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Deep Learning Applications in Wildlife Recognition.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning Applications in Wildlife Recognition./
Author:	Miao, Zhongqi.
Description:	1 online resource (110 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
Contained By:	Dissertations Abstracts International84-05B.
Subject:	Ecology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28971382click for full text (PQDT)
ISBN:	9798351475639

Deep Learning Applications in Wildlife Recognition.
Miao, Zhongqi.

Deep Learning Applications in Wildlife Recognition. - 1 online resource (110 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2022.

Includes bibliographical references

Deep learning has attracted much attention from the ecological community for its capability of extracting and generalizing patterns from data sets with highly complicated structures, such as images, audios, and motion signals. However, despite the promising cases, deep learning is complicated in terms of application and has shortcomings when applied to real-world ecological data sets. In this dissertation, we focus on: 1) demystifying the hidden mechanisms of deep learning in terms of wildlife recognition, 2) identifying the challenges of deep learning applications in wildlife recognition, and 3) proposing a generic recognition framework that can be practically deployed in the fields.In the first chapter, we examine how deep learning recognizes wildlife through Convolutional Neural Network feature deconstruction and interpretation. The objective is to demystify aspects of artificial intelligence and facilitate wildlife recognition research.The second chapter identifies three major challenges to automatic wildlife recognition through an avian recognition case study and provides preliminary solutions addressing each challenge. This chapter aims to increase awareness in the ecological community of these challenges, bridge the gap between ecological applications and state-of-the-art computer science, and open doors to future research.In the third chapter, we propose a hybrid recognition system of machine learning and human-in-the-loop that overcomes two challenges discussed in the second chapter: imbalanced data distribution and continuous data expansion. Moreover, with the self-updating mechanism of our approach, the system can be practically deployed in the fields.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798351475639Subjects--Topical Terms:

516476
Ecology.
Subjects--Index Terms:

Computer visionIndex Terms--Genre/Form:

542853
Electronic books.

Deep Learning Applications in Wildlife Recognition.
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500 $a Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
500 $a Advisor: Getz, Wayne M.; Yu, Stella X.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2022.
504 $a Includes bibliographical references
520 $a Deep learning has attracted much attention from the ecological community for its capability of extracting and generalizing patterns from data sets with highly complicated structures, such as images, audios, and motion signals. However, despite the promising cases, deep learning is complicated in terms of application and has shortcomings when applied to real-world ecological data sets. In this dissertation, we focus on: 1) demystifying the hidden mechanisms of deep learning in terms of wildlife recognition, 2) identifying the challenges of deep learning applications in wildlife recognition, and 3) proposing a generic recognition framework that can be practically deployed in the fields.In the first chapter, we examine how deep learning recognizes wildlife through Convolutional Neural Network feature deconstruction and interpretation. The objective is to demystify aspects of artificial intelligence and facilitate wildlife recognition research.The second chapter identifies three major challenges to automatic wildlife recognition through an avian recognition case study and provides preliminary solutions addressing each challenge. This chapter aims to increase awareness in the ecological community of these challenges, bridge the gap between ecological applications and state-of-the-art computer science, and open doors to future research.In the third chapter, we propose a hybrid recognition system of machine learning and human-in-the-loop that overcomes two challenges discussed in the second chapter: imbalanced data distribution and continuous data expansion. Moreover, with the self-updating mechanism of our approach, the system can be practically deployed in the fields.
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538 $a Mode of access: World Wide Web
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650 4 $a Computer science. $3 523869
650 4 $a Geographic information science. $3 3432445
650 4 $a Wildlife management. $3 571816
650 4 $a Wildlife conservation. $2 fast $3 542165
653 $a Computer vision
653 $a Deep learning
653 $a Wildlife recognition
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