Nondestructive detection for SSC and firmness of plums by hyperspectral imaging and artificial neural network

2021 ◽  
Author(s):  
Jing Shang ◽  
Qinglong Meng ◽  
Renshuai Huang ◽  
Yan Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hyperspectral Imaging ◽  
Nondestructive Detection ◽  
Artificial Neural

FT-IR Hyperspectral Imaging and Artificial Neural Network Analysis for Identification of Pathogenic Bacteria

2018 ◽  
Vol 90 (15) ◽  
pp. 8896-8904 ◽  
Author(s):  
Peter Lasch ◽  
Maren Stämmler ◽  
Miao Zhang ◽  
Malgorzata Baranska ◽  
Alejandra Bosch ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Hyperspectral Imaging ◽  
Pathogenic Bacteria ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Ft Ir ◽  
Artificial Neural

scholarly journals Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of Colla Corii Asini Adulteration

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Huihui Wang ◽  
Kunlun Wang ◽  
Biyao Wang ◽  
Yan Lv ◽  
Xueheng Tao ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Hyperspectral Imaging ◽  
Prediction Models ◽  
Hyperspectral Data ◽  
Neural Network Modeling ◽  
Imaging Features ◽  
Artificial Neural ◽  
Colla Corii Asini

The study of hyperspectral imaging in tandem with spectral preprocessing and neural network techniques was conducted to realize Colla Corii Asini (CCA, E’jiao) adulteration discrimination. CCA was adulterated with pig skin gelatin (PSG) in the range of 5–95% (w/w) at 5% increments. Three methods were used to pretreat the original spectra, which are multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, and the combination of MSC and SG (MSC-SG). SPA was employed to select the characteristic wavelengths (CWs) to reduce the high dimension. Colour and texture features of CWs were extracted as input of prediction model. Two kinds of artificial neural network (ANN) with three spectral preprocessing methods were applied to establish the prediction models. The prediction model of generalized regression neural network (GRNN) in tandem with the MSC-SG preprocessed method presented satisfactory performance with the correct classification rate value of 92.5%. The results illustrated that the integration of preprocessing methods, hyperspectral imaging features, and ANN modeling had a great potential and feasibility for CCA adulteration discrimination.

scholarly journals Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 898
Author(s):  
Sajad Sabzi ◽  
Razieh Pourdarbani ◽  
Mohammad Hossein Rohban ◽  
Alejandro Fuentes-Penna ◽  
José Luis Hernández-Hernández ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hyperspectral Imaging ◽  
Majority Voting ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Significant Difference ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network–imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network–harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the K-nearest-neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network–biogeography-based optimization (ANN-BBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.

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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