Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments

Evgeniy Bryndin

doi:doi:10.11648/j.ajsea.20190802.11

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Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments

Evgeniy Bryndin

Published in American Journal of Software Engineering and Applications (Volume 8, Issue 2)

Received: 7 October 2019 Accepted: 25 October 2019 Published: 31 October 2019

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Abstract

Specialists of past generations have accumulated huge and valuable experience of analytics and forecasting in a variety of subject areas. Within the framework of the development of methods of processing scientific data, taking into account the accumulated experience of generations of specialists, it is possible to structure, specify, classify and rank them into flows of smart data for automation of research of various scientific problems by cognitive systems of artificial intelligence. Automated analytics is based on big data. A large amount of data is generated in real time by modeling a scientific experiment. When working with data, it must be processed as efficiently as possible to get the correct output. The main thing is to prepare the training sample correctly. If you select the training data sampling principle correctly, you can scale the task using a more complete set of data. It should be understood that rationing and data preparation is crucial for traditional machine learning. This process significantly affects the choice of the architecture of neural networks used, especially in so-called deep learning, when it is necessary to correctly determine the number of hidden layers in the neural network and the number of artificial neurons in them. One of the main advantages of multilayer neural networks is the simulation of the work of some complex mathematical dependence.

Published in	American Journal of Software Engineering and Applications (Volume 8, Issue 2)
DOI	10.11648/j.ajsea.20190802.11
Page(s)	36-43
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Scientific Data, Topology of Data, Artificial Intelligence, Trance - Disciplinary Researches

References

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[2]	Brazhe R. A. Mathematical modeling of nanostructures and their physical properties. USTUУ. 2014. 99 pages.
[3]	Evgeniy Bryndin. BIG DATA MODELLING of TRANSFORMATION and BIFURCATION of NANOSTRUCTURES. Inter conference "Management of development of large-scale systems (MLSD’2018"). T. 2 - M.: IPM RAS, 2018. Page 340-343.
[4]	Demchenko Y., Laat C. De, Membrey P. Defining architecture components of the Big Data Ecosystem. Collaboration Technologies and Systems (CTS), 2014 International Conference. 2014. May. P. 104–112.
[5]	Evgeniy Bryndin. Cognitive Robots with Imitative Thinking for Digital Libraries, Banks, Universities and Smart Factories. International Journal of Management and Fuzzy Systems. V. 3, N. 5, 2017, pp 57-66.
[6]	Evgeniy Bryndin. Program Hierarchical Realization of Adaptation Behavior of the Cognitive Mobile Robot with Imitative Thinking. International Journal of Engineering Management. Volume 1, Issue 4. 2017, pp. 74-79.
[7]	Evgeniy Bryndin. Technological Thinking, Communication and Behavior of Androids. Communications. Vol. 6, No. 1, 2018. Pages: 13-19.
[8]	Evgeniy Bryndin. Communicative Associative Logic of Cognitive Professional Robot with Imitative Thinking. Journal Engineering Mathematics, V. 2, Issue 2. 2018. Pages: 79-85.
[9]	Victor Maier-Shenberger, Kenneth Kukyer. Big data. Revolution which will change how we live we work and we think. — M.: Mann, Ivanov and Ferber, 2014. —240 pages.
[10]	Evgeniy Bryndin. Modeling of Transformation of Nanostructures by Cognitive Systems on the Basis of Big Smart Data. International Journal of Artificial Intelligence and Mechatronics. Volume 7, Issue 4. 2019. P. 19-22.
[11]	Evgeniy Bryndin. Digital technologies of the industry 4.0. Chepter 10, С. 201-222, Book: Computer Science Advances: Research and Applications. USA: Nova Science Publisher. 2019. 252 pages.
[12]	Evgeniy Bryndin. System retraining to professional competences of cognitive robots on basis of communicative associative logic of technological thinking. International Robotics Automation Journal. 2019; 5 (3): 112-119.
[13]	Evgeniy Bryndin. Mobile Innovative Transformational Ecosystem of Management of Humane Technological Society. Integrative Journal of Conference Proceedings. Volume 1, Issue 3, 2019.
[14]	Evgeniy Bryndin. Human Digital Doubles with Technological Cognitive Thinking and Adaptive Behaviour. Software Engineering, Volume 7, Issue 1, 2019. P. 1-9.
[15]	Evgeniy Bryndin, Irina Bryndina. Technological Diagnostics of Human Condition According to Spectral Analysis of Biofield. Advances in Bioscience and Bioengineering. Volume 7, Issue 3, 2019. Pages: 64-68.
[16]	Evgeniy Bryndin. Mainstreaming technological development of industrial production based on artificial intelligence. COJ Technical & Scientific Research, 2 (3). 2019. Pages: 1-5.
[17]	Evgeniy Bryndin. Robots with Artificial Intelligence and Spectroscopic Sight in Hi-Tech Labor Market. International Journal of Systems Science and Applied Mathematic, V. 4, № 3, 2019. Pages: 31-37.

