Variable selection in classification of environmental soil samples for partial least square and neural network models

2001 ◽  
Vol 446 (1-2) ◽  
pp. 231-242 ◽  
Author(s):  
Ziad Ramadan ◽  
Xin-Hua Song ◽  
Philip K Hopke ◽  
Mara J Johnson ◽  
Kate M Scow
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Models ◽  
Soil Samples ◽  
Partial Least Square ◽  
Least Square ◽  
Neural Network Models

scholarly journals Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45993-45999
Author(s):  
Ung Yang ◽  
Seungwon Oh ◽  
Seung Gon Wi ◽  
Bok-Rye Lee ◽  
Sang-Hyun Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Network Models ◽  
Convolution Neural Network ◽  
Neural Network Models

Classification of first quality fancy cashew kernels using four deep convolutional neural network models

2020 ◽  
Vol 43 (12) ◽  
Author(s):  
Sriram K. Vidyarthi ◽  
Samrendra K. Singh ◽  
Rakhee Tiwari ◽  
Hong‐Wei Xiao ◽  
Rewa Rai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models

Testing of Fiber Contents in Mixture Textiles by NIR Combined with BP Neural Network

2014 ◽  
Vol 651-653 ◽  
pp. 301-304
Author(s):  
Li Liu ◽  
Li Yan ◽  
Yao Cheng Xie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Near Infrared ◽  
Human Life ◽  
Back Propagation ◽  
Fiber Content ◽  
Partial Least Square ◽  
Least Square ◽  
Neural Network Models ◽  
Mixture Samples

Textiles are necessaries of human life. The fiber content is index of textile quality and how to measure it has important meaning. A method for testing fiber contents in mixture textiles by near infrared spectroscopy (NIR) was researched. The near infrared Spectra of samples in the range of 4000 cm-1 - 10000 cm-1 were obtained. Noise reduction and compression of spectra data was done by wavelet transform (WT). The reconstructed spectral signals were established based on WT and the correction models based on back propagation (BP) neural network were built. Comparisons between the BP neural network models at different analysis scale and the model of partial least square method (PLS) were given. When the structure of neural network is 11-9-2 for cotton/ terylene mixture samples and 21-13-2 for cotton/wool mixture samples, the best accuracy and fastest convergence speed is achieved. Experimental results have shown that this approach by Fourier transform NIR based on the BP neural network to predict the fiber content of textile mixture can satisfy the requirement of quantitative analysis and is also suitable for other fiber contents measurement of mixture textiles.

scholarly journals Classification of particle height in a hopper bin from limited discharge data using convolutional neural network models

2018 ◽  
Vol 339 ◽  
pp. 615-624 ◽  
Author(s):  
Shaohua Chen ◽  
Laurent A. Baumes ◽  
Aytekin Gel ◽  
Manogna Adepu ◽  
Heather Emady ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Discharge Data

scholarly journals Classification of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts

2006 ◽  
Vol 3 (1) ◽  
pp. 201-227 ◽  
Author(s):  
N. Lauzon ◽  
F. Anctil ◽  
C. W. Baxter
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Network Models ◽  
Rainfall Runoff ◽  
Neural Network Models ◽  
Kohonen Neural Network ◽  
Streamflow Forecasts ◽  
Rainfall Runoff Model

Abstract. 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 method using a Kohonen neural network is proposed for the classification of precipitation fields. The evaluation and improvement of the performance of a lumped rainfall-runoff model for one-day ahead predictions is then established based on this classification. Multilayer perceptron neural networks are employed as lumped rainfall-runoff models. The Bas-en-Basset watershed in France, which is equipped with 23 rain gauges with data for a 21-year period, is employed as the application case. The results demonstrate the relevance of the proposed classification method, which produces groups of precipitation fields that are in agreement with the global climatological features affecting the region, as well as with the topographic constraints of the watershed (i.e., orography). The strengths and weaknesses of the rainfall-runoff models are highlighted by the analysis of their performance vis-à-vis the classification of precipitation fields. The results also show the capability of multilayer perceptron neural networks to account for the heterogeneity of precipitation, even when built as lumped rainfall-runoff models.

scholarly journals Detection of Fake Doctored Images used to Circulate Fake News

2020 ◽  
Vol 9 (7) ◽  
pp. 1031-1036
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Neural Network Models ◽  
The Public ◽  
Learning Techniques ◽  
The World ◽  
Classification Of Images

Over the few years the world has seen a surge in fake news and some people are even calling it an epidemic. Misleading false articles are sold as news items over social media, whatsapp etc where no proper barrier is set to check the authenticity of posts. And not only articles but news items also contain images which are doctored to mislead the public or cause sabotage. Hence a proper barrier to check for authenticity of images related to news items is absolutely necessary. And hence classification of images(related to news items) on the basis of authenticity is imminent. This paper discusses the possibilities of identifying fake images using machine learning techniques. This is an introduction into fake news detection using the latest evolving neural network models

Broiler weight forecasting using dynamic neural network models with input variable selection

2019 ◽  
Vol 159 ◽  
pp. 97-109 ◽  
Author(s):  
Simon V. Johansen ◽  
Jan D. Bendtsen ◽  
Martin R.-Jensen ◽  
Jesper Mogensen
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Models ◽  
Neural Network Models ◽  
Dynamic Neural Network
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