scholarly journals Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models

2012 ◽  
Vol 91 (12) ◽  
pp. 3286-3294 ◽  
Author(s):  
A. Faridi ◽  
N.K. Sakomura ◽  
A. Golian ◽  
S.M. Marcato
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Broiler Chicken ◽  
Carcass Characteristics ◽  
Network Models ◽  
Support Vector ◽  
Neural Network Models

Comparisons of Support Vector Regression and Neural Network in Modelling the Hydraulic Damper

2011 ◽  
Vol 403-408 ◽  
pp. 3805-3812 ◽  
Author(s):  
Kong Hui Guo ◽  
Xian Yun Wang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Support Vector Regression ◽  
Network Models ◽  
Support Vector ◽  
Neural Network Models ◽  
Hydraulic Damper ◽  
Nonparametric Models ◽  
Force Velocity ◽  
Bpnn Model

Nonparametric models of hydraulic damper based on support vector regression (SVR) are developed. Then these models are compared with two kinds neural network models. One is backpropagation neural network (BPNN) model; another is radial basis function neural network (RBFNN) model. Comparisons are carried out both on virtual damper and actual damper. The force-velocity relation of a virtual damper is obtained based on a rheological model. Then these data are used to identify the characteristics of the virtual damper. The dynamometer measurements of an actual displacement-dependent damper are obtained by experiment. And these data are used to identify the characteristics of this actual damper. The comparisons show that BPNN model is best at identifying the characteristics of the virtual damper, but SVR model is best at identifying the characteristics of the actual damper. The reason is that all experimental data include noise more or less. When the amplitude of the noise is smaller than the parameter of SVR, the noise can not affect the construction of the resulting model. So when training a model based on the experimental data, SVR is superior to other neural networks methods.

scholarly journals HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8184
Author(s):  
Tian Gao ◽  
Anil Kumar Nalini Chandran ◽  
Puneet Paul ◽  
Harkamal Walia ◽  
Hongfeng Yu
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Seed Quality ◽  
Network Models ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Neural Network Models ◽  
Systematic Analysis ◽  
Background Removal ◽  
End To End

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.

scholarly journals Forecasts of Tropical Pacific Sea Surface Temperatures by Neural Networks and Support Vector Regression

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Silvestre Aguilar-Martinez ◽  
William W. Hsieh
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Tropical Pacific ◽  
Sea Surface ◽  
Water Volume ◽  
Support Vector ◽  
Neural Network Models ◽  
Error Norm ◽  
Sst Anomalies ◽  
The Tropical Pacific

Two nonlinear regression methods, Bayesian neural network (BNN) and support vector regression (SVR), and linear regression (LR), were used to forecast the tropical Pacific sea surface temperature (SST) anomalies at lead times ranging from 3 to 15 months, using sea level pressure (SLP) and SST as predictors. Datasets for 1950–2005 and 1980–2005 were studied, with the latter period having the warm water volume (WWV) above the 20∘C isotherm integrated across the equatorial Pacific available as an extra predictor. The forecasts indicated that the nonlinear structure is mainly present in the second PCA (principal component analysis) mode of the SST field. Overall, improvements in forecast skills by the nonlinear models over LR were modest. Although SVR has two structural advantages over neural network models, namely (a) no multiple minima in the optimization process and (b) an error norm robust to outliers in the data, it did not give better overall forecasts than BNN. Addition of WWV as an extra predictor generally increased the forecast skills slightly; however, the influence of WWV on SST anomalies in the tropical Pacific appears to be linear.

Gated Hierarchical LSTMs for Target-Based Sentiment Analysis

2018 ◽  
Vol 28 (11n12) ◽  
pp. 1719-1737
Author(s):  
Hao Wang ◽  
Xiaofang Zhang ◽  
Bin Liang ◽  
Qian Zhou ◽  
Baowen Xu
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Network Models ◽  
Neural Model ◽  
Attention Mechanism ◽  
Support Vector ◽  
Neural Network Models ◽  
Long Distance ◽  
Sentence Level

In the field of target-based sentiment analysis, the deep neural model combining attention mechanism is a remarkable success. In current research, it is commonly seen that attention mechanism is combined with Long Short-Term Memory (LSTM) networks. However, such neural network-based architectures generally rely on complex computation and only focus on single target. In this paper, we propose a gated hierarchical LSTM (GH-LSTMs) model which combines regional LSTM and sentence-level LSTM via a gated operation for the task of target-based sentiment analysis. This approach can distinguish different polarities of sentiment of different targets in the same sentence through a regional LSTM. Furthermore, it is able to concentrate on the long-distance dependency of target in the whole sentence via a sentence-level LSTM. The final results of our experiments on multi-domain datasets of two languages from SemEval 2016 indicate that our approach yields better performance than Support Vector Machine (SVM) and several typical neural network models. A case study of some typical examples also makes a supplement to this conclusion.

scholarly journals Automatic migraine classification using artificial neural networks

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 618
Author(s):  
Paola A. Sanchez-Sanchez ◽  
José Rafael García-González ◽  
Juan Manuel Rúa Ascar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nearest Neighbor ◽  
Emergency Services ◽  
Network Models ◽  
Support Vector ◽  
Neural Network Models ◽  
Accuracy And Precision ◽  
Artificial Neural ◽  
Selection Of

Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

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