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Evgeniy Bryndin. (2019). Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments. American Journal of Software Engineering and Applications, 8(2), 36-43. https://doi.org/10.11648/j.ajsea.20190802.11

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ACS Style

Evgeniy Bryndin. Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments. Am. J. Softw. Eng. Appl. 2019, 8(2), 36-43. doi: 10.11648/j.ajsea.20190802.11

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Evgeniy Bryndin. Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments. Am J Softw Eng Appl. 2019;8(2):36-43. doi: 10.11648/j.ajsea.20190802.11

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@article{10.11648/j.ajsea.20190802.11,
  author = {Evgeniy Bryndin},
  title = {Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments},
  journal = {American Journal of Software Engineering and Applications},
  volume = {8},
  number = {2},
  pages = {36-43},
  doi = {10.11648/j.ajsea.20190802.11},
  url = {https://doi.org/10.11648/j.ajsea.20190802.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20190802.11},
  abstract = {Specialists of past generations have accumulated huge and valuable experience of analytics and forecasting in a variety of subject areas. Within the framework of the development of methods of processing scientific data, taking into account the accumulated experience of generations of specialists, it is possible to structure, specify, classify and rank them into flows of smart data for automation of research of various scientific problems by cognitive systems of artificial intelligence. Automated analytics is based on big data. A large amount of data is generated in real time by modeling a scientific experiment. When working with data, it must be processed as efficiently as possible to get the correct output. The main thing is to prepare the training sample correctly. If you select the training data sampling principle correctly, you can scale the task using a more complete set of data. It should be understood that rationing and data preparation is crucial for traditional machine learning. This process significantly affects the choice of the architecture of neural networks used, especially in so-called deep learning, when it is necessary to correctly determine the number of hidden layers in the neural network and the number of artificial neurons in them. One of the main advantages of multilayer neural networks is the simulation of the work of some complex mathematical dependence.},
 year = {2019}
}

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TY - JOUR
T1 - Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments
AU - Evgeniy Bryndin
Y1 - 2019/10/31
PY - 2019
N1 - https://doi.org/10.11648/j.ajsea.20190802.11
DO - 10.11648/j.ajsea.20190802.11
T2 - American Journal of Software Engineering and Applications
JF - American Journal of Software Engineering and Applications
JO - American Journal of Software Engineering and Applications
SP - 36
EP - 43
PB - Science Publishing Group
SN - 2327-249X
UR - https://doi.org/10.11648/j.ajsea.20190802.11
AB - Specialists of past generations have accumulated huge and valuable experience of analytics and forecasting in a variety of subject areas. Within the framework of the development of methods of processing scientific data, taking into account the accumulated experience of generations of specialists, it is possible to structure, specify, classify and rank them into flows of smart data for automation of research of various scientific problems by cognitive systems of artificial intelligence. Automated analytics is based on big data. A large amount of data is generated in real time by modeling a scientific experiment. When working with data, it must be processed as efficiently as possible to get the correct output. The main thing is to prepare the training sample correctly. If you select the training data sampling principle correctly, you can scale the task using a more complete set of data. It should be understood that rationing and data preparation is crucial for traditional machine learning. This process significantly affects the choice of the architecture of neural networks used, especially in so-called deep learning, when it is necessary to correctly determine the number of hidden layers in the neural network and the number of artificial neurons in them. One of the main advantages of multilayer neural networks is the simulation of the work of some complex mathematical dependence.
VL - 8
IS - 2
ER -

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Author Information

Evgeniy Bryndin

Scientific Department, Research Center "Estestvoinformatika", Novosibirsk, Russia

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APA Style

Evgeniy Bryndin. (2019). Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments. American Journal of Software Engineering and Applications, 8(2), 36-43. https://doi.org/10.11648/j.ajsea.20190802.11

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ACS Style

Evgeniy Bryndin. Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments. Am. J. Softw. Eng. Appl. 2019, 8(2), 36-43. doi: 10.11648/j.ajsea.20190802.11

Copy | Download

AMA Style

Evgeniy Bryndin. Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments. Am J Softw Eng Appl. 2019;8(2):36-43. doi: 10.11648/j.ajsea.20190802.11

Copy | Download

@article{10.11648/j.ajsea.20190802.11,
  author = {Evgeniy Bryndin},
  title = {Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments},
  journal = {American Journal of Software Engineering and Applications},
  volume = {8},
  number = {2},
  pages = {36-43},
  doi = {10.11648/j.ajsea.20190802.11},
  url = {https://doi.org/10.11648/j.ajsea.20190802.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20190802.11},
  abstract = {Specialists of past generations have accumulated huge and valuable experience of analytics and forecasting in a variety of subject areas. Within the framework of the development of methods of processing scientific data, taking into account the accumulated experience of generations of specialists, it is possible to structure, specify, classify and rank them into flows of smart data for automation of research of various scientific problems by cognitive systems of artificial intelligence. Automated analytics is based on big data. A large amount of data is generated in real time by modeling a scientific experiment. When working with data, it must be processed as efficiently as possible to get the correct output. The main thing is to prepare the training sample correctly. If you select the training data sampling principle correctly, you can scale the task using a more complete set of data. It should be understood that rationing and data preparation is crucial for traditional machine learning. This process significantly affects the choice of the architecture of neural networks used, especially in so-called deep learning, when it is necessary to correctly determine the number of hidden layers in the neural network and the number of artificial neurons in them. One of the main advantages of multilayer neural networks is the simulation of the work of some complex mathematical dependence.},
 year = {2019}
}

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