Forecasting IT industry trends using a fuzzy Decision Support System

Oleksii  Dudnyk; Zoia  Sokolovska; Alexander Gegov; Farzad Arabikhan

doi:10.1109/CAI54212.2023.00086

Forecasting IT industry trends using a fuzzy Decision Support System

Oleksii Dudnyk, Zoia Sokolovska, Alexander Gegov, Farzad Arabikhan

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

The Information Technology (IT) industry plays a vital role in the global economy and its significance will only continue to grow as digitalization accelerates. During times of increased entropy resulting from crises or military conflicts, the stable functioning of the IT industry is of paramount importance. This study explores the possibility of using a fuzzy Decision Support System (DSS) to forecast and predict how the IT industry in a specific country will operate. For the purpose of demonstration, we use self-developed FuzzyKIDE fuzzy DSS software platform, which employs fuzzy logic to provide a reasonable assessment even when the information is imprecise. We propose a DSS structure that utilizes a combined model of semantic networks and fuzzy implication rules. To demonstrate its effectiveness, we analyze Ukraine's IT industry at various time points and compare the results with historical data.

Original language	English
Title of host publication	Proceedings of the 2023 IEEE Conference on Artificial Intelligence
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	181-182
Number of pages	2
ISBN (Electronic)	9798350339840
ISBN (Print)	9798350339857
DOIs	https://doi.org/10.1109/CAI54212.2023.00086
Publication status	Published - 2 Aug 2023
Event	2023 IEEE Conference on Artificial Intelligence, CAI 2023 - Santa Clara, United States Duration: 5 Jun 2023 → 6 Jun 2023

Conference

Conference	2023 IEEE Conference on Artificial Intelligence, CAI 2023
Country/Territory	United States
City	Santa Clara
Period	5/06/23 → 6/06/23

Keywords

decision support system
development trends
fuzzy logic
FuzzyKIDE software platform
IT industry

Access to Document

10.1109/CAI54212.2023.00086

Forecasting IT Industry trends using a Fuzzy Decision Systems - Dudnyk Oleksii, Sokolovska Zoia, Alexander Gegov, Farzad Arabikhan
© © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript (Post-print), 197 KB

Cite this

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title = "Forecasting IT industry trends using a fuzzy Decision Support System",

abstract = "The Information Technology (IT) industry plays a vital role in the global economy and its significance will only continue to grow as digitalization accelerates. During times of increased entropy resulting from crises or military conflicts, the stable functioning of the IT industry is of paramount importance. This study explores the possibility of using a fuzzy Decision Support System (DSS) to forecast and predict how the IT industry in a specific country will operate. For the purpose of demonstration, we use self-developed FuzzyKIDE fuzzy DSS software platform, which employs fuzzy logic to provide a reasonable assessment even when the information is imprecise. We propose a DSS structure that utilizes a combined model of semantic networks and fuzzy implication rules. To demonstrate its effectiveness, we analyze Ukraine's IT industry at various time points and compare the results with historical data.",

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Dudnyk, O, Sokolovska, Z, Gegov, A & Arabikhan, F 2023, Forecasting IT industry trends using a fuzzy Decision Support System. in Proceedings of the 2023 IEEE Conference on Artificial Intelligence. Institute of Electrical and Electronics Engineers Inc., pp. 181-182, 2023 IEEE Conference on Artificial Intelligence, CAI 2023, Santa Clara, United States, 5/06/23. https://doi.org/10.1109/CAI54212.2023.00086

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