Simplified intelligence (Record no. 23344)
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| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250818101011.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250818b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 27093 |
| Author | Vaishnavi, D. L. S. |
| 245 ## - TITLE STATEMENT | |
| Title | Simplified intelligence |
| Remainder of title | : shallow learning applications for urban transformation |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.3(3), Sep-Dec |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Ghaziabad |
| Name of publisher, distributor, etc. | MAT Journals |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 42-53p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This review explores the applications of shallow learning techniques in smart city ecosystems, focusing on their practicality and computational efficiency in urban data processing. The paper examines how shallow learning models contribute to urban infrastructure management, predictive maintenance, resource optimization, service delivery enhancement, and urban system modeling. It highlights the advantages of these models, including cost-effectiveness, scalability, and suitability for structured datasets typical in urban systems. The review also discusses challenges, such as limited adaptability to complex tasks and scalability constraints, and suggests potential solutions like hybrid shallow-deep learning models and automated feature engineering. This study demonstrates its potential for creating reliable, efficient, and sustainable smart city solutions by addressing these limitations and leveraging shallow learning's strengths. The findings aim to guide researchers and practitioners in advancing the application of shallow learning in smart cities and inspire future exploration to meet evolving urban needs. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 22401 |
| Topical term or geographic name entry element | Artificial Intelligence & Machine Learning |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25533 |
| Co-Author | Yogi, Manas Kumar |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Ghaziabad MAT Journals |
| Title | Research & review : Machine learning and cloud computing |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://matjournals.net/engineering/index.php/RRMLCC/article/view/1188 |
| 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 | 18/08/2025 | 2025-1330 | 18/08/2025 | 18/08/2025 | Articles Abstract Database |