Detecting Aedes aegypti mosquitoes through audio classification with convolutional neural networks
Open accessAbstract
The incidence of mosquito-borne diseases is significant in under-developed regions, mostly due to the lack of resources to implement aggressive control measurements against mosquito proliferation. A potential strategy to raise community awareness regarding mosquito proliferation is building a live map of mosquito incidences using smartphone apps and crowdsourcing. In this paper, we explore the possibility of identifying Aedes aegypti mosquitoes using machine learning techniques and audio analysis captured from commercially available smartphones. In summary, we downsampled Aedes aegypti wingbeat recordings and used them to train a convolutional neural network (CNN) through supervised learning. As a feature, we used the recording spectrogram to represent the mosquito wingbeat frequency over time visually. We trained and compared three classifiers: a binary, a multiclass, and an ensemble of binary classifiers. In our evaluation, the binary and ensemble models achieved accuracy of 97.65% (±0.55) and 94.56% (±0.77), respectively, whereas the multiclass had an accuracy of 78.12% (±2.09). The best sensitivity was observed in the ensemble approach (96.82% ± 1.62), followed by the multiclass for the particular case of Aedes aegypti (90.23% ± 3.83) and the binary (88.49% ± 6.68). The binary classifier and the multiclass classifier presented the best balance between precision and recall, with F1-measure close to 90%. Although the ensemble classifier achieved the lowest precision, thus impairing its F1-measure (79.95% ± 2.13), it was the most powerful classifier to detect Aedes aegypti in our dataset.
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[106] achieved an accuracy of 69% to 73.08% for emotion recognition.
“After deploying the data collect by sensors on trained ML models, [107] obtained an accuracy that ranges from 53.6% to 69.9%, in contrast, [106] achieved an accuracy of 69% to 73.08%”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [106]
Table 5 in the Final considerations reports accuracy for classifying mosquitoes by wingbeat sound, not for emotion recognition. Its reported accuracies are 97.65% for the binary model, 78.12% for the multiclass model, and 94.56% for the ensemble—not 69% to 73.08%. (LLM, conf=0.90)
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503 · see citing works
Environmental sound classification with convolutional neural networks
Piczak, K. J. (2015) · Machine learning for signal processing ... · doi:10.1109/mlsp.2015.7324337 · Cited by 971 · see citing works
Deep Learning: A Practitioner's Approach
Patterson, J., & Adam, G. (2017) · Cited by 486 · see citing works
Insecticide resistance in the major dengue vectors Aedes albopictus and Aedes aegypti
VONTAS, J. G., Kioulos, E., Pavlidi, N., Morou, E., della Torre, A., & Ranson, H. (2012) · Pesticide Biochemistry and Physiology · doi:10.1016/j.pestbp.2012.05.008 · Cited by 444 · see citing works
Tolle, M. A. (2009) · Current problems in pediatric and adolescent health care · doi:10.1016/j.cppeds.2009.01.001 · Cited by 429 · see citing works
End-to-end learning for music audio
Dieleman, S., & Schrauwen, B. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854950 · Cited by 377 · see citing works
Taking connected mobile-health diagnostics of infectious diseases to the field
Wood, C. S., Thomas, M. R., Budd, J., Mashamba-Thompson, T. P., Herbst, K., Pillay, D., Peeling, R. W. W., Johnson, A. M., McKendry, R. A., & Stevens, M. M. (2019) · Nature · doi:10.1038/s41586-019-0956-2 · Cited by 325 · see citing works
Automated classification of bird and amphibian calls using machine learning: A comparison of methods
Acevedo, M. A., Corrada-Bravo, C. J., Corrada-Bravo, H., Villanueva-Rivera, L. J., & Aide, T. M. (2009) · Ecological Informatics · doi:10.1016/j.ecoinf.2009.06.005 · Cited by 317 · see citing works
Emergency department management of mosquito-borne illness: malaria, dengue, and West Nile virus.
Caraballo, H., & King, K. (2014) · PubMed · Cited by 291 · see citing works
Harmonic Convergence in the Love Songs of the Dengue Vector Mosquito
Cator, L. J., Arthur, B. J., Harrington, L. C., & Hoy, R. R. (2009) · Science · doi:10.1126/science.1166541 · Cited by 286 · see citing works
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
(2021) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp39728.2021 · Cited by 257 · see citing works
2016 24th European Signal Processing Conference (EUSIPCO)
(2016) · doi:10.1109/eusipco37884.2016 · Cited by 251 · see citing works
Exploiting the potential of vector control for disease prevention.
