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Synopsis
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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About the Authors
Bella Mary I. Thusnavis received her B.E in Electronics and Communication Engineering with distinction from Karunya Institute of Technology, affiliated to Anna University, Chennai, India in 2007 and her M.E in Applied Electronics from Karunya University, India in 2009. She completed her Ph.D degree in Information and Communication Engineering under Anna University, Chennai, India, in 2020. Her research includes Multimedia Image Retrieval for Medical Diagnosis, Statistical Feature Extraction, Optimization algorithm and Embedded Systems. She is currently working as Assistant Professor in Electronics and Communication Engineering, Karunya University, Coimbatore, India. She has 11 international Journal Publications and 19 International and National Conference Publications.
Dr. Martin Sagayam received his PhD in Electronics and Communication Engineering (Signal image processing using machine learning algorithms) from Karunya University, Coimbatore, India. He received his both ME in Communication Systems and BE in Electronics and Communication Engineering from Anna University, Chennai. Currently, he is working as Assistant Professor in the Department of ECE, Karunya Institute Technology and Sciences, Coimbatore, India. He has authored/ co-authored more number of referred International Journals. He has also presented more number of papers in reputed international and national conferences. He has authored 2 edited book, 2 authored book, book series and more than 15 book chapters with reputed international publishers. He has three Indian patents and two Australian patents for his innovations and intellectual property right. He is an active IEEE member. His area of interest includes Communication systems, signal and image processing, machine learning and virtual reality.
Ahmed A. Elngar currently is an Assistant Professor at Faculty of Computers and Artificial Intelligence, Department of Computer Science, Beni Suef University. Their current project is 'Biometrics, AI-based Security, Image processing, AI-based Control, AI-based Smart Grids'.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9781799887645
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