A hybrid approach for search and rescue using 3DCNN and PSO

Search and rescue are essential applications of disaster management in which people are evacuated from the disaster-prone area to a safer place. This overall process of search and rescue can be more efficient if an automated system can quickly locate the human or area where rescue is required. To pr...

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Bibliographic Details
Published in:	Neural computing & applications Vol. 33; no. 17; pp. 10813 - 10827
Main Authors:	Mishra, Balmukund, Garg, Deepak, Narang, Pratik, Mishra, Vipul
Format:	Journal Article
Language:	English
Published:	London Springer London 01.09.2021 Springer Nature B.V
Subjects:	Artificial Intelligence Automation Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Disaster management Evacuations & rescues Image Processing and Computer Vision Mathematical models Natural disasters Optimization Parameters Powered models Probability and Statistics in Computer Science S. I : Hybridization of Neural Computing with Nature Inspired Algorithms Searching Special Issue on Hybridization between Neural Computing and Nature Inspired Algorithms for Solving Multi-Criteria Decision-Making Problems Surveillance Training Tuning Video data Action recognition Unmanned aerial vehicle Particle swarm optimization (PSO) Convolution neural network Search and rescue Drone-based surveillance
ISSN:	0941-0643, 1433-3058
Online Access:	Get full text
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