TY - GEN
T1 - Infected Mosquito Detection System Using Spectral Analysis
AU - Haro, Marco
AU - Nakano-Miyatake, Mariko
AU - Cime-Castillo, Jorge
AU - Lanz-Mendoza, Humberto
AU - Gonzalez-Lee, Mario
AU - Perez-Meana, Hector
N1 - Publisher Copyright:
© 2022 The authors and IOS Press. All rights reserved.
PY - 2022/9/14
Y1 - 2022/9/14
N2 - Considering that an accurate detection of infected mosquitos may directly avoid the propagation of mosquito-borne disease; in this paper, we propose a detection system of infected mosquitos by Dengue virus type II, that uses seven spectral feature measures, which are applied to the spectrogram estimated from wingbeat signal emitted by mosquito's flight. To evaluate the proposed system, we construct our own dataset with 20 infected Aedes aegypti by Dengue and 20 healthy ones. Seven spectral analysis methods, such as Spectral Rolloff, Spectral Centroide, etc., are applied to the spectrogram obtained by using the Short Time Fourier Transform (STFT) to generate feature vectors with 15 elements. These are feed into common machine learning techniques, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Logistic Regression to detect the infected mosquitos differentiating form the healthy ones. Evaluation results show that, the best detection accuracy (84.32%) is provided by the KNN with K=3.
AB - Considering that an accurate detection of infected mosquitos may directly avoid the propagation of mosquito-borne disease; in this paper, we propose a detection system of infected mosquitos by Dengue virus type II, that uses seven spectral feature measures, which are applied to the spectrogram estimated from wingbeat signal emitted by mosquito's flight. To evaluate the proposed system, we construct our own dataset with 20 infected Aedes aegypti by Dengue and 20 healthy ones. Seven spectral analysis methods, such as Spectral Rolloff, Spectral Centroide, etc., are applied to the spectrogram obtained by using the Short Time Fourier Transform (STFT) to generate feature vectors with 15 elements. These are feed into common machine learning techniques, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Logistic Regression to detect the infected mosquitos differentiating form the healthy ones. Evaluation results show that, the best detection accuracy (84.32%) is provided by the KNN with K=3.
KW - Mosquito's detection
KW - beat sound
KW - dengue fever
KW - infected mosquitos
KW - machine learning
KW - spectral analysis
UR - http://www.scopus.com/inward/record.url?scp=85139741993&partnerID=8YFLogxK
U2 - 10.3233/FAIA220296
DO - 10.3233/FAIA220296
M3 - Contribución a la conferencia
AN - SCOPUS:85139741993
T3 - Frontiers in Artificial Intelligence and Applications
SP - 669
EP - 677
BT - New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022
A2 - Fujita, Hamido
A2 - Watanobe, Yutaka
A2 - Azumi, Takuya
PB - IOS Press BV
T2 - 21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022
Y2 - 20 September 2022 through 22 September 2022
ER -