Townson, H., Nathan, M. B., Zaim, M., Guillet, P., Manga, L., Bos, R., & Kindhauser, M. K. (2005) · Europe PMC (PubMed Central) · Cited by 187 · see citing works
Flying Insect Classification with Inexpensive Sensors
Chen, Y., Why, A., Batista, G., Mafra‐Neto, A., & Keogh, E. (2014) · Journal of Insect Behavior · doi:10.1007/s10905-014-9454-4 · Cited by 152 · see citing works
Huzaifah, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.07156 · Cited by 148 · see citing works
2017 25th European Signal Processing Conference (EUSIPCO)
(2017) · doi:10.23919/eusipco40094.2017 · Cited by 129 · see citing works
Using mobile phones as acoustic sensors for high-throughput mosquito surveillance
Mukundarajan, H., Hol, F. J. H., Castillo, E. A., Newby, C., & Prakash, M. (2017) · eLife · doi:10.7554/elife.27854 · Cited by 127 · see citing works
Application of convolutional neural networks for classification of adult mosquitoes in the field
Motta, D., Santos, A. Á. B., Winkler, I., Machado, B. A. S., Pereira, D. A. D. I., Cavalcanti, A. M., Fonseca, E. O. L., Kirchner, F., & Badaró, R. J. D. S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210829 · Cited by 124 · see citing works
Convolutional recurrent neural networks for bird audio detection
Çakır, E., Adavanne, S., Parascandolo, G., Drossos, K., & Virtanen, T. I. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081508 · Cited by 114 · see citing works
Temporal Pooling And Multiscale Learning For Automatic Annotation And Ranking Of Music Audio.
Hamel, P., Lemieux, S., Bengio, Y., & Douglas Eck, C. (2011) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1418236 · Cited by 96 · see citing works
Arthur, B. J., Emr, K. S., Wyttenbach, R. A., & Hoy, R. R. (2014) · The Journal of the Acoustical Society of America · doi:10.1121/1.4861233 · Cited by 95 · see citing works
Exploring Low Cost Laser Sensors to Identify Flying Insect Species
Silva, D. F., Souza, V. M. A., Ellis, D. P. W., Keogh, E., & Batista, G. (2015) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-014-0168-9 · Cited by 93 · see citing works
Villarreal, S. M., Winokur, O. C., & Harrington, L. C. (2017) · Journal of Medical Entomology · doi:10.1093/jme/tjx079 · Cited by 89 · see citing works
Bioacoustic detection with wavelet-conditioned convolutional neural networks
Kiskin, I., Zilli, D., Li, Y., Sinka, M. E., Willis, K. J., & Roberts, S. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3626-7 · Cited by 86 · see citing works
Rund, S. S. C., Gentile, J. E., & Duffield, G. E. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-218 · Cited by 84 · see citing works
Cator, L. J., Arthur, B. J., Ponlawat, A., & Harrington, L. C. (2011) · Journal of Medical Entomology · doi:10.1603/me11019 · Cited by 71 · see citing works
Potamitis, I., Rigakis, I. I., & Fysarakis, K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140474 · Cited by 69 · see citing works
The Long and Short of Hearing in the Mosquito Aedes aegypti
Menda, G., Nitzany, E. I., Shamble, P. S., Wells, A., Harrington, L. C., Miles, R. N., & Hoy, R. R. (2019) · Current Biology · doi:10.1016/j.cub.2019.01.026 · Cited by 69 · see citing works
Rolim, F. A., Langlois, T. J., Rodrigues, P. F. C., Bond, T., Motta, F. S., Neves, L. M., & Gadig, O. B. F. (2019) · PLoS ONE · doi:10.1371/journal.pone.0204970 · Cited by 68 · see citing works
Effective and Efficient Dropout for Deep Convolutional Neural NetworksPreprint
Cai, S., Shu, Y., Chen, G., Ooi, B. C., Wang, W., & Zhang, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03392 · Cited by 67 · see citing works
Mosquito wingbeat analysis and classification using deep learning
Fanioudakis, E., Geismar, M., & Potamitis, I. (2018) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2018.8553542 · Cited by 65 · see citing works
Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps
