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A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats

Kyukwang Kim, Jieum Hyun, Hyeongkeun Kim, Hwijoon Lim, Hyun Myung
2019 Sensors  
In this research, we present the preliminary results of an automated system that detects the presence of mosquitoes via image processing using multiple deep learning networks.  ...  This system demonstrates a higher efficiency than hunting adult mosquitos while avoiding damage to other insects.  ...  This research has also applied deep learning based-architecture for the image processing.  ... 
doi:10.3390/s19122785 fatcat:yskxjegrbzcp5bowpnrqwnxl7i

Automatic Surgical Instrument Recognition—A Case of Comparison Study between the Faster R-CNN, Mask R-CNN, and Single-Shot Multi-Box Detectors

Jiann-Der Lee, Jong-Chih Chien, Yu-Tsung Hsu, Chieh-Tsai Wu
2021 Applied Sciences  
It would be of great help, in surgery, to quickly and automatically identify and keep count of the surgical instruments in the operating room using only video information.  ...  In this study, the recognition rate of fourteen surgical instruments is studied using the Faster R-CNN, Mask R-CNN, and Single Shot Multi-Box Detectors, which are three deep learning networks in recent  ...  The features used in determining the similarities between groups can be extracted from the objects using machine learning methods, and in the CNN-based deep learning architectures, these features can be  ... 
doi:10.3390/app11178097 fatcat:2mqwb65p4ndbdf3hxlfmaazjlu

The Automatic Classification of Pyriproxyfen-Affected Mosquito Ovaries

Mark T. Fowler, Rosemary S. Lees, Josias Fagbohoun, Nancy S. Matowo, Corine Ngufor, Natacha Protopopoff, Angus Spiers
2021 Insects  
Using TensorFlow, a resnet-50 CNN was pretrained with the ImageNet dataset. This CNN architecture was then retrained using a novel dataset of 524 dissected ovary images from An. gambiae s.l.  ...  Data augmentation increased the training set to 6973 images. A test set of 157 images was used to measure accuracy.  ...  Discussion This study aimed to use deep learning, data augmentation, and transfer learning to develop an automatic method for the classification of mosquito fecundity.  ... 
doi:10.3390/insects12121134 pmid:34940222 pmcid:PMC8703609 fatcat:g763pfm74vh3zp3pi7iayce2ya

Neural Networks for Dengue Prediction: A Systematic Review [article]

Kirstin Roster, Francisco A. Rodrigues
2021 arXiv   pre-print
Following the PRISMA guidelines, we conduct a systematic search of studies that use neural networks to forecast Dengue in human populations.  ...  In this systematic review, we provide an introduction to the neural networks relevant to Dengue forecasting and review their applications in the literature.  ...  The best model used three nodes (corresponding to three input features). Abeyrathna et al (2019) [32] compared shallow and deep architectures.  ... 
arXiv:2106.12905v1 fatcat:btbvlokb6vhjrkv2hosqgcq2ma

Deep learning approaches for challenging species and gender identification of mosquito vectors

Veerayuth Kittichai, Theerakamol Pengsakul, Kemmapon Chumchuen, Yudthana Samung, Patchara Sriwichai, Natthaphop Phatthamolrat, Teerawat Tongloy, Komgrit Jaksukam, Santhad Chuwongin, Siridech Boonsang
2021 Scientific Reports  
This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes.  ...  We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm.  ...  In addition, we thank the College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology, Ladkrabang, for providing the deep learning platform and software to support the research  ... 
doi:10.1038/s41598-021-84219-4 pmid:33649429 pmcid:PMC7921658 fatcat:bp7d4lck4rdqll4vt2tuvcd6dm

An Efficient Pest Classification In Smart Agriculture Using Transfer Learning

Tuan Nguyen, Quoc-Tuan Vien, Harin Sellahewa
2021 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems  
In this paper, we introduce an efficient method basing on deep learning approach to classify pests from images captured from the crops.  ...  The proposed method is implemented on various EfficientNet and shown to achieve a considerably high accuracy in a complex dataset, but only a few iterations are required in the training process.  ...  Training a deep learning model needs more than one epoch to pass the whole dataset several times to the same deep learning model.  ... 
doi:10.4108/eai.26-1-2021.168227 fatcat:ixltqdlmdbcgvpwujosewojb7i

A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture

Yuanhong Zhong, Junyuan Gao, Qilun Lei, Yao Zhou
2018 Sensors  
Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system.  ...  A more practical method is to use traps, in this way, insects can be attracted by a variety of ways based on light [12], color [5, 13] , pheromones [7, 14] , etc.  ...  System Testing As mentioned above, we design the counting and recognition system using YOLO deep learning network to do detection and coarse counting of flying insects and using SVM to do specific classification  ... 
doi:10.3390/s18051489 pmid:29747429 pmcid:PMC5982143 fatcat:zmkcpm5kjfdvjkggwb6yah6btm

Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

Asma Maqsood, Muhammad Shahid Farid, Muhammad Hassan Khan, Marcin Grzegorzek
2021 Applied Sciences  
Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world.  ...  Deep learning approaches modernize the world with their superior performance.  ...  Fully convolutional regression networks (FCRN) [47] regress CNN spatial feature maps to detect and count cells in microscopic images.  ... 
doi:10.3390/app11052284 fatcat:w5utaierwnbjfjmcdsvtsb276a

Performance assessment of Deep Learning procedures on Malaria dataset

Shruti Sinha, Udit Srivastava, Vikas Dhiman, Akhilan P.S, Sashikala Mishra
2020 Journal of Robotics and Control (JRC)  
The prototypical model uses different deep learning algorithms that uses the same dataset to validate stability. The model uses the two various components of CNN like Sequential and ResNet.  ...  The proposed model uses the conception of Convolutional Neural Network (CNN) to lessen the time complexity in the identification of Malaria.  ...  And 10% of training data was used for validation purposes. The dataset was applied with data augmentation to artificially expand the dataset.  ... 
doi:10.18196/jrc.2145 fatcat:fkvnho5yozalteyx62sitduxuu

A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico

Annalisa Appice, Yulia R. Gel, Iliyan Iliev, Vyacheslav Lyubchich, Donato Malerba
2020 IEEE Access  
In this paper, we formulate a holistic machine learning strategy to analyze the temporal dynamics of temperature and dengue data and use this knowledge to produce accurate predictions of dengue, based  ...  The mosquito-borne dengue fever is a major public health problem in tropical countries, where it is strongly conditioned by climate factors such as temperature.  ...  ACKNOWLEDGMENT The authors would like to thank Lynn Rudd for her help in proof reading the manuscript and Mauricio Santillana for providing the official dengue incidence data.  ... 
doi:10.1109/access.2020.2980634 fatcat:sz5cbmtuwzdrho6twckzacgf6u

Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities

Ioannis Saradopoulos, Ilyas Potamitis, Stavros Ntalampiras, Antonios I. Konstantaras, Emmanuel N. Antonidakis
2022 Sensors  
deep-learning models.  ...  All edge devices were able to process images and report accuracy in counting exceeding 95%, but at different rates and power consumption.  ...  Acknowledgments: We acknowledge Giota Psirofonia for providing us enough insect collections for our experiments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22052006 pmid:35271153 pmcid:PMC8914644 fatcat:p5gmiyotwrfwbemwsnmuofc62m

Deep Learning Application in Plant Stress Imaging: A Review

Zongmei Gao, Zhongwei Luo, Wen Zhang, Zhenzhen Lv, Yanlei Xu
2020 AgriEngineering  
In this paper, we reviewed the latest deep learning approaches pertinent to the image analysis of crop stress diagnosis.  ...  To address this issue, rapid methods are in urgent needs.  ...  Such images could be enabled to operate in the deep transfer learning architecture, such as Alexnet, VGG, GoogleNet, while such pre-trained transfer networks could not be applied to the 3D datasets, such  ... 
doi:10.3390/agriengineering2030029 fatcat:ehqowfrcdrddlgpr63rtkkfwxm

Counting and Classification of Malarial Parasite from Giemsa-Stained Thin Film Images

Wasu Kudisthalert, Kitsuchart Pasupa, Sissades Tongsima.
2020 IEEE Access  
This is achieved by WELM in conjunction with deep-learned (AlexNet_FC7) and the hand-crafted (color) features. INDEX TERMS Combining features, Giemsa-stained thin film, malaria.  ...  To cure a malaria infected patient and prevent further spreading, malaria diagnosis using microscopy to visualize Giemsa-stained parasites is commonly done.  ...  ACKNOWLEDGMENT The authors would like to thank Ms. Parichat Prommana and Dr.  ... 
doi:10.1109/access.2020.2990497 fatcat:3bsme2rpdrapfkmbh7xemsiwy4

Malaria Parasite Detection from Microscopic Blood Smear Image: A Literature Survey

Thenu Eliza, Sreekumar K.
2019 International Journal of Computer Applications  
This allows the parasite to enter the human body, and they get matured in the liver and affect the RBC.  ...  Malaria is a mosquito-borne parasitic disease that is caused by the parasite of the genus Plasmodium. This infectious disease is transmitted via the bite of infected Anopheles mosquitoes.  ...  Developed a Deep Learning method for the detection of malarial parasite using the Deep Belief Network. CONCLUSION Malaria is life-threatening diseases that affect millions of people in this world.  ... 
doi:10.5120/ijca2019918741 fatcat:fi4ump3ftnd2vjxhc5g5cs4dxu

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Nuria C. Caballé, José L. Castillo-Sequera, Juan A. Gómez-Pulido, José M. Gómez-Pulido, María L. Polo-Luque
2020 Applied Sciences  
This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial  ...  To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases.  ...  Thus, Deep Learning arose as a specific kind of Machine Learning allowing for us to deal with this type of databases.  ... 
doi:10.3390/app10155135 fatcat:uy4uncqbjvfx3jnsqmthfvvzaq
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