[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117200-en":3,"doc-seo-117200-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117200,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Monitoring of Wildlife Using Unmanned Aerial Vehicle (UAV) with Machine Learning - Abstract","Wildlife monitoring is critical for ecological study, conservation, and wildlife management, but traditional approaches have drawbacks. The combination of unmanned aerial vehicles (UAVs) with machine learning (ML) offers a viable approach to overcoming the limits of traditional wildlife monitoring methods and improving wildlife management and conservation tactics. UAVs with high-resolution cameras capture airborne imagery, while ML automates animal detection, tracking, and behavior analysis. The chapter reviews challenges, limitations, and future directions, considering regulatory, technical, and ethical constraints, and emphasizes continued research and technological advancement.","Documents  \nAhmed, Z. E.a b , Hashim, A. H.A. b , Saeed, R.A.c , Saeed, M. M.d  \nMonitoring of wildlife using unmanned aerial vehicle (UAV) with machine learning  \n(2024) Applications of Machine Learning in UAV Networks , pp. 97-120.  \nDOI: [10.4018/979-8-3693-0578-2.ch005](10.4018/979-8-3693-0578-2.ch005)  \na Department of Computer Engineering, University of Gezira, Sudan  \nb Department of Electrical and Computer Engineering, International Islamic University Malaysia, Malaysia c Department of Computer Engineering, College of Computers and Information Technology, Taif University, Saudi Arabia d Department of Communications and Electronics Engineering, Faculty of Engineering, University of Modern Sciences (UMS), Yemen  \nAbstract  \nWildlife monitoring is critical for ecological study, conservation, and wildlife management, but traditional approaches have drawbacks. The combination of unmanned aerial vehicles (UAVs) with machine learning (ML) offers a viable approach to overcoming the limits of traditional wildlife monitoring methods and improving wildlife management and conservation tactics. The combination of UAVs and ML provides efficient and effective solutions for wildlife monitoring. UAVs with high-resolution cameras record airborne footage, while machine learning algorithms automate animal detection, tracking, and behavior analysis. The chapter discusses challenges, limitations, and future directions in using UAVs and ML for wildlife monitoring, addressing regulatory, technical, and ethical considerations, and emphasizing the need for ongoing research and technological advancements. Overall, the integration of UAVs and ML provides a promising solution to overcome the limitations of traditional wildlife monitoring methods and enhance wildlife management and conservation strategies. © 2024, IGI Global. All rights reserved.  \nReferences  \nAbang Abdurahman, A.Z. , Wan Yaacob, W.F. , Md Nasir, S.A. , Jaya, S. , Mokhtar, S.  \nUsing Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia  \n(2022) Sustainability (Basel) , 14 (5), p. 2735.  \n Abbas, A. , Rania, A.  \n(2021) Quality of Services Based on Intelligent IoT WLAN MAC Protocol Dynamic RealTime Applications in Smart Cities ,  \nComputational Intelligence and Neuroscience. Hindawi  \nAbdulameer, M.H. , Ahmed, H.W., Ahmed, I.S.  \nBird Image Dataset Classification using Deep Convolutional Neural Network Algorithm  \n(2022) 2022 International Conference on Data Science and Intelligent Computing, ICDSIC, pp. 81-86.  \n2022, IEEE  \n Ahmed, E. , Rashid, A. , Sheetal, N. , Amitava, M.  \n(2020) Energy Optimization in LPWANs by using Heuristic Techniques, LPWAN Technologies for IoT and M2M Applications. Elsevier  \n Ahmed, F. , Mohanta, J.C. , Keshari, A. , Yadav, P.S.  \nRecent Advances in Unmanned Aerial Vehicles: AReview  \n(2022) Arabian Journalfor Scienceand Engineering , 47 (7), pp. 7963-7984. PMID:35492958  \n Alaa, M.  \n(2022) Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load,  \nWireless Communications and Mobile Computing. IEEE  \n Alatabani, L.E. , Ali, E.S.  \nDeep Learning Approaches for IoV Applications and Services  \n(2021) Intelligent Technologies for Internet of Vehicles. Internet of Things (Technology, Communications, and Computing) ,  \nN. Magaia, G. Mastorakis, C. Mavromoustakis, E. Pallis, & E. K. Markakis (Eds. ), Springer  Ali, E.S. , Hassan, M.B.  \nMachine Learning Technologies on Internet of Vehicles  \n(2021) Intelligent Technologies for Internet of Vehicles. Internet of Things (Technology, Communications, and Computing) ,  \nN. Magaia, G. Mastorakis, C. Mavromoustakis, E. Pallis, & E. K. Markakis (Eds. ), Springer  Ali, R.A. , Ali, E.S. , Mokhtar, R.A.  \nBlockchain for IoT-Based Cyber-Physical Systems (CPS): Applications and Challenges  \n(2022) Blockchainbased Internet of Things. Lecture Notes on Data Engineering and Communications Technologies, 112.  \nD. De, S. Bhattacharyya, & J. J. P. ","cbCaieEKTFhjzIAf","https://ap.wps.com/l/cbCaieEKTFhjzIAf","pdf",297029,1,10,"English","en",105,"# Abstract\n## UAVs and machine learning for wildlife monitoring\n## Key challenges, limitations, and future directions\n## Regulatory, technical, and ethical considerations","[{\"question\":\"How do UAVs and machine learning work together for wildlife monitoring?\",\"answer\":\"UAVs equipped with high-resolution cameras capture aerial footage, while machine learning algorithms automate animal detection, tracking, and behavior analysis.\"},{\"question\":\"What drawbacks do traditional wildlife monitoring methods have?\",\"answer\":\"Traditional approaches have limitations that affect ecological study, conservation, and wildlife management, motivating alternative solutions combining UAVs and ML.\"},{\"question\":\"Which considerations does the chapter highlight for implementing UAV+ML monitoring?\",\"answer\":\"It discusses challenges and limitations, including regulatory, technical, and ethical considerations, and outlines future research directions.\"}]","Monitoring of Wildlife Using Unmanned Aerial Vehicle (UAV) with Machine Learning - 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