Inversion of inherent optical properties of water using artificial neural network techniques for coastal water

2009 ◽  
Author(s):  
I. Ioannou ◽  
J. Zhou ◽  
A. Gilerson ◽  
B. Gross ◽  
F. Moshary ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optical Properties ◽  
Coastal Water ◽  
Inherent Optical Properties ◽  
Artificial Neural

scholarly journals Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Zhibin Yu ◽  
Yubo Wang ◽  
Bing Zheng ◽  
Haiyong Zheng ◽  
Nan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optical Properties ◽  
Optical Sensors ◽  
Deep Neural Network ◽  
Marine Biology ◽  
Depth Information ◽  
Learning Technology ◽  
Inherent Optical Properties ◽  
Artificial Neural

Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA) deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.

scholarly journals Classification of hazelnut varieties by using artificial neural network and discriminant analysis

2021 ◽  
Vol 19 (4) ◽  
pp. e0211-e0211
Author(s):  
Omer Keles ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optical Properties ◽  
Discriminant Analysis ◽  
Success Rates ◽  
Independent Variables ◽  
Dependent Variables ◽  
Artificial Neural ◽  
Corylus Avellana L

Aim of study: This study was conducted to classify hazelnut (Corylus avellana L.) varieties by using artificial neural network and discriminant analysis. Area of study: Samsun Province, Turkey. Material and methods: The physical, mechanical and optical properties of 11 hazelnut varieties were determined for three major axes. The parameters of physical, mechanical and optical properties were included as independent variables, while hazelnut varieties were included as dependent variables. Models were created for each of the three axes to classify hazelnut varieties. Main results: Classification success rates with Artificial Neural Networks (ANN) and Discriminant Analysis (DA) were found as 89.1% and 92.7% for X axis, as 92.7% and 92.7% for Y axis and as 86.8% and 88.7% for Z axis, respectively. The classification results of ANN and DA models were found to be very close to each other. Both models can be used in the classification of hazelnut varieties. Research highlights: The results obtained for the identification and classification of hazelnut varieties show the feasibility and effectiveness of the proposed models.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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