Classification of hazelnut varieties by using artificial neural network and discriminant analysis

Aim of study: This study was conducted to classify hazelnut (Corylus avellana L.) varieties by using artificial neural network and discriminant analysis. Area of study: Samsun Province, Turkey. Material and methods: The physical, mechanical and optical properties of 11 hazelnut varieties were determined for three major axes. The parameters of physical, mechanical and optical properties were included as independent variables, while hazelnut varieties were included as dependent variables. Models were created for each of the three axes to classify hazelnut varieties. Main results: Classification success rates with Artificial Neural Networks (ANN) and Discriminant Analysis (DA) were found as 89.1% and 92.7% for X axis, as 92.7% and 92.7% for Y axis and as 86.8% and 88.7% for Z axis, respectively. The classification results of ANN and DA models were found to be very close to each other. Both models can be used in the classification of hazelnut varieties. Research highlights: The results obtained for the identification and classification of hazelnut varieties show the feasibility and effectiveness of the proposed models.

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Artificial Neural Network for Combined Steam-Carbon Dioxide Reforming of Methane

Journal of Nanoscience and Nanotechnology ◽

10.1166/jnn.2020.17634 ◽

2020 ◽

Vol 20 (9) ◽

pp. 5730-5733 ◽

Cited By ~ 1

Author(s):

Sharon Jo ◽

Byung Chol Ma ◽

Young Chul Kim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Reaction Temperature ◽

Reaction Rate ◽

Complex Reaction ◽

Direct Influence ◽

Independent Variables ◽

Dependent Variables ◽

Artificial Neural ◽

Artificial Neural Network Ann

The CH4 conversion, CO2 conversion, and H2/CO ratio were set as dependent variables, as the feed rate, flow rate and reaction temperature as independent variables in the complex reaction of methane. We used the Artificial Neural Network (ANN) technique to build a model of the process. The ANN technique was able to predict the reforming process with higher accuracy due to the training capability. The reaction temperature has the greatest effect on the CO2–CH4 reforming reaction. This is because the catalytic reaction temperature has a direct influence on the thermodynamic value and the reaction rate and the equilibrium state.

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Seed classification of three species of amaranth (Amaranthus spp.) using artificial neural network and canonical discriminant analysis – CORRIGENDUM

The Journal of Agricultural Science ◽

10.1017/s0021859619000819 ◽

2019 ◽

Vol 157 (5) ◽

pp. 469-469

Author(s):

A. Bagheri ◽

L. Eghbali ◽

R. Sadrabadi Haghighi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Discriminant Analysis ◽

Canonical Discriminant Analysis ◽

Artificial Neural ◽

Amaranthus Spp

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Classification of Heartbeats based on Linear Discriminant Analysis and Artificial Neural Network

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference ◽

10.1109/iembs.2005.1616626 ◽

2005 ◽

Cited By ~ 1

Author(s):

M.H. Song ◽

J. Lee ◽

H.D. Park ◽

K.J. Lee

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Linear Discriminant ◽

Artificial Neural

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Classification of Cardiac Signals Using Artificial Neural Network and Fuzzy Equivalence Relation

i-manager’s Journal on Software Engineering ◽

10.26634/jse.2.4.496 ◽

2008 ◽

Vol 2 (4) ◽

pp. 48-56

Author(s):

B. Anuradha ◽

V.C. Veera Reddy

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Equivalence Relation ◽

Artificial Neural ◽

Cardiac Signals

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Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

10.36652/0042-4633-2020-8-61-64 ◽

2020 ◽

pp. 61-64

Author(s):

Yu.G. Kabaldin ◽

A.A. Khlybov ◽

M.S. Anosov ◽

D.A. Shatagin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Acoustic Emission ◽

Acoustic Emission Signal ◽

Low Temperatures ◽

Cold Resistance ◽

Emission Signal ◽

Impact Bending ◽

Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

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