Abstract
Models used for creating intelligent systems based on artificial non-chromic networks indicate to the teachers which educational as well as teaching activities should be corrected. Activities that require to be corrected are performed at established distance learning systems and thus can be lectures, assignments, tests, grading, competitions, directed leisure activities, and case studies. Results regarding data processing in artificial neural networks specifically indicate a specific activity that needs to be maintained, promoted, or changed in order to improve students’ abilities and achievements. The developed models are also very useful to students who can understand their achievements much better as well as to develop their skills for future competencies. These models indicate that students’ abilities are far more developed in those who use some of the mentioned distance learning systems in comparison with the students who learn due to the traditional classes system.
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References
R. Sharda, Neural networks for the MS/OR analyst: An application bibliography. Interfaces 24(2), 116–130 (1994)
H. White, Learning in artificial neural networks: A statistical perspective. Neural Comput. 1, 425–464 (1989)
K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989)
P. McClure, N. Kriegeskorte, Representational distance learning for deep neural networks. Front. Comput. Neurosci. 10, 131 (2016)
T. Saito, Y. Watanobe, Learning path recommendation system for programming education based on neural networks. Int. J. Distance Educ. Technol. (IJDET) 18(1), 36–64 (2020)
M.R. Syed, Methods and applications for advancing distance education technologies: International issues and solutions. Simulation, 348 (2020)
A.A. Heidari, H. Faris, S. Mirjalili, I. Aljarah, M. Mafarja, Ant lion optimizer: Theory, literature review, and application in multi-layer perceptron neural networks, in Nature-Inspired Optimizers, (Springer, Cham, 2020), pp. 23–46
S. Milinković, M. Maksimović, Case study: Using decision tree classifier for analyzing students’ activities. J. Inform. Technol. Appl., Aprerion, Banja Luka, Republic of Srpska, BiH 3(2), 87–95 (2013)
O. Ghorbanzadeh, T. Blaschke, K. Gholamnia, S.R. Meena, D. Tiede, J. Aryal, Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens. 11(2), 196 (2019)
C.R. Gil, H. Calvo, H. Sossa, Learning an efficient gait cycle of a biped robot based on reinforcement learning and artificial neural networks. Appl. Sci. 9(3), 502 (2019)
H. Nguyen, X.N. Bui, Predicting blast-induced air overpressure: A robust artificial intelligence system based on artificial neural networks and random forest. Nat. Resour. Res. 28(3), 893–907 (2019)
S. Liu, C.W. Oosterlee, S.M. Bohte, Pricing options and computing implied volatilities using neural networks. Risks 7(1), 16 (2019)
B.G. Weinstein, S. Marconi, S. Bohlman, A. Zare, E. White, Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks. Remote Sens. 11(11), 1309 (2019)
I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufman, Burlington, 2005)
C. Mair, G. Kadoda, M. Le, K. Phalp, C. Scho, M. Shepperd, S. Webster, An investigation of machine learning based prediction systems. J. Syst. Softw. 53, 23–29 (2000)
S.B. Kotsiantis, Supervised machine learning: A review of classification techniques. Informatica 31, 249–268 (2007)
G.A. Carpenter, S. Grossberg, J.H. Reynolds, ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Netw. 4, 565–588 (1991)
C.M. Bishop, Pattern recognition and machine learning, vol 4 (Springer, Berlin, 2006)
S.J. Raudys, A.K. Jain, Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Mach. Intell. 13, 252–264 (1991)
Марковић,Љ., 2009. Примена backpropagation алгоритма за обучавање неуронских мрежа, Симпозијум о операционим истраживањима, SYM-OP-IS 2009 Београд, (pp. 1–4), ISBN 978-86-80593-43-2
М. Зекић-Сушац, А. Фрајман-Јакшић, H. иДрвенкар, Неуронске мреже и стабла одлучивања за предвиђање успјешности студирања. Економски вјесник XXII, 314–327 (2009)
I. Isaković, CRM performances accented with the implementation of data warehousing and data mining technologies. J Inform Technol Appl, Aprerion, Banja Luka, Republic of Srpska, BiH 3(2), 107–112 (2013)
S. Tomić, D. Drljača, DDLM - quality standard for electronic education programs in higher education of Bosnia and Herzegovina. JITA – J. Inform. Technol. Appl. 9(2), 67–79 (2019)
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Vasiljević, D., Vasiljević, J., Ribarić, B. (2021). Artificial Neural Networks in Creating Intelligent Distance Learning Systems. In: Bauk, S., Ilčev, S.D. (eds) The 1st International Conference on Maritime Education and Development. Springer, Cham. https://doi.org/10.1007/978-3-030-64088-0_18
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