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COMPARATIVE STUDY ON DEEP NEURAL NETWORK MODELS FOR CROP CLASSIFICATION USING TIME SERIES POLSAR AND OPTICAL DATA
Deep neural networks CNNs LSTMs ConvLSTMs Crop classification
2019/2/28
Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a c...
Studies of the categorical perception (CP) of sensory continua have a long and rich history in
psychophysics. In 1977, Macmillan et al. introduced the use of signal detection theory to CP
studies. A...
Neural network models and cognitive neuropsychology
Neural network models cognitive neuropsychology
2015/6/19
Neural network models and cognitive neuropsychology.
广东外语外贸大学运筹学英文课件第八章 Network Models。
Assessing the Effect of Quantitative and Qualitative Predictors on Gastric Cancer Individuals Survival Using Hierarchical Artificial Neural Network Models
Survival Life Expectancy Proportional Hazards Model Neural Networks
2015/9/29
Background: There are numerous unanswered questions in the application of artificial neural network models for analysis of survival data. In most studies, independent variables have been studied as qu...
Analysis of Partially Observed Networks via Exponential-family Random Network Models
Analysis Partially Observed Networks via Exponential-family Random Network Models
2013/4/27
Exponential-family random network (ERN) models specify a joint representation of both the dyads of a network and nodal characteristics. This class of models allow the nodal characteristics to be model...
Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves ...
Maximum Likelihood Estimation in Network Models
beta model polytope of degree sequences random graphs Rasch model p1 model
2011/6/20
We study maximum likelihood estimation for the statistical model for both directed and undirected
random graph models in which the degree sequences areminimal sufficient statistics. In the undirected...
Towards a Better Understanding of Large Scale Network Models
Better Understanding Large Scale Network Models
2011/3/4
Connectivity and capacity are two fundamental properties of wireless multi-hop networks. The scalability of these properties has been a primary concern for which asymptotic analysis is a useful tool.
Data transformation for neural network models in water resources applications
Data transformation neural network models water resources applications
2009/12/4
A step that should be considered when developing artificial neural network (ANN) models for water resources applications is the selection of an appropriate transformation of the data. In general, the ...
Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning
Regression Artificial Neural Network Models Surface Roughness Prediction Cutting Parameters CNC Turning
2009/9/4
Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in machining of parts. In this study, the experimental results corresponding to the effects of di...
Clustering of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts
rainfall-runoff Clustering
2009/5/13
This work addresses the issue of better considering the heterogeneity of precipitation fields within lumped rainfall-runoff models where only areal mean precipitation is usually used as an input. A me...
Analysis of Data on Xanthan Fermentation in Stationary Phase Using Black Box and Metabolic Network Models
数据分析 黄原胶 发酵法
2009/4/28
The xanthan fermentation data in the stationary phase was analyzed using the black box and the metabolic network models. The data consistency ls checked through the elemental balance in the black bo...
Neural network models of protein domain evolution
models in biochemistry protein domain evolution neural networks ethics of modeling
2008/4/7
Protein domains are complex adaptive systems, and here a novel procedure is presented that models the evolution of new functional sites within stable domain folds using neural networks. Neural network...
NEURAL NETWORK MODELS FOR DESIGN OF BLOCK CIPHER SYSTEM
Block cipher system Hopfield neural net
2007/8/7
In this paper, a class of block cipher systems having asymptotically perfect secrecy is constructed by using the neural network models modified from the Hopfield model.