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A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats
2019
Sensors
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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
2021
Applied Sciences
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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
2021
Insects
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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]
2021
arXiv
pre-print
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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
2021
Scientific Reports
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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
2021
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
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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
2018
Sensors
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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
2021
Applied Sciences
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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
2020
Journal of Robotics and Control (JRC)
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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
2020
IEEE Access
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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
2022
Sensors
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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
2020
AgriEngineering
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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
2020
IEEE Access
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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
2019
International Journal of Computer Applications
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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
2020
Applied Sciences
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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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