Tania STAROVOYT
The National University of Water and Environmental Engineering, Rivne, Ukraine
Yuriy ZAYCHENKO,DSc.(Engin.), Prof.
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine
Abstract
DOI: https://doi.org/10.17721/AIT.2023.1.06
Background. Efficiently converting large amounts of unstructured text data into spatial information is crucial for managing water distribution systems. This allows for the conversion of extensive sets of text information, such as reports, orders, letters, and other documents, into point classes of spatial objects in geographic information systems. To tackle this challenge, a promising new approach involves combining hybrid quantum-classical neural networks with geo-information technologies.
Methods. The study utilized quantum-enhanced hybrid neural networks in combination with GIS methods to identify named entities such as personal accounts and balance sheet objects of Kyivvodokanal by their addresses and geocoding. This information was then published on a geoportal using the ArcGIS Enterprise platform in real-time, which holds great promise for effective water management. The performance of the developed model was evaluated by accuracy indicators, recall parameters, and weighted harmonic average of accuracy and recall.
Results. The obtained results indicate that the developed hybrid quantum-classical model of artificial intelligence can be successfully applied to transform large volumes of unstructured textual information into spatial information. The model was integrated into GIS using ArcGIS Enterprise. By combining the obtained point classes of spatial objects with already existing data, methods of spatial connections, an interactive map with an update interval of every five minutes was developed.
Conclusions. Taking advantage of quantum computing and combining it with classical hardware and classical AI models, it became possible to achieve similar and even better performance in various tasks compared to state-of-the-art methods. Quantum natural language processing is a promising new field that has the potential to revolutionize the way one analyzes and understands human language.
Keywords: QNLP, hybrid quantum-classical neural networks, geocoding, geoparsing, geographic information systems, ArcGIS, entity recognition, unstructured textual information, variational quantum igniter, QLSTM, spatial objects.
Information about the author
Tania Starovoyt, Student, 5th-year student of the Department of Hydroinformatics of the National University of Water and Environmental Engineering.
Research interests: geospatial artificial intelligence, quantum artificial intelligence, computational intelligence methods in water distribution systems, cognitive computing, swarm intelligence.
Yuriy Zaychenko, DSc (Engin.), Prof., Head of the Department of Second Higher and Postgraduate Education of the Institute of Applied and System Analysis, Professor of the Department of Mathematical Methods of System Analysis of Ihor Sikorskyi Kyiv Polytechnic Institute.
Research interests: the theory of decision-making under conditions of uncertainty, the application of systems with fuzzy logic and fuzzy neural networks in intelligent systems, the development of new models, methods and algorithms in the field of computational intelligence and their application in economics and financial analysis.
References
Published
2023-12-15
How to Cite
T. Starovoyt, Yu. Zaychenko “A hybrid quantum-perfected model of artificial intelligence in the problem of automatic recognition and fast conversion of unstructured text information into spatial”, Advanced Information Technology, vol.1(2), pp.38-48, 2023.
Issue
Advanced Information Technology № 1 (2), 2023
Section
Applied information systems and technology
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