JACIII Vol.18 No.3 pp. 347-352
doi: 10.20965/jaciii.2014.p0347


Integration of Results from Recognition Algorithms Applied to the Uranium Deposits

Ravil I. Muhamediyev*1,*2, Yedilkhan Amirgaliyev*2,
Syrymbet Kh. Iskakov*1, Yan I. Kuchin*3,
and Elena Muhamediyeva*4

*1International IT University, 34A/8A, Manas str./Zhandosov str., Almaty 050040, Kazakhstan

*2Institute of Problems of Information and Control, Ministry of Education and Science of the Republic of Kazakhstan, 125 Pushkina str., Almaty 050010, Kazakhstan

*3LLC Geotechnoservice, Kazatomprom, 156-156a, Bogenbay-batyr str., Almaty 050020, Kazakhstan

*4Riga Technical University, 1 Kaļķu str., Riga LV-1658, Latvia

October 14, 2013
January 31, 2014
May 20, 2014
machine learning, artificial neural network, k-NN algorithm, uranium deposit, post-processing stage

Data interpretation of electric logging can be performed using self-learning systems such as artificial neural networks (ANNs). Preliminary research shows that by using ANN we can achieve 52-73% of coincidence of interpretable data and experimental results. Therefore, it is necessary to analyze the possibility of using other classification algorithms, and that of using several classification algorithms simultaneously through a unified system (referred to as an integrator. These algorithms may improve the quality of recognition of individual species. The problem of developing a recognition system that combines several classification algorithms, also known as the integrator, is formulated here. A simple algorithm is developed for the learning and recognition of an integrator for the post processing stage; this enhances the recognition accuracy by 1-3%.

Cite this article as:
R. Muhamediyev, Y. Amirgaliyev, <. Iskakov, Y. Kuchin, and <. Muhamediyeva, “Integration of Results from Recognition Algorithms Applied to the Uranium Deposits,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.3, pp. 347-352, 2014.
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Last updated on Nov. 16, 2018