Matters of Neural Network Repository Designing for Analyzing and Predicting of Spatial Processes

The article is devoted to solving the scientific problem of accumulating and systematizing models and machine learning algorithms by developing a repository of deep neural network models for analyzing and predicting of spatial processes in order to support the process of making managerial decisions in the field of ensuring conditions for sustainable development of regions. The issues of architecture development and software implementation of a repository of deep neural network models for spatial data analysis are considered, based on a new ontological model, which makes it possible to systematize models in terms of their application for solving design problems. An ontological model of a deep neural network repository for spatial data analysis is decomposed into the domain of deep machine learning models, problems being solved and data. Special attention is paid to the problems of storing data in the repository and the development of a subsystem for visualizing neural networks using a graph model. The authors have shown that for organizing a repository of deep neural network models, it is advisable to use a scientifically grounded set of database management systems integrated into a multi-model storage, characterizing the domains of using relational and NoSQL storages.

@article{Yamashkin2021,
  title     = {Matters of Neural Network Repository Designing for Analyzing and Predicting of Spatial Processes},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {5},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Stanislav A. Yamashkin and Anatoliy A. Yamashkin and Ekaterina O. Yamashkina and Anastasiya A. Kamaeva},
  doi       = {10.14569/IJACSA.2021.0120503},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120503}
}