資料來源:
三民書局
Applications of machine learning in UAV networks / Jahan Hassan, Saeed Alsamhi.
- 其他作者：
- 其他題名：
- Advances in computational intelligence and robotics (ACIR) book series
- ACIR book series
- 出版： Hershey, PA : Engineering Science Reference (an imprint of IGI Global) ©2024
- 叢書名： Advances in computational intelligence and robotics (ACIR) book series
- 主題： Drone aircraft , Industrial applications. , Drone aircraft in remote sensing , Data processing. , Machine learning. , Drone aircraft--Industrial applications. , Drone aircraft in remote sensing--Data processing. , Machine learning.
- ISBN： 9798369305782 (hardback): NT$7398 、 9798369305799 (paperback)
- 書目註：Includes bibliographical references and index.
-
讀者標籤：
- 系統號： 005730402 | 機讀編目格式
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Applications of Machine Learning in UAV Networks presents a pioneering exploration into the symbiotic relationship between machine learning techniques and UAVs. In an age where UAVs are revolutionizing sectors as diverse as agriculture, environmental preservation, security, and disaster response, this meticulously crafted volume offers an analysis of the manifold ways machine learning drives advancements in UAV network efficiency and efficacy. This book navigates through an expansive array of domains, each demarcating a pivotal application of machine learning in UAV networks. From the precision realm of agriculture and its dynamic role in yield prediction to the ecological sensitivity of biodiversity monitoring and habitat restoration, the contours of each domain are vividly etched. These explorations are not limited to the terrestrial sphere; rather, they extend to the pivotal aerial missions of wildlife conservation, forest fire monitoring, and security enhancement, where UAVs adorned with machine learning algorithms wield an instrumental role. Scholars and practitioners from fields as diverse as machine learning, UAV technology, robotics, and IoT networks will find themselves immersed in a confluence of interdisciplinary expertise. The book's pages cater equally to professionals entrenched in agriculture, environmental studies, disaster management, and beyond.
摘要註
"Unmanned aerial vehicles (UAVs) continue to become more advanced and complex as researchers push the boundaries of other supporting technologies. Applications of Machine Learning in UAV Networks presents a pioneering exploration into the symbiotic relationship between machine learning techniques and UAVs. In an age where UAVs are revolutionizing sectors as diverse as agriculture, environmental preservation, security, and disaster response, this meticulously crafted volume offers an analysis of the manifold ways machine learning drives advancements in UAV network efficiency and efficacy. This book navigates through an expansive array of domains, each demarcating a pivotal application of machine learning in UAV networks. From the precision realm of agriculture and its dynamic role in yield prediction to the ecological sensitivity of biodiversity monitoring and habitat restoration, the contours of each domain are vividly etched. These explorations are not limited to the terrestrial sphere; rather, they extend to the pivotal aerial missions of wildlife conservation, forest fire monitoring, and security enhancement, where UAVs adorned with machine learning algorithms wield an instrumental role. Scholars and practitioners from fields as diverse as machine learning, UAV technology, robotics, and IoT networks will find themselves immersed in a confluence of interdisciplinary expertise. The book's pages cater equally to professionals entrenched in agriculture, environmental studies, disaster management, and beyond. Furthermore, the students and researchers finds knowledge that illuminates the convergence of UAVs and machine learning, arguably one of the most riveting frontiers in contemporary research"--