A CNN-based mosquito classification using image transformation of wingbeat features

Jose Alvaro Luna-Gonzalez, Daniel Robles-Camarillo, Mariko Nakano-Miyatake, Humberto Lanz-Mendoza, Hector Perez-Meana

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

In this paper, a classification of mosquito's specie is performed using mosquito wingbeats samples obtained by optical sensor. Six world-wide representative species of mosquitos, which are Aedes aegypti, Aedes albopictus, Anopheles arabiensis, Anopheles gambiae and Culex pipiens, Culex quinquefasciatus, are considered for classification. A total of 60, 000 samples are divided equally in each specie mentioned above. In total, 25 audio feature extraction algorithms are applied to extract 39 feature values per sample. Further, each audio feature is transformed to a color image, which shows audio features presenting by different pixel values. We used a fully connected neural networks for audio features and a convolutional neural network (CNN) for image dataset generated from audio features. The CNN-based classifier shows 90.75% accuracy, which outperforms the accuracy of 87.18% obtained by the first classifier using directly audio features.

Idioma originalInglés
Título de la publicación alojadaKnowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 19th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2020
EditoresHamido Fujita, Ali Selamat, Sigeru Omatu
EditorialIOS Press BV
Páginas127-137
Número de páginas11
ISBN (versión digital)9781643681146
DOI
EstadoPublicada - 15 sep. 2020
Evento19th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2020 - Virtual, Online, Japón
Duración: 22 sep. 202024 sep. 2020

Serie de la publicación

NombreFrontiers in Artificial Intelligence and Applications
Volumen327
ISSN (versión impresa)0922-6389
ISSN (versión digital)1879-8314

Conferencia

Conferencia19th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2020
País/TerritorioJapón
CiudadVirtual, Online
Período22/09/2024/09/20

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