Cluster Analysis and Artificial Neural Networks Multivariate Classification of Onion Varieties

2010 ◽  
Vol 58 (21) ◽  
pp. 11435-11440 ◽  
Author(s):  
Beatriz Rodríguez Galdón ◽  
Eladia Peña-Méndez ◽  
Josef Havel ◽  
Elena María Rodríguez Rodríguez ◽  
Carlos Díaz Romero
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Artificial Neural Networks ◽  
Multivariate Classification ◽  
Artificial Neural

Classification of Fourier Transform Infrared Microscopic Imaging Data of Human Breast Cells by Cluster Analysis and Artificial Neural Networks

2003 ◽  
Vol 57 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Lin Zhang ◽  
Gary W. Small ◽  
Abigail S. Haka ◽  
Linda H. Kidder ◽  
E. Neil Lewis
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Fourier Transform ◽  
Artificial Neural Networks ◽  
Fourier Transform Infrared ◽  
Carcinoma Cells ◽  
Human Breast ◽  
Microscopic Imaging ◽  
Artificial Neural

Cluster analysis and artificial neural networks (ANNs) are applied to the automated assessment of disease state in Fourier transform infrared microscopic imaging measurements of normal and carcinomatous immortalized human breast cell lines. K-means clustering is used to implement an automated algorithm for the assignment of pixels in the image to cell and non-cell categories. Cell pixels are subsequently classified into carcinoma and normal categories through the use of a feed-forward ANN computed with the Broyden–Fletcher–Goldfarb–Shanno training algorithm. Inputs to the ANN consist of principal component scores computed from Fourier filtered absorbance data. A grid search optimization procedure is used to identify the optimal network architecture and filter frequency response. Data from three images corresponding to normal cells, carcinoma cells, and a mixture of normal and carcinoma cells are used to build and test the classification methodology. A successful classifier is developed through this work, although differences in the spectral backgrounds between the three images are observed to complicate the classification problem. The robustness of the final classifier is improved through the use of a rejection threshold procedure to prevent classification of outlying pixels.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

2007 ◽  
Vol 38 (3) ◽  
pp. 303-314 ◽  
Author(s):  
K. Srinivasa Raju ◽  
D. Nagesh Kumar
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Artificial Neural Networks ◽  
Optimal Number ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Homogeneous Groups ◽  
Index Approach ◽  
Artificial Neural

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies–Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

Classification of ground-truth fire debris samples using artificial neural networks

2021 ◽  
Vol 23 ◽  
pp. 100313
Author(s):  
Nicholas A. Thurn ◽  
Taylor Wood ◽  
Mary R. Williams ◽  
Michael E. Sigman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ground Truth ◽  
Fire Debris ◽  
Artificial Neural

Easy classification of traditional Minas cheeses using artificial neural networks and discriminant analysis

2017 ◽  
Vol 70 (4) ◽  
pp. 492-498 ◽  
Author(s):  
Leandro S Santos ◽  
Roberta M D Cardozo ◽  
Natália Moreiria Nunes ◽  
Andréia B Inácio ◽  
Ana Clarissa dos S Pires ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Artificial Neural
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