Author(s)

Lingqi Xue ¹

Affiliation(s)

¹ College of Accounting, Zhanjiang Science and Technology College, Zhanjiang 524094, Guangdong, China

Corresponding Author

Lingqi Xue

ABSTRACT

The financial problem can be easily solved, all exchange of money processes can be accurately followed. The transaction money can be followed, and the interest of the money also followed accurately. In this financial field, this method implemented many technologies. The financial big data can be used to store complete details about the separated accounts. The Field Programmable Gate Array (FPGA) can control the entire system, and the K-Nearest Neighbors Density Perak Clustering (KNNDPC) algorithm can be used to solve the mathematical issues. The input can be taken from the user, and the details can be processed to have some steps, then it can be process based on the Field Programmable Gate Array (FPGA) controller. Then the output can be taken through the operating system like the personal computer, the implementation of Field Programmable Gate Array (FPGA) can control the complete process. The existing system contains the algorithm is Deep Learning (DL) algorithm and K-Means algorithm. The demerits of the existing algorithm can occur, and the desired accuracy cannot be achieved. The proposed algorithm is the K-Nearest Neighbors Density Peak Clustering (KNNDPC) algorithm, and the proposed system overcomes the existing demerits.

KEYWORDS

Finance Problem, Financial Field, Field Programmable Gate Array (Fpga) Controller, Is Deep Learning (Dl) Algorithm and K-Means Algorithm, Error, Big Data

CITE THIS PAPER

Lingqi Xue, Research and Application of Financial Big Data Based on Fpga and K nearest Neighbor Density Peak Clustering. Financial Engineering and Risk Management (2022) Vol. 5: 14-25. DOI: http://dx.doi.org/10.23977/ferm.2022.050203.

REFERENCES

[1] S. Kurt, J. Heitz, N. Bundi and W. Breymann, "Large-Scale Data-Driven Financial Risk Modeling Using Big Data Technology," 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), Zurich, 2018, pp. 206-207, doi: 10.1109/BDCAT.2018.00033.
[2] M. Lokanan, "Coding and Analytical Problems with Big Data When Conducting Research on Financial Crimes," 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 5386-5388, doi: 10.1109/BigData.2018.8621976.
[3] A. V. Bataev, "Analysis of the Application of Big Data Technologies in the Financial Sphere," 2018 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT&QM&IS), Saint Petersburg, Russia, 2018, pp. 568-572, doi: 10.1109/ITMQIS.2018.8525121.
[4] T. Zhang, "Research on Big Data's Prevention Technology of Financial Systemic Risk," 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Vientiane, Laos, 2020, pp. 663-666, doi: 10.1109/ICITBS49701.2020.00145.
[5] H. Wang, "Research on Financial Information Integration of Agricultural Supply-Side Structural Reform in Heilongjiang Province Under the System  of Big Data," 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), Jishou, China, 2019, pp. 299-302, doi: 10.1109/ICVRIS.2019.00079.
[6] A. V. Bataev, "Evaluation of Using Big Data Technologies in Russian Financial Institutions," 2018 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT&QM&IS), Saint Petersburg, Russia, 2018, pp. 573-577, doi: 10.1109/ITMQIS.2018.8524938.
[7] R. Yang, "Research on the Risk and Supervision Method of Big Data Application in Financial Field," 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Vientiane, Laos, 2020, pp. 695-698, doi: 10.1109/ICITBS49701.2020.00153.
[8] Leilei, "The relation between financial industry agglomeration and industrial economic growth in Hubei province based on big data analysis," 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), Chongqing, China, 2020, pp. 494-496, doi: 10.1109/CVIDL51233.2020.00-43.
[9] N. Wang, Y. Liu, Z. Liu and X. Huang, "Application of Artificial Intelligence and Big Data in Modern Financial Management," 2020 International Conference on Artificial Intelligence and Education (ICAIE), Tianjin, China, 2020, pp. 85-87, doi: 10.1109/ICAIE50891.2020.00027.
[10] Y. Sun and Z. Lu, "Financial Quantitative Big Data Platform Based on High Performance Computing," 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), New York, NY, USA, 2019, pp. 303-307, doi: 10.1109/CSE/EUC.2019.00065.
[11] C. Ren, L. Sun and Q. Wu, "Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors," 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Dalian, China, 2019, pp. 664-668, doi: 10.1109/ISKE47853.2019.9170434.
[12] D. Yu, G. Liu, M. Guo, X. Liu and S. Yao, "Density Peaks Clustering Based on Weighted Local Density Sequence and Nearest Neighbor Assignment," in IEEE Access, vol. 7, pp. 34301-34317, 2019, doi: 10.1109/ACCESS.2019.2904254.
[13] C. Ren, L. Sun, Y. Yu and Q. Wu, "Effective Density Peaks Clustering Algorithm Based on the Layered K-Nearest Neighbors and Subcluster Merging," in IEEE Access, vol. 8, pp. 123449-123468, 2020, doi: 10.1109/ACCESS.2020.3006069.
[14] L. Sun and R. Zhang, "Density Clustering Algorithm Based on the Dynamic Selection of Cluster Center," 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Guilin, China, 2019, pp. 253-260, doi: 10.1109/CyberC.2019.00050.
[15] J. Chen and P. Yu, "A Domain Adaptive Density Clustering Algorithm for Data with Varying Density Distribution," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2019.2954133.
[16] Dr.G.Kavithaa et al., "Reliability Enhancement of a power semiconductor with optimized solder layer thickness," IEEE Transactions on Power Electronics, June 2020, Volume: 35, Issue: 6, pp: 6397 - 6404, DOI: 10.1109/TPEL.2019.2951815
[17] A. Sangeetha, Dr. M. Chandrasekaran, Dr.  G. Kavithaa, 2020, "Time situate recurrence estimation technique for efficient data collection in war field sensor network," in Microprocessors and Microsystems, Elsevier, March 2020, Volume 73, https://doi.org/10.1016/j.micpro.2020.102988
[18] Dr. G. Kavithaa, M. Prithivi Raj, 2019, "Dynamic signal driving strategy based high speed and low powered dual edge-triggered flip flop design used memory applications," in Microprocessors and Microsystems, Elsevier, November 2019, Volume 71, https://doi.org/10.1016/j.micpro.2019.102879

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