Special Issue on Advances in Unmanned Aerial Vehicles with Fuzzy Fused Hierarchical Deep Neural Networks
Research on Unmanned aerial vehicles (UAV) continue to investigate new technologies that maximize their efficiency in everyday operations. Due to their infinite number of use-cases, UAVs are at the forefront of finding innovative technologies to offer better and faster decision-making abilities with improved quality of services. Further, the commercial UAV sector is in a way towards the new era of automation. Regardless of its application, every UAV system faces some challenges when it comes to decision making and automation in performing its operations. They are in need of intelligent algorithms and predictive functionality assisted by drone technologies to make an accurate analysis of aerial insights. Since the UAV makes use of sensors to measure, capture, transmit, and store the multiple layers of the data, it has become crucial to detect patterns and trends in real-time. Ranging from infrastructure asset management to disaster response assistance systems, they require improved technologies to bridge the gap for better and more informed decision making. This is because the accuracy of the prediction algorithm may be relatively high, but the prediction result can be obscure.
Integrating fuzzy fused hierarchical deep neural networks allow the UAV to perceive and understand the world more powerfully and innovatively. The use of these techniques provides the biologically inspired simulation models to perform the specific task. It significantly enhances the process of UAV data collection, analysis, prediction, and processing of the data with far-reaching benefits across various streams such as energy, agriculture, urban management, and emergency services. It provides powerful visualization and enhanced data exploration of the UAV data delivering more robust and timely insights across various sectors. Practically these two paradigms go hand in hand. Any of the UAV applications that operate autonomously requires some sense to understand the surroundings. Through the effective implementation of the fuzzy fused hierarchical deep neural networks, the UAV can perceive their environment, map areas, track objects, and offer analytical feedback in real-time. Fuzzy hierarchical neural networks driven UAV systems can empower real-time data and analytics ranging from aerial mapping and modelling to tracking and analytics. It mainly assists the UAV systems in automating the tasks without human interventions based on the data and analytics.
To sum up, it is undeniable that fuzzy fused hierarchical neural networks have huge potential for UAV based systems, more specifically for autonomous systems. Exploring more advanced research in this field will enable more efficient, robust, and precise decision making in UAV applications. However, it has potential risks, such as the need for new innovative regulatory frameworks and security compliance. In this context, this special issue aims to bring out the advances in fuzzy fused hierarchical deep neural networks for UAV applications. Potential topics include, but are not limited to:
**Should be a industry or public sector professional, or academic with a research interest in one of the topics listed on the web page.
- Advances in fuzzy-fused hierarchical neural networks for UAV applications
- Fuzzy logic to deal with uncertain conditions in UAV
- Wireless vision assisted fuzzy controllers for object detection in UAV systems
- Computer vision assisted fuzzy controller approaches for UAV
- Synergy of artificial neural networks and fuzzy systems for UAV
- Adaptive neuro fuzzy systems and applications for UAV
- Unlocking the potential of aerial data with fuzzy-fused hierarchical deep neural networks
- Aerial data collection and analysis with fuzzy-fused hierarchical deep neural networks for efficient decision making
- Fuzzy assisted drones in security and surveillance
- Fuzzy deep neural networks based decision making for UAV applications
For more information, please contact the Guest Editors:
Dr. A. Shanthini
SRM Institute of Science and Technology, India
Dr. Gunasekaran Manogaran
Howard University, Washington D.C., USA
Dr. Priyan Malarvizhi Kumar
Kyung Hee University, South Korea
Deadline to submit manuscripts for consideration: January 31, 2023
Please submit your article at https://www.editorialmanager.com/saeconnautomveh and include a submission note in Editorial Manager to indicate that it is for this special issue.