Machine learning algorithm for fintech innovation in blockchain applications (Record no. 22725)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250425154741.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250425b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26003 |
| Author | Lakshmana Narayanan, V. |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning algorithm for fintech innovation in blockchain applications |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.14(1), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3165-3172p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The rapid growth of Fintech innovation and the widespread adoption<br/>of blockchain technologies have indeed had a transformative impact on<br/>the financial industry. In this paper, the focus is on the application of<br/>machine learning algorithms, specifically the Random Forest<br/>Regression algorithm, within the context of Fintech and blockchain.<br/>This research contributes to the advancement of machine learning<br/>techniques in the field of Fintech and blockchain. The Random Forest<br/>Regression algorithm utilizes ensemble learning, combining multiple<br/>decision trees to analyze complex financial data and make predictions<br/>on various outcomes. This algorithm has proven to be effective in<br/>addressing key challenges within the industry, such as predicting loan<br/>defaults, detecting fraud, and assessing risks. Through experimental<br/>evaluations and case studies, the paper demonstrates the effectiveness<br/>of the Random Forest Regression algorithm in enhancing Fintech<br/>innovation in blockchain applications. The algorithm improved<br/>accuracy, scalability, and interpretability enable financial institutions<br/>to make data-driven decisions and optimize their operations. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26004 |
| Co-Author | Ramesh Pandi, G. |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Chennai ICT Academy |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/10_IJSC_Vol_14_Iss_1_Paper_10_3165_3172.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 25/04/2025 | 2025-0682 | 25/04/2025 | 25/04/2025 | Articles Abstract Database |