An ensemble learning approach to predict employees' preference for E-working in the post-pandemic world (Record no. 19624)
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| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230713095613.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230713b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 21353 |
| Author | Abesiri, S. A. D. D. |
| 245 ## - TITLE STATEMENT | |
| Title | An ensemble learning approach to predict employees' preference for E-working in the post-pandemic world |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.18(4), Dec |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Hyderabad |
| Name of publisher, distributor, etc. | IUP Publications |
| Year | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 7-24p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The Covid-19 pandemic has forced a large segment of the global workforce to shift to e-working. The pandemic has convinced many organizations that e-working has benefits for a successful business. As a result, it is critical to identify employees' suggestions and evaluate their motivation to continue the e-working concept in the post-pandemic world. The study was conducted by randomly surveying employees using various Machine Learning algorithms, including Naive Bayes, Decision Tree, Random Forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM) and logistic regression. The ensembling algorithm uses 66% of the percentage split method in the Waikato Environment for Knowledge Analysis (WEKA) tool. Accuracy, precision, recall, f-measure values and error rates were used to compare the results. The ensemble learning algorithm shows the best results with 90% accuracy, making it easier to predict employees' preference for e-working and accordingly take decisions. |
| 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) | 19709 |
| Co-Author | Rupasingha, R A H M |
| 773 0# - HOST ITEM ENTRY | |
| International Standard Serial Number | 0973-2896 |
| Place, publisher, and date of publication | Hyderabad IUP Publications |
| Title | IUP journal of information technology |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://iupindia.in/1222/Information%20Technology/An_Ensemble.asp |
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 13/07/2023 | 2023-0996 | 13/07/2023 | 13/07/2023 | Articles Abstract Database |