Ensemble model - based bankruptcy prediction (Record no. 22727)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250428145203.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250428b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25135 |
| Author | Arumugam, J. |
| 245 ## - TITLE STATEMENT | |
| Title | Ensemble model - based bankruptcy prediction |
| 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 | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3147-3153p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Bankruptcy prediction is a crucial task in the determination of an<br/>organization’s economic condition, that is, whether it can meet its<br/>financial obligations or not. It is extensively researched because it<br/>includes a crucial impact on staff, customers, management,<br/>stockholders, bank disposition assessments, and profitableness. In<br/>recent years, Artificial Intelligence and Machine Learning techniques<br/>have been widely studied for bankruptcy prediction and Decision-<br/>making problems. When it comes to Machine Learning, Artificial<br/>Neural Networks perform really well and are extensively used for<br/>bankruptcy prediction since they have proven to be a good predictor in<br/>financial applications. various machine learning models are integrated<br/>into one called the ensemble technique. It lessens the bias and variance<br/>of the ml model. This improves prediction power. The proposed model<br/>operated on quantitative and qualitative datasets. This ensemble model<br/>finds key ratios and factors of Bankruptcy prediction. LR, decision tree,<br/>and Naive Bayes models were compared with the proposed model’s<br/>results. Model performance was evaluated on the validation set.<br/>Accuracy was taken as a metric for the model’s performance evaluation<br/>purpose. Logistic Regression has given 100% accuracy on the<br/>Qualitative Bankruptcy Data Set dataset, resulting in the Ensemble<br/>model also performing well. |
| 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) | 26006 |
| Co-Author | Raja Sekar, S. |
| 773 0# - HOST ITEM ENTRY | |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| Place, publisher, and date of publication | Chennai ICT Academy |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/8_IJSC_Vol_14_Iss_1_Paper_8_3147_3153.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 | 28/04/2025 | 2025-0684 | 28/04/2025 | 28/04/2025 | Articles Abstract Database |