prikaz prve stranice dokumenta Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture
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Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture
U: Ceur Workshop Proceedings SEBD 2020. Villasimius: University of Padua ; University of Cagliari ; University of BologPolitecnico di Milanona, 2020. str. 88-99. urn:nbn:hr:112:870310
Župan, Mario; Letinić, Svjetlana; Budimir, Verica

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Župan, M., Letinić, S. i Budimir, V. (2020). Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture. Ceur Workshop Proceedings SEBD 2020 (str. 88-99). Villasimius: University of Padua ; University of Cagliari ; University of BologPolitecnico di Milanona. Preuzeto s https://urn.nsk.hr/urn:nbn:hr:112:870310

Župan, Mario, et al. "Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture." Ceur Workshop Proceedings SEBD 2020, University of Padua ; University of Cagliari ; University of BologPolitecnico di Milanona, 2020, str. 88-99. https://urn.nsk.hr/urn:nbn:hr:112:870310

Župan, Mario, Svjetlana Letinić i Verica Budimir. "Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture." U Ceur Workshop Proceedings SEBD 2020, 88-99. Villasimius: University of Padua ; University of Cagliari ; University of BologPolitecnico di Milanona, 2020. Citirano 05.02.2025.. https://urn.nsk.hr/urn:nbn:hr:112:870310

Župan, M., Letinić, S. i Budimir, V. (2020) 'Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture', Ceur Workshop Proceedings SEBD 2020, University of Padua ; University of Cagliari ; University of BologPolitecnico di Milanona, Villasimius, str. 88-99. Preuzeto s: https://urn.nsk.hr/urn:nbn:hr:112:870310 (Datum pristupa: 05.02.2025.)

Župan M, Letinić S, Budimir V. Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture. Ceur Workshop Proceedings SEBD 2020. Villasimius: University of Padua ; University of Cagliari ; University of BologPolitecnico di Milanona; 2020. Str. 88-99. [pristupljeno 05.02.2025.] Dostupno na: https://urn.nsk.hr/urn:nbn:hr:112:870310

M. Župan, S. Letinić i V. Budimir, "Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture", Ceur Workshop Proceedings SEBD 2020 Villasimius: University of Padua ; University of Cagliari ; University of BologPolitecnico di Milanona, 2020. [Online] Dostupno na: https://urn.nsk.hr/urn:nbn:hr:112:870310 [Citirano: 05.02.2025.]

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Podaci o radu
Naslov (engleski)Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture
AutorMario Župan
AutorSvjetlana Letinić
AutorVerica Budimir
Autorova ustanovaVeleučilište u Požegi
Znanstveno / umjetničko
područje, polje i grana		DRUŠTVENE ZNANOSTI
Ekonomija
Znanstveno / umjetničko
područje, polje i grana		DRUŠTVENE ZNANOSTI
Informacijske i komunikacijske znanosti
Sažetak (engleski)
Our tries to learn machines how to reconstruct journal entries with the aim of finding anomalies lead us to deep learning (DL) technologies. Nowadays Variational autoencoder and Long short- term memory architectures as well as other deep learning architectures solves wide range of problems, yet they are not enough implemented in a field of accounting information systems (AIS). Inside AIS, accounting data follows accounting logic and makes specific datasets constructed by different type of columns - categorical and continuous variables. Our aim is reconstruction of these variables. Development of the model capable for precise reconstruction is not an easy task. This paper describes our research for anomaly detection model architecture which will be capable to reconstruct dataset with categorical features mixed with continuous monetary value feature. We developed basic models trained on accounting journals from 2007 to 2018 and then tested in the fiscal year 2019. Still, lots of hyperparameters need to be checked if we want to improve accuracy. Deep learning research is an engineering task leaded by experience so there is no linearity in the model improvement. Consequently, this paper is our contribution to collection of experience in developing accurate, useful and intelligent accounting control system.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov zbornikaCeur Workshop Proceedings SEBD 2020
Brojčani podacistr. 88-99
e-ISSN1613-0073
URN:NBNurn:nbn:hr:112:870310
Datum objave publikacije2020
SkupNaziv: 28th Italian Symposium on Advanced Database Systems
Lokacija: Villasimius, Italija
Datum početka: 2020-06-21
Datum završetka: 2020-06-24
Vrsta izlaganja: Predavanje
Vrsta resursaTekst
IzdavačUniversity of Padua ; University of Cagliari ; University of BologPolitecnico di Milanona
Mjesto izdavanjaVillasimius
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Zaštićeno autorskim pravom.
Datum i vrijeme pohrane2024-05-07 09:24:17