UDC 004.032.26
1 Institute of Telecommunication and Global Information Space of the National Academy of Sciences of Ukraine, Kyiv, Ukraine
 pryjmalnya@gmail.com
|
2 National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine
maksym.zoziuk@gmail.com
|
3 National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine; Institute of Telecommunication and Global Information Space of the National Academy of Sciences of Ukraine, Kyiv, Ukraine; Laboratory of Digital Innovations of the UNESCO Interdisciplinary Chair of Biotechnology and Bioetics, University of Rome Tor Vergata, Italy
dimitri.koroliouk@ukr.net
|
|
AN ADAPTIVE DEEP LEARNING SYSTEM FOR EXPLORING GENERAL DATA
Abstract. The authors present an approach to the analysis of a large amount of data that have no clear interconnection, but the nature of such a connection, if it exists, needs to be established. The possibility of automatic data processing and automatic change of parameters of the deep learning system for the formation of a deep learning platform during the analysis of data parameters is shown. It is also shown how such a system solves the needs of specialists from other fields who have never used deep learning systems. With the help of the well-known MNIST data set, it is established that with the use of individual parameters, it is possible to change their influence on the accuracy of prediction.
Keywords: neural network, automatic data processing, sampling informativeness, forecasting system, processing methods.
full text
REFERENCES
- Shaikh A.A., Doss A.N., Subramanian M., Jain V., Naved M., Mohiddin Md.K. Major applications of data mining in medical. Materials Today: Proceedings. 2022. Vol. 56, Pt. 4. P. 2300–2304. https://doi.org/10.1016/j.matpr.2021.11.642.
- Behrad F., Abadeh M.S. An overview of deep learning methods for multimodal medical data mining. Expert Systems with Applications. 2022. Vol. 200. Article 117006. https://doi.org/ 10.1016/j.eswa.2022.117006.
- Taranath N.L., Roopashree H.R., Yogeesh A.C., Darshan L.M., Subbaraya C.K. Medical decision support system using data mining. In: Predictive Modeling in Biomedical Data Mining and Analysis. Roy S., Goyal L.M., Balas V.E., Agarwal B., Mittal M. (Eds.). Academic Press, 2022. P. 49–64. "https://doi.org/10.1016/B978-0-323-99864-2.00007-X .
- Brescia M., Longo G. Astroinformatics, data mining and the future of astronomical research. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2013. Vol. 720. P. 92–94, https://doi.org/10.1016/j.nima.2012.12.027.
- Settino M., Ruffolo A., La Regina F. “Mining” the sky from data mining. Acta Astronautica. 2006. Vol. 59, Iss. 6. P. 499–502. "https://doi.org/10.1016/j.actaastro.2006.03.006.
- Chen Y., Kong R., Kong L. Applications of artificial intelligence in astronomical big data. In: Big Data in Astronomy. Kong L., Huang T., Zhu Y., Yu S. (Eds.). Elsevier, 2020. P. 347–375. https://doi.org/10.1016/B978-0-12-819084-5.00006-7.
- Carlbring P., Hadjistavropoulos H., Kleiboer A., Andersson G. A new era in Internet interventions: The advent of Chat-GPT and AI-assisted therapist guidance. Internet Interventions. 2023. 100621. https://doi.org/10.1016/j.invent.2023.100621 .
- Thelwall M., Sud P. Webometric research with the Bing Search API 2.0. Journal of Informetrics. 2012. Vol. 6, Iss. 1. P. 44–52. https://doi.org/10.1016/j.joi.2011.10.002.
- Antonello F., Baraldi P., Shokry A., Zio E., Gentile U., Serio L. A novel association rule mining method for the identification of rare functional dependencies in Complex Technical Infrastructures from alarm data. Expert Systems with Applications. 2021. Vol. 170. 114560. https://doi.org/10.1016/j.eswa.2021.114560 .
- He Y.-L., Ou G.-L., Fournier-Viger P., Huang J.Z., Suganthan P.N. A novel dependency-oriented mixed-attribute data classification method. Expert Systems with Applications. 2022. Vol. 199. 116782. https://doi.org/10.1016/j.eswa.2022.116782.
- Di Martino F., Loia V., Sessa S. Fuzzy transforms method and attribute dependency in data analysis. Information Sciences. 2010. Vol. 180, Iss. 4. P. 493–505. https://doi.org/10.1016/ j.ins.2009.10.012 .
- Koroliouk D.V., Korolyuk V.S. Filtration of stationary Gaussian statistical experiments. J. Math. Sci. 2018. Vol. 229, Iss. 1. P. 30–35. https://doi.org/10.1007/s10958-018-3660-0.
- Koroliouk D.V. Binary statistical experiments with persistent nonlinear regression. Theor. Probability and Math. Statist. 2015. Vol. 91. P. 71–80. https://doi.org/10.1090/tpms/967.
- Koroliouk D., Koroliuk V.S., Nicolai E., Bisegna P., Stella L., Rosato N. A statistical model of macromolecules dynamics for Fluorescence Correlation Spectroscopy data analysis. Statistics, Optimization & Information Computing. 2016. Vol. 4, N 3. P. 233–242. https://doi.org/ 10.19139/soic.v4i3.219.
- Koroliouk D.V. Multivariate statistical experiments with persistent non-linear regression and equilibrium. Theor. Probability and Math. Statist. 2016. Vol. 92. P. 71–79. https://doi.org/10.1090/tpms/983.
- Cybenko G.V. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems. 1989. Vol. 2, N 4. P. 303–314.
- Deng L. The MNIST database of handwritten digit images for machine learning research [best of the Web]. IEEE Signal Processing Magazine. 2012. Vol. 29, N 6. P. 141–142. https://doi.org/10.1109/MSP.2012.221147 7.
