Reptilerecon: Um Arcabouço para Extração e Análise de Sinais de Lagartos
Resumo
A comunicação animal é essencial para a sobrevivência, e os pesquisadores têm se dedicado ao estudo dos padrões de sinais emitidos por lagartos, com destaque para a comunicação visual por meio dos movimentos de cabeceios (headbobs). Nesse contexto, este trabalho propõe o uso de técnicas de aprendizado de máquina para identificar e analisar padrões de comunicação em lagartos do gênero Tropidurus. A metodologia consiste em extrair os sinais dos vídeos por meio de algoritmos de aprendizado profundo e, em seguida, aplicar algoritmos não supervisionados para identificar padrões nos sinais extraídos. Os resultados obtidos demonstram a presença de padrões claros nos sinais analisados.
Palavras-chave:
Comunicação animal, aprendizado de máquina, aprendizado profundo, agrupamento de dados
Referências
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Goodfellow, I. J., Bengio, Y., and Courville, A. C. (2016). Deep learning. Adaptive computation and machine learning. MIT Press.
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Jain, A. K. and Dubes, R. C. (1988). Algorithms for clustering data. Prentice-Hall.
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Kaplan, G. (2014). Animal communication. Wiley Interdisciplinary Reviews: Cognitive Science, 5(6):661–677.
Martins, E. P. (1991). Individual and sex differences in the use of the push-up display by the sagebrush lizard, Sceloporus graciosus. Animal Behaviour, 41(3):403–416.
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Martins, E. P. (1994). Structural complexity in a lizard communication system: the Sceloporus graciosus"push-up"display. Copeia, pages 944–955.
McInnes, L., Healy, J., and Astels, S. (2017). hdbscan: Hierarchical density based clustering. J. Open Source Softw., 2(11):205.
Montgomery, D. C., Jennings, C. L., and Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
Moulavi, D., Jaskowiak, P. A., Campello, R. J. G. B., Zimek, A., and Sander, J. (2014). Density-based clustering validation. In Proceedings of the 2014 SIAM International Conference on Data Mining (SDM), pages 839–847.
Nelson, C. M. and Ord, T. J. (2022). Identifying potential cues of species identity in complex animal signals. Animal Behaviour, 186:121–136.
Ord, T. J. and Martins, E. P. (2006). Tracing the origins of signal diversity in anole lizards: phylogenetic approaches to inferring the evolution of complex behaviour. Animal Behaviour, 71(6):1411–1429.
Passos, D. C. (2016). Área de vida, organização social e comunicação visual de Tropidurus do grupo semitaeniatus (Squamata: Tropiduridae). Tese de doutorado, Universidade do Estado do Rio de Janeiro.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65.
Silva, A. O. (2018). Framework para extração de sinais na comunicação visual de lagartos. Trabalho de conclusão de curso, Universidade Federal de Ouro Preto.
Spong, M., Hutchinson, S., and Vidyasagar, M. (2005). Robot modeling and control. Wiley select coursepack. Wiley.
Van der Maaten, L. and Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(11).
Bochkovskiy, A. (2021). Yolo v4, v3 and v2 for Windows and Linux.
Briechle, K. and Hanebeck, U. D. (2001). Template matching using fast normalized cross correlation. In Optical pattern recognition XII, volume 4387, pages 95–102.
Campello, R. J. G. B., Moulavi, D., Zimek, A., and Sander, J. (2013). A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. Data Mining and Knowledge Discovery, 27(3):344–371.
Carpenter, C. C. and Ferguson, G. W. (1977). Variation and evolution of stereotyped behavior in reptiles. Biology of the Reptilia, 7:335–554.
Chiarot, G. and Silvestri, C. (2023). Time series compression survey. ACM Computing Surveys, 55(10):1–32.
Das, G., Lin, K.-I., Mannila, H., Renganathan, G., and Smyth, P. (1998). Rule discovery from time series. In KDD, volume 98, pages 16–22.
Fu, T.-c. (2011). A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1):164–181.
Gertrudes, J. C., Zimek, A., Sander, J., and Campello, R. J. G. B. (2019). A unified view of density-based methods for semi-supervised clustering and classification. Data Mining and Knowledge Discovery, 33(6):1894–1952.
Goodfellow, I. J., Bengio, Y., and Courville, A. C. (2016). Deep learning. Adaptive computation and machine learning. MIT Press.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8):1735–1780.
Jain, A. K. and Dubes, R. C. (1988). Algorithms for clustering data. Prentice-Hall.
Jaskowiak, P. A., Costa, I. G., and Campello, R. J. G. B. (2022). The area under the ROC curve as a measure of clustering quality. Data Mining and Knowledge Discovery, 36(3):1219–1245.
Kaplan, G. (2014). Animal communication. Wiley Interdisciplinary Reviews: Cognitive Science, 5(6):661–677.
Martins, E. P. (1991). Individual and sex differences in the use of the push-up display by the sagebrush lizard, Sceloporus graciosus. Animal Behaviour, 41(3):403–416.
Martins, E. P. (1993). Contextual use of the push-up display by the sagebrush lizard, Sceloporus graciosus. Animal Behaviour, 45(1):25–36.
Martins, E. P. (1994). Structural complexity in a lizard communication system: the Sceloporus graciosus"push-up"display. Copeia, pages 944–955.
McInnes, L., Healy, J., and Astels, S. (2017). hdbscan: Hierarchical density based clustering. J. Open Source Softw., 2(11):205.
Montgomery, D. C., Jennings, C. L., and Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
Moulavi, D., Jaskowiak, P. A., Campello, R. J. G. B., Zimek, A., and Sander, J. (2014). Density-based clustering validation. In Proceedings of the 2014 SIAM International Conference on Data Mining (SDM), pages 839–847.
Nelson, C. M. and Ord, T. J. (2022). Identifying potential cues of species identity in complex animal signals. Animal Behaviour, 186:121–136.
Ord, T. J. and Martins, E. P. (2006). Tracing the origins of signal diversity in anole lizards: phylogenetic approaches to inferring the evolution of complex behaviour. Animal Behaviour, 71(6):1411–1429.
Passos, D. C. (2016). Área de vida, organização social e comunicação visual de Tropidurus do grupo semitaeniatus (Squamata: Tropiduridae). Tese de doutorado, Universidade do Estado do Rio de Janeiro.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65.
Silva, A. O. (2018). Framework para extração de sinais na comunicação visual de lagartos. Trabalho de conclusão de curso, Universidade Federal de Ouro Preto.
Spong, M., Hutchinson, S., and Vidyasagar, M. (2005). Robot modeling and control. Wiley select coursepack. Wiley.
Van der Maaten, L. and Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(11).
Publicado
25/09/2023
Como Citar
FERNANDES ZENÓBIO, João Gabriel; LOPES SILVA, Pedro Henrique; DA SILVA LUZ, Eduardo José; MOREIRA, Gladston Juliano Prates; GALDINO, Conrado Aleksander Barbosa; CASTRO GERTRUDES, Jadson.
Reptilerecon: Um Arcabouço para Extração e Análise de Sinais de Lagartos. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 38. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 154-166.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2023.231703.
