Mitigating Wild Animals Poaching Through State-of-the-art Multimedia Data Mining Techniques: A Review-Reference-Cited by-同舟云学术

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Mitigating Wild Animals Poaching Through State-of-the-art Multimedia Data Mining Techniques: A Review

Published:2020-08-05 Issue: Volume: Page:
ISSN:
Container-title:2020 2nd International Conference on Image Processing and Machine Vision
language:
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Author:

Bakana Sibusiso R.¹,Zhang Yongfei²,Twala Bhekisipho³

Affiliation:

1. School of Computer Science and Engineering: Beihang University, China

2. School of Computer Science and Engineering and Beijing Key Lab of Digital Media: Beihang University, China Beijing

3. Department of Electrical Engineering:Durban University of Technology, South Africa Durban

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3421558.3421584

Reference107 articles.

1. A novel decision tree classification based on post-pruning with Bayes minimum risk

2. Amosov O.S. 2019. Using the ensemble of deep neural networks for normal and abnormal situations detection and recognition in the continuous video stream of the security system. Procedia Computer Science. 150 (2019) 532–539. DOI:https://doi.org/10.1016/j.procs.2019.02.089. Amosov O.S. 2019. Using the ensemble of deep neural networks for normal and abnormal situations detection and recognition in the continuous video stream of the security system. Procedia Computer Science. 150 (2019) 532–539. DOI:https://doi.org/10.1016/j.procs.2019.02.089.

3. Bai Y. 2018. SOD-MTGAN: Small object detection via multi-task generative adversarial network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11217 LNCS i (2018) 210–226. DOI:https://doi.org/10.1007/978-3-030-01261-8_13. Bai Y. 2018. SOD-MTGAN: Small object detection via multi-task generative adversarial network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11217 LNCS i (2018) 210–226. DOI:https://doi.org/10.1007/978-3-030-01261-8_13.

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