Autonomous Transaction Model for E-Commerce Management Using Blockchain Technology

Sekar, S.; Solayappan, Annamalai; J., None Srimathi; Raja, S.; Durga, S.; Poongodi, M.; Hamdi, Mounir; Tunze, Godwin Brown

doi:10.4018/ijitwe.304047

Cited by 24 publications

(12 citation statements)

References 14 publications

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“…The system captures user data, processes it, and delivers a visual representation, ensuring transparency and security in the e-commerce industry. (Sekar S. et al, 2022).…”

Section: Review Of Literaturementioning

confidence: 99%

Transactionology of publication under reference and periodical sections using ICT and coding

Krishna Sharath,

Sreenivasa Rao

2024

Daluang: Journal of Library and Information Science

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Purpose. The project intends to use a qualitative approach to investigate the state and procedures of present transaction processes to pinpoint inefficiencies. Integrating ICT and coding solutions will be formulated and examined through prototyping to evaluate their feasibility and effectiveness. By concentrating on user behaviour and section-specific dynamics, the study offers a more analytical and data-driven approach to library management by leveraging Python's analytical capabilities and extracting quantitative data from transaction logs. Future funding can be directed towards required and necessary subjects by using library management's data to research trends and student requirements. This research can potentially transform library operations and ultimately enhance everyone's user experience for all.Methodology. The approach entails the utilization of ICT and programming to conduct a qualitative examination of data about the transaction aspects of publications.Results and discussion. By utilising ICT and coding, publication transaction data is consolidated efficiently in the preferred format, paving the way for advanced analytics and strategic insights with minimal human input.Conclusions. This study contributes to the examination, implementation, and consolidation of automated transaction recording across various sections of a library, with a prospective expansion towards integration with library management systems such as KOHA, Evergreen, Easylib, and Software for University Libraries (SOUL).

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“…The system captures user data, processes it, and delivers a visual representation, ensuring transparency and security in the e-commerce industry. (Sekar S. et al, 2022).…”

Section: Review Of Literaturementioning

confidence: 99%

Transactionology of publication under reference and periodical sections using ICT and coding

Krishna Sharath,

Sreenivasa Rao

2024

Daluang: Journal of Library and Information Science

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“…Then, Sekar S et al [8] proposed a blockchain-based autonomous trading system data security storage system to address the security issues of post-payment data storage in e-commerce applications. The experiment demonstrates that the system captures user data and effectively protects user privacy from external/bank infringement.…”

Section: Related Workmentioning

confidence: 99%

“…In Equation ( 7), M represents the information that needs to be encrypted. The calculation for decryption is shown in Equation (8).…”

Section: Design Of Data Sharing and Data Encryption Technology Solutionsmentioning

confidence: 99%

Internet of Things Security Design Based on Blockchain and Identity Re-encryption

Yang

2024

JCSANDM

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With the development of IoT technology, IoT devices have penetrated various fields, such as smart homes, industrial automation, healthcare, etc. However, the security issues of IoT devices have always troubled people. Attackers can use various means to attack IoT devices, such as stealing data, tampering with programs, malware infection, etc. These attack methods will bring great losses to users. In response to the above issues, this study uses proxy re-encryption technology to transform data collected by IoT devices into ciphertext. Then, it stores the encrypted IoT data on the blockchain. The combination of blockchain and proxy re-encryption technologies offers a method for the secure sharing of IoT data. The experimental results show that whether the algorithm encrypts a small or large amount of data, the encryption time is relatively short, and the time consumption does not exceed 15 ms. When encrypting 8196 bytes of data, its encryption time is 50% lower than the Attribute-Based Encryption (ABE) algorithm. The proposed IoT data security sharing method has the advantages of high security, fast encryption speed, and good stability, which helps to improve the security performance of the IoT and provides a new approach to IoT security.

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“…The regularization parameter C is set to 1 to maintain a compromise between maximizing the margin and minimizing the classification error. The Radial Basis Function (RBF) kernel is chosen for its ability to handle both linear and nonlinear patterns in the data [64][65][66][67][68][69][70][71][72][73]. The model undergoes 100 iterations during training to ensure convergence and optimal performance.…”

Section: A Dataset and Implementationmentioning

confidence: 99%

Research on the Application of Random Forest-based Feature Selection Algorithm in Data Mining Experiments

Wang

2023

IJACSA

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Handling high-dimensional big data presents substantial challenges for Machine Learning (ML) algorithms, mainly due to the curse of dimensionality that leads to computational inefficiencies and increased risk of overfitting. Various dimensionality reduction and Feature Selection (FS) techniques have been developed to alleviate these challenges. Random Forest (RF), a widely-used Ensemble Learning Method (ELM), is recognized for its high accuracy and robustness, including its lesser-known capability for effective FS. While specialized RF models are designed for FS, they often struggle with computational efficiency on large datasets. Addressing these challenges, this study proposes a novel Feature Selection Model (FSM) integrated with data reduction techniques, termed Dynamic Correlated Regularized Random Forest (DCRRF). The architecture operates in four phases: Preprocessing, Feature Reduction (FR) using Best-First Search with Rough Set Theory (BFS-RST), FS through DCRRF, and feature efficacy assessment using a Support Vector Machine (SVM) classifier. Benchmarked against four gene expression datasets, the proposed model outperforms existing RF-based methods in computational efficiency and classification accuracy. This study introduces a robust and efficient approach to feature selection in highdimensional big-data scenarios.

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Autonomous Transaction Model for E-Commerce Management Using Blockchain Technology

Cited by 24 publications

References 14 publications

Transactionology of publication under reference and periodical sections using ICT and coding

Transactionology of publication under reference and periodical sections using ICT and coding

Internet of Things Security Design Based on Blockchain and Identity Re-encryption

Research on the Application of Random Forest-based Feature Selection Algorithm in Data Mining Experiments

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