Applications of machine learning in UAV networks:
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, th...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey PA, USA
IGI Global
[2024]
|
| Schriftenreihe: | Advances in Computational Intelligence and Robotics (ACIR) Book Series
|
| Schlagworte: | |
| Online-Zugang: | DE-91 DE-898 DE-1050 Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 Online-Ressource (xix, 406 Seiten) Illustrationen |
| ISBN: | 9798369305805 |
| DOI: | 10.4018/979-8-3693-0578-2 |
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| discipline | Verkehr / Transport |
| discipline_str_mv | Verkehr / Transport |
| doi_str_mv | 10.4018/979-8-3693-0578-2 |
| format | Electronic eBook |
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| id | DE-604.BV049587058 |
| illustrated | Illustrated |
| index_date | 2024-07-03T23:32:49Z |
| indexdate | 2024-12-17T19:01:33Z |
| institution | BVB |
| isbn | 9798369305805 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034931832 |
| oclc_num | 1424570483 |
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| physical | 1 Online-Ressource (xix, 406 Seiten) Illustrationen |
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| publisher | IGI Global |
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| series2 | Advances in Computational Intelligence and Robotics (ACIR) Book Series |
| spelling | Applications of machine learning in UAV networks Jahan Hassan, Saeed Alsamhi, editors Applications of machine learning in unmanned aerial vehicles networks Hershey PA, USA IGI Global [2024] © 2024 1 Online-Ressource (xix, 406 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Advances in Computational Intelligence and Robotics (ACIR) Book Series 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. Drone aircraft in remote sensing Drone aircraft Control systems Machine learning Hassan, Jahan edt Alsamhi, Saeed edt Erscheint auch als Druck-Ausgabe, Hardcover 979-8-3693-0578-2 Erscheint auch als Druck-Ausgabe, Paperback 979-8-3693-0579-9 https://doi.org/10.4018/979-8-3693-0578-2 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Applications of machine learning in UAV networks Drone aircraft in remote sensing Drone aircraft Control systems Machine learning |
| title | Applications of machine learning in UAV networks |
| title_alt | Applications of machine learning in unmanned aerial vehicles networks |
| title_auth | Applications of machine learning in UAV networks |
| title_exact_search | Applications of machine learning in UAV networks |
| title_exact_search_txtP | Applications of machine learning in UAV networks |
| title_full | Applications of machine learning in UAV networks Jahan Hassan, Saeed Alsamhi, editors |
| title_fullStr | Applications of machine learning in UAV networks Jahan Hassan, Saeed Alsamhi, editors |
| title_full_unstemmed | Applications of machine learning in UAV networks Jahan Hassan, Saeed Alsamhi, editors |
| title_short | Applications of machine learning in UAV networks |
| title_sort | applications of machine learning in uav networks |
| topic | Drone aircraft in remote sensing Drone aircraft Control systems Machine learning |
| topic_facet | Drone aircraft in remote sensing Drone aircraft Control systems Machine learning |
| url | https://doi.org/10.4018/979-8-3693-0578-2 |
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