)
Mazurov, M.E.: Modeling of the neural network technologies. In: International Conference of Artificial Intelligence, Medical Engineering, Education, Moscow, Russia, pp. 365–376 (2018)
Iyanda, A.R., Ninan, O.D., Ajayi, A.O., Anyabolu, O.G.: Predicting student academic performance in computer science courses: a comparison of neural network models. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 10(6), 1–9 (2018). https://doi.org/10.5815/ijmecs.2018.06.01
Alkhathlan, A.A., Al-Daraiseh, A.A.: An analytical study of the use of social networks for collaborative learning in higher education. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 9(2), 1–13 (2017). https://doi.org/10.5815/ijmecs.2017.02.01
Moshref, M., Al-Sayyad, R.: Developing ontology approach using software tool to improve data visualization (case study: computer network). Int. J. Mod. Educ. Comput. Sci. (IJMECS) 11(4), 32–39 (2019). https://doi.org/10.5815/ijmecs.2019.04.04
Cheah, C.S., Leong, L.: Investigating the redundancy effect in the learning of C++ computer programming using screencasting. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 11(6), 19–25 (2019). https://doi.org/10.5815/ijmecs.2019.06.03
Nurafifah, M.S., Abdul-Rahman, S., Mutalib, S., Hamid, N.H.A., Ab Malik, A.M.: Review on predicting students’ graduation time using machine learning algorithms. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 11(7), 1–13 (2019)
Alvarez-Dionisi, L.E., Mittra, M., Balza, R.: Teaching artificial intelligence and robotics to undergraduate systems engineering students. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 11(7), 54–63 (2019). https://doi.org/10.5815/ijmecs.2019.07.06
Adekunle, S.E., Adewale, O.S., Boyinbode, O.K.: Appraisal on perceived multimedia technologies as modern pedagogical tools for strategic improvement on teaching and learning. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 11(8), 15–26 (2019). https://doi.org/10.5815/ijmecs.2019.08.02
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Mazurov, M., Egisapetov, E., Markovsky, S. (2020). Neuro-Educational System for Training Standard and Selective Neural Network Technology. In: Hu, Z., Petoukhov, S., He, M. (eds) Advances in Artificial Systems for Medicine and Education III. AIMEE 2019. Advances in Intelligent Systems and Computing, vol 1126. Springer, Cham. https://doi.org/10.1007/978-3-030-39162-1_39
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