Silva, D. F., Souza, V. M. A., Batista, G. E. A. P. A., Keogh, E., & Ellis, D. P. W. (2013) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2013.24 · Cited by 64 · see citing works
Keeping track of mosquitoes: a review of tools to track, record and analyse mosquito flight
Spitzen, J., & Takken, W. (2018) · Parasites & Vectors · doi:10.1186/s13071-018-2735-6 · Cited by 64 · see citing works
Johnson, B. J., & Ritchie, S. A. (2015) · Journal of Medical Entomology · doi:10.1093/jme/tjv165 · Cited by 61 · see citing works
Mosquito vector monitoring system based on optical wingbeat classification
Yang, T.-H. O., Yang, E., Jiang, J., & Lin, T. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.08.021 · Cited by 52 · see citing works
Brogdon, W. G. (1994) · Journal of Medical Entomology · doi:10.1093/jmedent/31.5.700 · Cited by 50 · see citing works
Recording of Sounds Produced by Certain Disease-Carrying Mosquitoes
Kahn, M. C., Celestin, W., & Offenhauser, W. H. (1945) · Science · doi:10.1126/science.101.2622.335 · Cited by 46 · see citing works
Potamitis, I., & Rigakis, I. I. (2016) · Applied Acoustics · doi:10.1016/j.apacoust.2016.03.005 · Cited by 44 · see citing works
LOCOMOBIS: a low-cost acoustic-based sensing system to monitor and classify mosquitoes
Vasconcelos, D., Nunes, N., Ribeiro, M., Prandi, C., & Rogers, A. (2019) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2019.8651767 · Cited by 40 · see citing works
Artificial neural network trained to identify mosquitoes in flight
Moore, A. (1991) · Journal of Insect Behavior · doi:10.1007/bf01048285 · Cited by 31 · see citing works
A Novel Methodology For Recording Wing Beat Frequencies of Untethered Male and Female Aedes aegypti
Staunton, K. M., Usher, L., Prachar, T., Ritchie, S. A., Snoad, N., & Johnson, B. J. (2019) · Journal of the American Mosquito Control Association · doi:10.2987/18-6799.1 · Cited by 29 · see citing works
Balestrino, F., Iyaloo, D. P., Elahee, K. B., Bheecarry, A., Campedelli, F., Carrieri, M., & Bellini, R. (2016) · Acta Tropica · doi:10.1016/j.actatropica.2016.09.002 · Cited by 28 · see citing works
Rohde, B., Staunton, K. M., Zeak, N. C., Beebe, N. W., Snoad, N., Bondarenco, A., Liddington, C., Anderson, J. A., Xiang, W., Mankin, R. W., et al. (2019) · Parasites & Vectors · doi:10.1186/s13071-019-3647-9 · Cited by 28 · see citing works
Li, Z., Zhou, Z., Shen, Z., & Yao, Q. (2006) · Springer eBooks · doi:10.1007/0-387-29295-0_52 · Cited by 24 · see citing works
Staunton, K. M., Crawford, J. E., Liu, J., Townsend, M., Yu, H., Desnoyer, M., Howell, P. I., Xiang, W., Burkot, T. R., Snoad, N., et al. (2020) · Journal of Medical Entomology · doi:10.1093/jme/tjaa151 · Cited by 21 · see citing works
(2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812365-2.00003-2 · Cited by 19 · see citing works
A new approach to improve acoustic trapping effectiveness for Aedes aegypti (Diptera: Culicidae)
Pantoja‐Sánchez, H., Vargas, J. F., Ruíz-López, F., Rúa‐Uribe, G., Vélez, V., Kline, D. L., & Bernal, X. E. (2019) · Journal of Vector Ecology · doi:10.1111/jvec.12352 · Cited by 15 · see citing works
Humbug Zooniverse: A Crowd-Sourced Acoustic Mosquito Dataset
Kiskin, I., Cobb, A. D., Wang, L., & Roberts, S. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053141 · Cited by 10 · see citing works
Regulation, G., & Control, T. (2008) · Springer eBooks · doi:10.1007/978-1-4020-6754-9_8532 · Cited by 8 · see citing works
Evolving Larger Convolutional Layer Kernel Sizes for a Settlement Detection Deep-Learner on Summit
Coletti, M. A., Lunga, D., Bassett, J. K., & Rose, A. (2019) · IEEE/ACM Workshop on Deep Learning on Supercomputers (DLS) · doi:10.1109/dls49591.2019.00010 · Cited by 6 · see citing works
Flagged first, then by citations
1 of the 58 references couldn’t be matched to a known paper.
