| Record Type: |
Electronic resources
: Monograph/item
|
| Title/Author: |
Computational intelligence techniques for 5G enabled IoT networks/ edited by Mohit Kumar ... [et al.]. |
| other author: |
Kumar, Mohit. |
| Published: |
Cham :Springer Nature Switzerland : : 2025., |
| Description: |
xi, 265 p. :ill. (some col.), digital ;24 cm. |
| [NT 15003449]: |
Part I. Computational Intelligence and Sustainability Solutions for Next-Gen IoT Networks -- Chapter 1. Computational Intelligence based Carbon Neutral Wireless Networks for Edge Cloud Continuum -- Chapter 2. AI/ML: Developing Algorithms for Handling Imbalanced Datasets in Classification Tasks -- Chapter 3. A Novel Algorithm for Digital Twin Accuracy -- Chapter 4. Google Cloud Spanner based Data Management Application for Smart Gaming Industry -- Part II. Optimization and Resilience Strategies for 5G-Enabled IoT Networks -- Chapter 5. Next Generation Cloud Security Paradigm: Orchestrating Cutting-Edge Machine Learning for DDoS Attack Detection Through Robust Optimization Algorithm -- Chapter 6. Role of Cognitive Radio in 5G Enabled IoT Networks -- Chapter 7. Influence of Soft Computing Techniques in Autonomous Vehicles -- Chapter 8. Disaster Resilience Communication through Optimized-UAV Enabled 5G Networks with D2D Communication -- Part III. Intelligent Resource Allocation and Service Optimization in 5G IoT Networks -- Chapter 9. Computational Intelligence based Optimal Service Placement in Fog-enabled IoT Networks -- Chapter 10. A study on Microservice Placement in the IoT-Fog Networks -- Chapter 11. Realtime IoT Offloading decision Strategy -- Chapter 12. IoT-Enabled 5G Smart Healthcare: Changing Patient Care and Connectivity -- Part IV. Case studies on Applied Computational Intelligence and 5G IoT Innovations for Industry 4.0 -- Chapter 13. Revolutionizing Agriculture: 5G-Enabled IoT Networks for Smart Farming -- Chapter 14. Real-Time Monitoring and Disease Detection in Greenhouses Using IoT and Deep Learning -- Chapter 15. Deep Learning-Driven IoT Framework for Detecting Pancreatic Neuroendocrine Tumors -- Chapter 16. Amalgamation of Computational Intelligence and 6G-enabled AIoT networks for Industry 4.0. |
| Contained By: |
Springer Nature eBook |
| Subject: |
Computational intelligence. - |
| Online resource: |
https://doi.org/10.1007/978-3-031-82733-4 |
| ISBN: |
9783031827334 |