Enhancing adaptive learning and decision-making systems using swarm intelligence and deep learning for advanced ai applications (Record no. 22713)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250424143516.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250424b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25983 |
| Author | Gupta, Brijendra |
| 245 ## - TITLE STATEMENT | |
| Title | Enhancing adaptive learning and decision-making systems using swarm intelligence and deep learning for advanced ai applications |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.15(2), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3482-3490p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The rapid development of autonomous vehicles (AVs) demands robust<br/>and adaptive AI systems capable of handling complex real-world<br/>environments. Traditional optimization and learning algorithms often<br/>struggle with dynamic and uncertain conditions, leading to suboptimal<br/>decision-making. Swarm intelligence, particularly Hawk Fire<br/>Optimization (HFO), offers a promising solution by simulating<br/>cooperative behaviors seen in nature, like hawks in hunting, to optimize<br/>decision-making processes. Coupled with advanced deep learning<br/>techniques like Federated Dropout Learning (FDL), this hybrid<br/>approach can enhance the adaptability, scalability, and efficiency of AI<br/>systems. This paper addresses the challenge of improving decision-<br/>making and learning in autonomous vehicles by integrating HFO with<br/>FDL. HFO optimizes parameters in real-time, allowing AVs to adapt<br/>rapidly to changing environments. Federated Dropout Learning, a<br/>variant of federated learning, further improves system resilience by<br/>sharing learning across distributed nodes while minimizing<br/>communication overhead and enhancing privacy. By combining these<br/>methods, the proposed system ensures robust performance in<br/>unpredictable scenarios. Experimental results show that the hybrid<br/>model outperforms traditional methods in terms of decision accuracy,<br/>response time, and energy efficiency. Specifically, the system achieved<br/>a 12% improvement in decision accuracy, reduced processing time by<br/>18%, and cut energy consumption by 22%, compared to standard<br/>algorithms. These findings suggest that the combination of HFO and<br/>FDL can significantly improve the performance of autonomous<br/>vehicles, providing safer and more efficient AI-driven navigation. |
| 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) | 25984 |
| Co-Author | Dusane, Atul |
| 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/IJSC_Vol_15_Iss_2_Paper_2_3482_3490.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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 24/04/2025 | 2025-0662 | 24/04/2025 | 24/04/2025 | Articles Abstract Database |