scholarly journals Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 99
Author(s):  
Galen Richardson ◽  
Sylvain G. Leblanc ◽  
Julie Lovitt ◽  
Krishan Rajaratnam ◽  
Wenjun Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Random Forest ◽  
Convolutional Neural Networks ◽  
Spatial Patterns ◽  
Surface Texture ◽  
Limiting Factors ◽  
Forest Models ◽  
Random Forest Models ◽  
Relationship Of

Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily rely on pixel DN values for classification. However, the limited spectral range of ground photos requires additional characteristics to differentiate lichen from spectrally similar objects, such as bright logs. By applying a neural network to tiles of a UAV orthomosaics, additional characteristics, such as surface texture and spatial patterns, can be used for inferences. Our methodology used a neural network (UAV LiCNN) trained on ground photo mosaics to predict lichen in UAV orthomosaic tiles. The UAV LiCNN achieved mean user and producer accuracies of 85.84% and 92.93%, respectively, in the high lichen class across eight different orthomosaics. We compared the known lichen percentages found in 77 vegetation microplots with the predicted lichen percentage calculated from the UAV LiCNN, resulting in a R2 relationship of 0.6910. This research shows that AI models trained on ground photographs effectively classify lichen in UAV orthomosaics. Limiting factors include the misclassification of spectrally similar objects to lichen in the RGB bands and dark shadows cast by vegetation.

scholarly journals An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model (Preprint)

2020 ◽  
Author(s):  
Hoon Ko ◽  
Heewon Chung ◽  
Wu Seong Kang ◽  
Chul Park ◽  
Do Wan Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Random Forest ◽  
Hospital Admission ◽  
Deep Neural Network ◽  
Care Providers ◽  
Blood Samples ◽  
Routine Blood ◽  
Forest Models ◽  
Random Forest Models

BACKGROUND COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. OBJECTIVE To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. METHODS We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. RESULTS In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. CONCLUSIONS Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.

scholarly journals An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model

10.2196/25442 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e25442
Author(s):  
Hoon Ko ◽  
Heewon Chung ◽  
Wu Seong Kang ◽  
Chul Park ◽  
Do Wan Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Random Forest ◽  
Hospital Admission ◽  
Deep Neural Network ◽  
Care Providers ◽  
Blood Samples ◽  
Routine Blood ◽  
Forest Models ◽  
Random Forest Models

Background COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. Objective To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. Methods We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. Results In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. Conclusions Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.

Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia

2020 ◽  
Vol 24 (16) ◽  
pp. 12079-12090 ◽  
Author(s):  
Mohammad Ali Ghorbani ◽  
Ravinesh C. Deo ◽  
Sungwon Kim ◽  
Mahsa Hasanpour Kashani ◽  
Vahid Karimi ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
River Flow ◽  
River Stage ◽  
Flow Prediction ◽  
Cascade Correlation ◽  
Forest Models ◽  
Random Forest Models

scholarly journals METAPHOR: Probability density estimation for machine learning based photometric redshifts

2016 ◽  
Vol 12 (S325) ◽  
pp. 197-200 ◽  
Author(s):  
V. Amaro ◽  
S. Cavuoti ◽  
M. Brescia ◽  
C. Vellucci ◽  
C. Tortora ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Probability Density Estimation ◽  
Photometric Redshifts ◽  
Validation Test ◽  
Fitting Method ◽  
Forest Models ◽  
Random Forest Models ◽  
Template Fitting

AbstractWe present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z’s and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF’s derived from a traditional SED template fitting method (Le Phare).

BP neural networks and random forest models to detect damage by Dendrolimus punctatus Walker

2018 ◽  
Vol 31 (1) ◽  
pp. 107-121 ◽  
Author(s):  
Zhanghua Xu ◽  
Xuying Huang ◽  
Lu Lin ◽  
Qianfeng Wang ◽  
Jian Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Random Forest ◽  
Bp Neural Networks ◽  
Forest Models ◽  
Random Forest Models

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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