Unmanned Aerial Vehicles and Multidisciplinary Applications Using AI Techniques eBook

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Unmanned aerial vehicles (UAVs) and artificial intelligence (AI) are gaining the attention of academic and industrial researchers due to the freedoms that UAVs afford when operating and monitoring activities remotely. Applying machine learning and deep learning techniques can result in fast and reliable outputs and have helped in real-time monitoring, data collection and processing, and prediction. UAVs utilizing these techniques can become instrumental tools for computer/wireless networks, smart cities, military applications, agricultural sectors, and mining. Unmanned Aerial Vehicles and Multidisciplinary Applications Using AI Techniques is an essential reference source that covers pattern recognition, machine and deep learning-based methods, and other AI techniques and the impact they have when applied to different real-time applications of UAVs. It synthesizes the scope and importance of machine learning and deep learning models in enhancing UAV capabilities, solutions to problems, and numerous application areas. Covering topics such as vehicular surveillance systems, yield prediction, and human activity recognition, this premier reference source is a comprehensive resource for computer scientists; AI engineers; data scientists; agriculturalists; government officials; military leaders; business managers and leaders; students and faculty of higher education; academic libraries; academicians; and researchers in computer science, computer vision, pattern recognition, imaging, and engineering.

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Additional ISBNs
9781799887652

Unmanned Aerial Vehicles and Multidisciplinary Applications Using AI Techniques is written by Thusnavis Bella and published by Engineering Science Reference. The Digital and eTextbook ISBNs for Unmanned Aerial Vehicles and Multidisciplinary Applications Using AI Techniques are 9781799887669, 1799887669 and the print ISBNs are 9781799887638, 1799887634. Additional ISBNs for this eTextbook include 9781799887652.