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

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

Župan, Mario, Svjetlana Letinić and Verica Budimir. "Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture." In Ceur Workshop Proceedings SEBD 2020, 88-99. Villasimius: University of Padua ; University of Cagliari ; University of BologPolitecnico di Milanona, 2020. Accessed 2024 August 24. https://urn.nsk.hr/urn:nbn:hr:112:870310

Župan, M., Letinić, S. and 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, pp. 88-99. Available at: https://urn.nsk.hr/urn:nbn:hr:112:870310 (Accessed 24 August 2024)

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. Pp. 88-99. [cited 2024 August 24] Available at: https://urn.nsk.hr/urn:nbn:hr:112:870310

M. Župan, S. Letinić and 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] Available at: https://urn.nsk.hr/urn:nbn:hr:112:870310 [Accessed: 24 August 2024]

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Metadata
Title (english)Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture
AuthorMario Župan
AuthorSvjetlana Letinić
AuthorVerica Budimir
Author's institutionPolytehnic in Pozega
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Economics
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Information and Communication Sciences
Abstract (english)
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.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Publication titleCeur Workshop Proceedings SEBD 2020
Numberingpp. 88-99
e-ISSN1613-0073
URN:NBNurn:nbn:hr:112:870310
Publication2020
ConferenceTitle: 28th Italian Symposium on Advanced Database Systems
Location: Villasimius, Italy
Start date: 2020-06-21
End date: 2020-06-24
Assembly type: Lecture
Type of resourceText
PublisherUniversity of Padua ; University of Cagliari ; University of BologPolitecnico di Milanona
Publishing placeVillasimius
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-05-07 09:24:17