Cited by
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A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366 · see citing works
“such as [106], where they use CNN to classify audios cap-”
MethodTao, R., Pan, Z., Das, R. K., Qian, X., Shou, M. Z., & Li, H. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475587 · Cited by 157 · see citing works
Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection
Tao, R., Pan, Z., Das, R. K., Qian, X., Shou, M. Z., & Li, H. (2021) · arXiv · doi:10.48550/arxiv.2107.06592 · Cited by 156 · see citing works
A Deep Convolutional Neural Network for Classification of Aedes Albopictus Mosquitoes
Adhane, G. W., Dehshibi, M. M., & Masip, D. (2021) · IEEE Access · doi:10.1109/access.2021.3079700 · Cited by 45 · see citing works
A Survey on Deep Learning Based Forest Environment Sound Classification at the Edge
Meedeniya, D., Ariyarathne, I., Bandara, M., Jayasundara, R., & Perera, C. (2023) · ACM Computing Surveys · doi:10.1145/3618104 · Cited by 43 · see citing works
Khalighifar, A., Jiménez‐García, D., Campbell, L. P., Ahadji‐Dabla, K. M., Aboagye‐Antwi, F., Ibarra‐Juárez, L. A., & Peterson, A. T. (2021) · Journal of Medical Entomology · doi:10.1093/jme/tjab161 · Cited by 32 · see citing works
A deep learning-based pipeline for mosquito detection and classification from wingbeat sounds
Yin, M. S., Haddawy, P., Ziemer, T., Wetjen, F., Supratak, A., Chiamsakul, K., Siritanakorn, W., Chantanalertvilai, T., Sriwichai, P., & Sa-ngamuang, C. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13367-0 · Cited by 31 · see citing works
Aedes Larva Detection Using Ensemble Learning to Prevent Dengue Endemic
Hossain, M. S., Raihan, M. E., Hossain, M. S., Syeed, M. M. M., Rashid, H., & Reza, M. S. (2022) · BioMedInformatics · doi:10.3390/biomedinformatics2030026 · Cited by 30 · see citing works
A ResNet attention model for classifying mosquitoes from wing-beating sounds
Wei, X., Hossain, M. Z., & Ahmed, K. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-14372-x · Cited by 27 · see citing works
Audio signal based danger detection using signal processing and deep learning
Fime, A. A., Ashikuzzaman, M., & Aziz, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121646 · Cited by 25 · see citing works
Joelianto, E., Mandasari, M. I., Marpaung, D. B., Hafizhan, N. D., Heryono, T., Prasetyo, M. E., Dani, D., Tjahjani, S., Anggraeni, T., & Ahmad, I. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102495 · Cited by 23 · see citing works
Counting Mosquitoes in the Wild
Vasconcelos, D., Yin, M. S., Wetjen, F., Herbst, A. T., Ziemer, T., Förster, A., Barkowsky, T., Nunes, N., & Haddawy, P. (2021) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3462203.3475914 · Cited by 22 · see citing works
Trivedi, K., & Shroff, H. (2021) · 2021 ITU Kaleidoscope: Connecting Physical and Virtual Worlds (ITU K) · doi:10.23919/ituk53220.2021.9662116 · Cited by 21 · see citing works
Herrera, C., Williams, M., Encarnação, J., Roura‐Pascual, N., Faulhaber, B., Jurado‐Rivera, J. A., & Leza, M. (2022) · Pest Management Science · doi:10.1002/ps.7296 · Cited by 19 · see citing works
Multi-label learning with Relief-based label-specific feature selection
Zhang, J., Liu, K., Yang, X., Ju, H., & Xu, S. (2023) · Applied Intelligence · doi:10.1007/s10489-022-04350-1 · Cited by 18 · see citing works
Artificial intelligence (AI): a new window to revamp the vector-borne disease control
Nayak, B., Khuntia, B., Murmu, L. K., Sahu, B., Pandit, R. S., & Barik, T. K. (2022) · Parasitology Research · doi:10.1007/s00436-022-07752-9 · Cited by 16 · see citing works
Automatic synthesis of insects bioacoustics using machine learning: a systematic review
Kyalo, H., Tonnang, H. E. Z., Egonyu, J. P., Olukuru, J., Tanga, C. M., & Senagi, K. (2025) · International Journal of Tropical Insect Science · doi:10.1007/s42690-024-01406-2 · Cited by 16 · see citing works
