Szczegóły publikacji
Opis bibliograficzny
Application of hybrid systems SARIMA ANFIS for monitoring workforce dynamics / Jakub Nowak, Marcin Korytkowski, Rafał SCHERER, Błażej Żak, Zorza Tymorek, Anita Zbieg // W: ISD2025 [Dokument elektroniczny] : [33rd international conference on Information Systems Development] : September 3-5, 2025, Belgrade, Serbia] : empowering the interdisciplinary role of ISD in addressing contemporary issues in digital transformation: how data science and generative AI contributes to ISD? : proceedings / eds. I. Luković, [et al.]. — Wersja do Windows. — Dane tekstowe. — Gdańsk : University of Gdańsk ; Belgrade : University of Belgrade, 2025. — ( Proceedings of the International Conference on Information Systems Development ; ISSN 2938-5202 ). — e-ISBN: 978-83-972632-1-5. — S. [1–8]. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1679&con... [2025-12-04]. — Bibliogr. s. [8], Abstr. — R. Scherer - dod. afiliacja: Czestochowa University of Technology Faculty of Computer Science and Artificial Intelligence, Czestochowa, Poland
Autorzy (6)
- Nowak Jakub
- Korytkowski Marcin
- AGHScherer Rafał
- Żak Błażej
- Tymorek Zorza
- Zbieg Anita
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164723 |
|---|---|
| Data dodania do BaDAP | 2025-12-15 |
| DOI | 10.62036/ISD.2025.91 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Uniwersytet Gdański |
| Konferencja | International Conference on Information Systems Development 2025 |
| Czasopismo/seria | Proceedings of the International Conference on Information Systems Development |
Abstract
This paper uses a hybrid SARIMA system and an adaptive neuro-fuzzy inference system (ANFIS) to analyze data on interactions between employees in a real-world entity, including email exchanges, chat messages from meetings, and in-person meetings, for the purpose of detecting position changes such as promotions, demotions, and supervisor changes. The dataset, comprising approximately 184 GB of textual data, includes sixteen features related to employee interactions, such as internal contacts, communication with supervisors, subordinates, and individuals at various levels of the hierarchy. The developed system achieved a detection accuracy of 96%, confirming its usefulness in monitoring personnel processes and optimizing human resource management. In this study, the SARIMA model was combined with the ANFIS system, enabling more precise forecasting of changes over time and the detection of employee behaviors, such as sudden position changes or team interactions. By operating in a quasi-real-time mode, the system allows for the rapid identification of potential irregularities, enhancing organizational security, and supporting personnel decision-making in dynamically changing conditions. The results of our research indicate that hybrid models that integrate the analysis of large datasets and flexible inference systems can effectively support management and behavioral profiling in organizations.
