scholarly journals Statistical Aspects of High-Dimensional Sparse Artificial Neural Network Models

2020 ◽  
Vol 2 (1) ◽  
pp. 1-19
Author(s):  
Kaixu Yang ◽  
Tapabrata Maiti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spatial Data ◽  
Feature Vector ◽  
Single Layer ◽  
Training Data ◽  
Classification Model ◽  
High Dimensional ◽  
Neural Network Models ◽  
Artificial Neural

An artificial neural network (ANN) is an automatic way of capturing linear and nonlinear correlations, spatial and other structural dependence among features. This machine performs well in many application areas such as classification and prediction from magnetic resonance imaging, spatial data and computer vision tasks. Most commonly used ANNs assume the availability of large training data compared to the dimension of feature vector. However, in modern applications, as mentioned above, the training sample sizes are often low, and may be even lower than the dimension of feature vector. In this paper, we consider a single layer ANN classification model that is suitable for analyzing high-dimensional low sample-size (HDLSS) data. We investigate the theoretical properties of the sparse group lasso regularized neural network and show that under mild conditions, the classification risk converges to the optimal Bayes classifier’s risk (universal consistency). Moreover, we proposed a variation on the regularization term. A few examples in popular research fields are also provided to illustrate the theory and methods.

scholarly journals Application of Convolution Neural Networks and Hydrological Images for the Estimation of Pollutant Loads in Ungauged Watersheds

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 239
Author(s):  
Chul Min Song
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Conceptual Model ◽  
Oxygen Demand ◽  
Training Data ◽  
Neural Network Models ◽  
Pollutant Loads ◽  
Artificial Neural ◽  
The Government ◽  
Ungauged Watersheds

River monitoring and predicting analysis for establishing pollutant loads management require numerous budgets and human resources. However, it is general that the number of government officials in charge of these tasks is few. Although the government has been commissioning a study related to river management to experts, it has been inevitable to avoid the consumption of a massive budget because the characteristics of pollutant loads present various patterns according to topographic of the watershed, such as topology like South Korea. To address this, previous studies have used conceptual and empirical models and have recently used artificial neural network models. The conceptual model has a shortcoming in which it required massive data and has vexatious that has to enforce the sensitivity and uncertain analysis. The empirical model and artificial neural network (ANN) need lower data than a conceptual model; however, these models have a flaw that could not reflect the topographical characteristic. To this end, this study has used a convolution neural network (CNN), one of the deep learning algorithms, to reflect the topographical characteristic and had estimated the pollutant loads of ungauged watersheds. The estimation results for the biochemical oxygen demand (BOD) and total phosphorus (TP) loads for three ungauged watersheds were all excellent. However, prediction results with low accuracy were obtained when the hydrological images of a watershed with a land cover status different from the ungauged watersheds were used as training data for the CNN model.

Assessing State of the Art on Artificial Neural Network Paradigms for Level of Eutrophication Estimation of Water Bodies

2016 ◽  
Vol 5 (4) ◽  
pp. 135
Author(s):  
Tushar Anthwal ◽  
M K Pandey
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spatial Data ◽  
Network Models ◽  
Water Bodies ◽  
Statistical Interpretation ◽  
Estimation Of Parameters ◽  
Neural Network Models ◽  
Resultant Data ◽  
Artificial Neural

With growing power of computer and blend of intelligent soft wares, the interpretation and analytical capabilities of the system had shown an excellent growth, providing intelligence solutions to almost every computing problem. In this direction here we are trying to identify how different geocomputation techniques had been implemented for estimation of parameters on water bodies so as to identify the level of contamination leading to the different level of eutrophication. The main mission of this paper is to identify state-of-art in artificial neural network paradigms that are prevailing and effective in modeling and combining spatial data for anticipation. Among this, our interest is to identify different analysis techniques and their parameters that are mainly used for quality inspection of lakes and estimation of nutrient pollutant content in it, and different neural network models that offered the forecasting of level of eutrophication in the water bodies. Different techniques are analyzed over the main steps;-assimilation of spatial data, statistical interpretation technique, observed parameters used for eutrophication estimation and accuracy of resultant data.

scholarly journals A Hybrid Artificial Neural Network to Estimate Soil Moisture Using SWAT+ and SMAP Data

2020 ◽  
Vol 2 (3) ◽  
pp. 283-306
Author(s):  
Katherine H. Breen ◽  
Scott C. James ◽  
Joseph D. White ◽  
Peter M. Allen ◽  
Jeffery G. Arnold
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Spatial Data ◽  
Assessment Tool ◽  
Training Data ◽  
Near Surface ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

In this work, we developed a data-driven framework to predict near-surface (0–5 cm) soil moisture (SM) by mapping inputs from the Soil & Water Assessment Tool to SM time series from NASA’s Soil Moisture Active Passive (SMAP) satellite for the period 1 January 2016–31 December 2018. We developed a hybrid artificial neural network (ANN) combining long short-term memory and multilayer perceptron networks that were used to simultaneously incorporate dynamic weather and static spatial data into the training algorithm, respectively. We evaluated the generalizability of the hybrid ANN using training datasets comprising several watersheds with different environmental conditions, examined the effects of standard and physics-guided loss functions, and experimented with feature augmentation. Our model could estimate SM on par with the accuracy of SMAP. We demonstrated that the most critical learning of the physical processes governing SM variability was learned from meteorological time series, and that additional physical context supported model performance when test data were not fully encapsulated by the variability of the training data. Additionally, we found that when forecasting SM based on trends learned during the earlier training period, the models appreciated seasonal trends.

scholarly journals Preliminary study on development of cocoa beans fermentation level measurement based on computer vision and artificial intelligence

2021 ◽  
Vol 924 (1) ◽  
pp. 012019
Author(s):  
C D Anggraini ◽  
A W Putranto ◽  
Z Iqbal ◽  
H Firmanto ◽  
D F Al Riza
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Classification Model ◽  
Cocoa Bean ◽  
Cocoa Beans ◽  
Neural Network Models ◽  
Important Indicator ◽  
Cocoa Bean Fermentation ◽  
Artificial Neural

Abstract The fermentation process is an important indicator of cocoa beans’ quality. The standard method used is the Magra test by splitting the cocoa beans and observing the color of the beans with the naked eye to estimate the degree of fermentation. Although, manual estimation systems require specific expertise, which can lead to inconsistency in predicting cocoa bean fermentation rate. This research aims to develop a classification model of two categories of cocoa, i.e., fermented and unfermented cocoa, using computer vision and a machine learning model. Image analysis has been carried out, and color features have been used to train and compare several classification models. After analyzing the data, it was found out that a model that can quantify the standard and accurate measurement of the degree of fermentation of cocoa beans using artificial neural network models so that it can segment, calculate, and grade classification by using color feature extraction, which is the average value of RGB and L*a*b. The Artificial Neural Network (ANN) Multilayer Perceptron (MLP) has been found to be superior compared to other models achieving training and validation accuracy of 94%.

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

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