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Mathematics
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Blockchain Technology Applied in Accounting
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Blockchain Technology Applied in Accounting

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 5741

Special Issue Editor

Dr. Emilio Abad Segura

E-Mail Website1 Website2
Guest Editor
Department of Economics and Business, Universidad de Almería, 04120 La Cañada, Almería, Spain
Interests: financial economics and accounting; accounting; finance; business; CSR; higher education

Special Issue Information

Dear Colleagues,

Blockchain technology offers the opportunity of an unalterable triple record of accounting processes, thus guaranteeing the veracity of the information and allowing the automation of processes to dispose of the accounting statements more effectively, efficiently, and with greater guarantees, facilitating verification. Digitization in accounting, blockchain, and artificial intelligence have transformed accounting processes, especially, in aspects of control and verification.

The importance of blockchain in the transparency of management in public administrations is noteworthy, since a public record in an unalterable distributed ledger and verified by the community would make it difficult for cases of corruption and fraud to occur that undermine the trust of members of society in their leaders.

Submissions are welcome for research papers that present an analytical treatment with applications in various fields of research, such as business, finance, or applied mathematics, and interdisciplinary approaches that emphasize directions for future research and the implications for different users of financial information.

Dr. Emilio Abad Segura
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • blockchain
  • accounting
  • smart contract
  • big data
  • cryptocurrency
  • artificial intelligence
  • internet of things
  • mobile learning
  • cyber-security
  • digital ledger

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Published Papers (2 papers)

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Research

21 pages, 826 KiB  
Article
BlockCrime: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety
by Dev Patel, Harshil Sanghvi, Nilesh Kumar Jadav, Rajesh Gupta, Sudeep Tanwar, Bogdan Cristian Florea, Dragos Daniel Taralunga, Ahmed Altameem, Torki Altameem and Ravi Sharma
Mathematics 2022, 10(17), 3195; https://doi.org/10.3390/math10173195 - 4 Sep 2022
Cited by 7 | Viewed by 2805
Abstract
Detecting malicious activity in advance has become increasingly important for public safety, economic stability, and national security. However, the disparity in living standards incites the minds of certain undesirable members of society to commit crimes, which may disrupt society’s stability and mental calm. [...] Read more.
Detecting malicious activity in advance has become increasingly important for public safety, economic stability, and national security. However, the disparity in living standards incites the minds of certain undesirable members of society to commit crimes, which may disrupt society’s stability and mental calm. Breakthroughs in deep learning (DL) make it feasible to address such challenges and construct a complete intelligent framework that automatically detects such malicious behaviors. Motivated by this, we propose a convolutional neural network (CNN)-based Xception model, i.e., BlockCrime, to detect crimes and improve public safety. Furthermore, we integrate blockchain technology to securely store the detected crime scene locations and alert the nearest law enforcement authorities. Due to the scarcity of the dataset, transfer learning has been preferred, in which a CNN-based Xception model is used. The redesigned Xception architecture is evaluated against various assessment measures, including accuracy, F1 score, precision, and recall, where it outperforms existing CNN architectures in terms of train accuracy, i.e., 96.57%. Full article
(This article belongs to the Special Issue Blockchain Technology Applied in Accounting)
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24 pages, 2058 KiB  
Article
Blockchain and Double Auction-Based Trustful EVs Energy Trading Scheme for Optimum Pricing
by Riya Kakkar, Rajesh Gupta, Smita Agrawal, Pronaya Bhattacharya, Sudeep Tanwar, Maria Simona Raboaca, Fayez Alqahtani and Amr Tolba
Mathematics 2022, 10(15), 2748; https://doi.org/10.3390/math10152748 - 3 Aug 2022
Cited by 7 | Viewed by 2289
Abstract
Electric vehicles (EVs) have gained prominence in smart transportation due to their unparalleled benefits of reduced carbon footprints, improved performance, and intelligent energy trading mechanisms. These potential benefits have increased EV adoption at massive scales, but energy management in EVs is a critical [...] Read more.
Electric vehicles (EVs) have gained prominence in smart transportation due to their unparalleled benefits of reduced carbon footprints, improved performance, and intelligent energy trading mechanisms. These potential benefits have increased EV adoption at massive scales, but energy management in EVs is a critical study problem. The problem is further intensified due to the scarcity of charging stations (CSs) in near EV proximity. Moreover, as energy transactions occur over open channels, it presents critical security, privacy, and trust issues among decentralized channels. To address the open limitations of trusted energy management and optimize the pricing control among EV entities (i.e., prosumers and consumers), the paper proposes a scheme that integrates blockchain and a truthful double auction strategy for trustful EV trading. To address the transaction scalability, we integrate an Interplanetary File System (IPFS) with a double auction mechanism handled through the Remix Smart Contract environment. The double auction leverages an optimal payoff condition between peer EVs. To address the communication latency, we present the scheme at the backdrop of Fifth Generation (5G) networks that minimizes the optimal payoff response time. The scheme is simulated against parameters such as convergence, profit for consumers, computation time, and blockchain analysis regarding node commit latency, collusion attacks, and EV energy consumption. The results indicate the scheme’s viability against traditional (non-blockchain) approaches with high reliability, scalability, and improved cost-efficiency. Full article
(This article belongs to the Special Issue Blockchain Technology Applied in Accounting)
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