A Deep Transfer Learning Framework for the Multi-Class Classification of Vector Mosquito Species
Pise, R. N., & Patil, K. R. (2023) · Journal of Ecological Engineering · doi:10.12911/22998993/168501 · Cited by 14 · see citing works
Enhance fashion classification of mosquito vector species via self-supervised vision transformer
Kittichai, V., Kaewthamasorn, M., Chaiphongpachara, T., Laojun, S., Saiwichai, T., Naing, K. M., Tongloy, T., Boonsang, S., & Chuwongin, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-83358-8 · Cited by 13 · see citing works
Machine vision for low-cost remote control of mosquitoes by power laser
Rakhmatulin, I. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01079-x · Cited by 12 · see citing works
Alar, H. S., & Fernandez, P. L. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104973 · Cited by 12 · see citing works
Deep learning in disease vector image identification
Bai, S., Shi, L., & Yang, K. (2024) · Pest Management Science · doi:10.1002/ps.8473 · Cited by 11 · see citing works
Paim, K. O., Rohweder, R., Recamonde‐Mendoza, M., Mansilha, R. B., & Cordeiro, W. L. D. C. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106342 · Cited by 10 · see citing works
Self-citationA low-cost TinyML model for Mosquito Detection in Resource-Constrained Environments
Kimutai, G., & Förster, A. (2023) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3582515.3609514 · Cited by 9 · see citing works
On the Use of Uncertainty in Classifying Aedes Albopictus Mosquitoes
Adhane, G. W., Dehshibi, M. M., & Masip, D. (2021) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2021.3122886 · Cited by 8 · see citing works
Classification System of National Music Rhythm Spectrogram Based on Biological Neural Network
Mi, D., & Qin, L. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2047576 · Cited by 6 · see citing works
Comparison of machine learning algorithms for dengue virus (DENV) classification
Exebio-Chepe, Y. V., Ruíz, J. A. B., & Monteza, V. A. T. (2024) · Journal of Applied Research and Technology · doi:10.22201/icat.24486736e.2024.22.5.2422 · Cited by 5 · see citing works
Lu, A., Yan, Z., Cui, H., & Ma, Q. (2024) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-024-02705-5 · Cited by 4 · see citing works
Vamsi, B., Bataineh, A. A., & Doppala, B. P. (2024) · Traitement du signal · doi:10.18280/ts.410437 · Cited by 4 · see citing works
UAV-based aerial imaging and path optimization to combat mosquito-borne diseases
Bapireddygari, H., & Anu, V. M. (2025) · Pathogens and Global Health · doi:10.1080/20477724.2025.2507681 · Cited by 4 · see citing works
Fengler, B., Seyidbayli, C., Reinhardt, A., Anders, M., & Westphal, C. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.103733 · Cited by 4 · see citing works
Insect Classification Using Mel-CSTFT: A Fusion of Mel Spectrogram and Chroma STFT Features
Raghuwanshi, P., & Kaushik, R. (2024) · International Conference on Electronics, Communication and Signal Processing (ICECSP) · doi:10.1109/icecsp61809.2024.10698728 · Cited by 4 · see citing works
Acoustic Leak Detection in Water Supply Pipelines Based on an Adaptive Multitask Model
Shen, Y., Lin, Q., Wang, H., & Yu, Z. (2025) · Journal of Water Resources Planning and Management · doi:10.1061/jwrmd5.wreng-6899 · Cited by 3 · see citing works
Augustin, J., Zsebők, S., Kovács, D., Jánki, Z., Bánhalmi, A., Soltész, Z., Seffer, P., Bilicki, V., & Garamszegi, L. Z. (2026) · PLOS One · doi:10.1371/journal.pone.0343060 · Cited by 2 · see citing works
The potential of bioacoustics for surveying carrion insects
Gorgeva, E., Robertson, J. A., Voss, S. C., & Hoogewerff, J. A. (2023) · Australian Journal of Forensic Sciences · doi:10.1080/00450618.2023.2295447 · Cited by 2 · see citing works
Hybrid Machine Learning Approach using Wingbeat Frequencies for Mosquito Species Classification
Chaudhari, S. S., Kulkarni, P., Vadivel, K. S., & Latha Kamatchi, S. (2025) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt64827.2025.10968635 · Cited by 2 · see citing works
Emerging application use cases and future directions
Ghorpade, S. N., & Chougule, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-322202-3.00024-5 · Cited by 1 · see citing works
Effect of physiological and environmental factors on mosquito wingbeat frequency
Kim, D., DeBriere, T. J., & Burkett-Cadena, N. D. (2024) · Journal of Vector Ecology · doi:10.52707/1081-1710-49.2.r70 · Cited by 1 · see citing works
Javed, N., López‐Denman, A. J., Paradkar, P. N., & Bhatti, A. (2025) · Advanced Biology · doi:10.1002/adbi.202400847 · Cited by 1 · see citing works
Sicam, A. J. M., & Aguinaldo, B. E. (2025) · International Conference on Advanced Machine Learning and Data Science (AMLDS) · doi:10.1109/amlds63918.2025.11159386 · Cited by 1 · see citing works
Krishnamoorthy, D., Sai, V. G. S., Vishal, R., & Benezeth, Y. (2026) · Scientific Reports · doi:10.1038/s41598-026-55380-5 · Cited by 1 · see citing works
Hasham, A., Hakim, A., Jabeen, J., & Naseem, S. (2022) · Sukkur IBA Journal of Computing and Mathematical Sciences · doi:10.30537/sjcms.v6i1.1061 · Cited by 1 · see citing works
Hybrid Machine Learning Approach for Mosquito Species Classification Using Wingbeat Analysis
Sekhar, N. G., & Kataiah, T. (2024) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2410312 · Cited by 1 · see citing works
Sound-based Mosquito Classification via Featurization and Machine Learning
Gupta, D., & Gadre, A. (2024) · IEEE International Conference on Big Data and Artificial Intelligence (BDAI) · doi:10.1109/bdai62182.2024.10692545 · Cited by 1 · see citing works
Identification of Aedes Mosquitoes with Audio Data Using Gammatone Filter
Mariyanto, & Pardede, H. F. (2024) · International Conference on Information Technology Research and Innovation (ICITRI) · doi:10.1109/icitri62858.2024.10699195 · Cited by 1 · see citing works
Nazera, F., Tanvir, K., Kabir, M. S., Hasan, M. S., Hena, K. T., & Gomes, D. J. (2025) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain66474.2025.11171832 · Cited by 1 · see citing works
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Castro, L. G. M., Costa, H. V., & Souza, V. M. A. (2024) · Data Science Journal · doi:10.5334/dsj-2024-025 · Cited by 1 · see citing works
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Nkouanga, H. Y., & Singh, S. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00246
Prastyantoko, K. J., Wahyuni, D., & Bukhori, S. (2023) · Contagion Scientific Periodical Journal of Public Health and Coastal Health · doi:10.30829/contagion.v5i2.15146
Condo-Tena, A., Hancco-Ramos, J., & Cabanillas-Carbonell, M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2397-5_64
de Oliveira, G. M., de Sousa, L. A., Gomez, J. P. D. S., Muralikrishna, A., Gomes, C. E., & da Silva, C. E. O. (2023) · Encontro Latino Americano de Iniciação Científica, Encontro Latino Americano de Pós-Graduação, INIC Jr, INID e ENEXUN (INIC/EPG) · doi:10.18066/inic0777.23
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Sumari, A. D. W., Sitorus, Z. A., & Abdie, S. (2023) · International Conference on Electrical and Information Technology (IEIT) · doi:10.1109/ieit59852.2023.10335583
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Mathias, A. C. D. O., Montanher, B., Beleti, C. R., Santiago, R. M., da Silva, R. F., & de Souza, R. C. T. (2023) · Arquivos do Mudi · doi:10.4025/arqmudi.v27iespecial3.70546
A Resource-Constrained Embedded Aedes Aegypti Mosquito Classifier for the Edge
Borges, P., Castel, L. D., & Azambuja, J. R. (2025) · IEEE Latin American Symposium on Circuits and Systems (LASCAS) · doi:10.1109/lascas64004.2025.10966241
IoT-Enabled Intelligent Framework for Real-Time Mosquito Detection and Monitoring
Seervi, A., Mulani, N., Anwar, M. H. B., & Venkanna, U. (2025) · SN Computer Science · doi:10.1007/s42979-025-04016-y
Rodrigues, Y. D. F., Rodrigues, R. N., Paim, K. O., Kreutz, D., Cordeiro, W. L. D. C., & Mansilha, R. B. (2024) · Escola Regional de Redes de Computadores (ERRC) · doi:10.5753/errc.2024.4713
Self-citationMachine learning techniques in the control of Aedes aegypti
Sibiya, A., & Vaseeharan, B. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21461-5.00004-3
Isaiah, N., & Nagesh, K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5214-3_48
Paim, K. O., Silva, T. R. M. B., Dilly, J., Tavares, A. R., Cunha, M. D. P., da Silva, O. S., Recamonde‐Mendoza, M., Mansilha, R. B., & Cordeiro, W. L. D. C. (2025) · Anais do Seminário Integrado de Software e Hardware (SEMISH) · doi:10.5753/semish.2025.6555
Self-citationStalder, D. H., Kudo, H. T. H., von Schmeling, N. A. Z., Coronel, M. C. F., Rodriguez, C., Cabrera, L. H., González, N., & Schaerer, C. E. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5678023
Amarnath, K., Nayakudi, N., & Nair, L. S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-12990-1_9
Breaking the Single-Mosquito Barrier: Synthetic Swarm Data for Acoustic Vector MonitoringPreprint
Dinh, T. D., Vo, M. L., Nguyen, C. T., Le, A. D., & Vo, B.-H. (2026) · Research Square · doi:10.21203/rs.3.rs-8242299/v1
Ishwarya, C., Arokiyaraj, C., Sreelakshmi, S., Parthasarathy, K., & Kumar, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6137867
AI and ML Approaches for Automated Mosquito Species Identification: A Review
Mandal, S. S., & Singh, P. (2026) · International Conference on Innovative Practices in Technology and Management (ICIPTM) · doi:10.1109/iciptm69057.2026.11465938
A two-stage OOD-aware approach for mosquito species detection based on RT-DETRv2
Ni, Z., Feng, Z., Jiao, J., Li, J., Zhu, H., & Yao, Q. Q. (2026) · Scientific Reports · doi:10.1038/s41598-026-52890-0
A Parameter-Efficient Cascaded Framework for Mosquito Species Identification using MFCC Features
Kiriwanna, N., Nukoolkit, C., & Triyason, T. (2026) · doi:10.1145/3816713.3818800
Grasshopper sound acoustic signal analysis using FFT and Butterworth filter
Khairunnisa, K., Sarifudin, S., & Damayanti, A. M. (2026) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v42.i3.pp708-720
da Silva, G. P., Rech, R. S., Becker, T., & Becker, C. D. L. (2026) · Dysphagia · doi:10.1007/s00455-026-10983-y
Pacheco, P. R. D. C., Javier-Hila, A. M. V., Francisco, J. C., Francisco, M. E., & Carvajal, T. M. (2026) · BMC Artificial Intelligence · doi:10.1186/s44398-026-00039-4
- Authors
- Fernandes, M. S., Cordeiro, W., & Recamonde-Mendoza, M.
- Affiliations
- Universidade Federal do Rio Grande do Sul (BR)
- Corresponding author
- Fernandes, Marcelo Schreiber
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 129
- Pages
- 104152
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W3055892316
- Cited by
- 64
- References
- 58
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 13 · 2023: 11 · 2022: 9 · 2021: 8
- FWCI
- 3.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Topics
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mosquito-borne diseases and control
- Social Sciences › Sociology and Political Science › Dengue and Mosquito Control Research
- Keywords
- Aedes aegypti, mosquito detection, audio classification, smartphone audio recording, convolutional neural networks, spectrogram features, ensemble classifiers, mosquito-borne diseases, mosquito surveillance, crowdsourcing
- MeSH terms
- Machine Learning, Mosquito Vectors, Aedes, Animals, Neural Networks, Computer
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