A Cervical Intraepithelial Neoplasia Classification Method Using Feature Extraction and Back Propagation Neural Network

Face recognition has recently become a hot research topic. In order to do more in that area, several algorithms have emerged. However, even the most efficient algorithm has limitations. To overcome this problem, the combination of algorithms is sometimes used. In this paper a methodology based on two approaches is presented. Firstly, we use Locality Preserving Projection (LPP) for feature extraction. Secondly, the Back Propagation Neural Network (BPNN) is used for recognition. Experiments have been done using 400 images of ORL database. Experimental results show that the algorithm is performs well and achieves good recognition.

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Performance Analysis of SOFM based Reduced Complexity Feature Extraction Methods with back Propagation Neural Network for Multilingual Digit Recognition

Indian Journal of Science and Technology ◽

10.17485/ijst/2015/v8i19/76217 ◽

2015 ◽

Vol 8 (19) ◽

Author(s):

John Sahaya Rani Alex ◽

Ajinkya Sunil Mukhedkar ◽

Nithya Venkatesan

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Performance Analysis ◽

Back Propagation ◽

Extraction Methods ◽

Back Propagation Neural Network ◽

Digit Recognition ◽

Reduced Complexity

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Identifikasi Tahu Berformalin dengan Electronic Nose Menggunakan Jaringan Syaraf Tiruan Backpropagation

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.15330 ◽

2016 ◽

Vol 6 (2) ◽

pp. 211

Author(s):

Wida Astuti ◽

Danang Lenono ◽

Faizah Faizah

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Volatile Compounds ◽

Electronic Nose ◽

Back Propagation ◽

Extraction Process ◽

Back Propagation Neural Network ◽

Differential Method ◽

Gas Sensor Array ◽

Network Training

During this time to identify pure and formalin tofu based on color and aroma involving human taster. But this tofu tester still has weaknesses such as subjective. Besides that, the standard chemical analytical methods requires a high cost and need expertise to analyzing it. Basically aroma of tofu is determined by volatile compounds such as heksanal, ethanol, and 1-hexanol, while aroma of formalin tofu is determined by volatile compounds such as OH, CO, and hydrocarbon. Electronic nose based on unselected gas sensor array has the ability to analyze samples with complex compositions that can be known characteristics and qualitative analysis of the samples. Stimulus aroma is transformed by electronic nose into fingerprint data then it is used by feature extraction process using the differential method. The results of feature extraction is used to process the back propagation neural network training to obtain optimal parameters. The parameters have been optimized is then tested on a random tofus. Based on test results, ANN-BP can identify samples with 100% accuracy rate so that the identification of a pure tofu and tofu formalin with electronic nose using back propagation neural network analysis has been successfully carried out.

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IDENTIFICATION OF NITROGEN STATUS IN Brassica juncea L.USING COLOR MOMENT GLCM AND BACKPROPAGATION NEURAL NETWORK

10.31219/osf.io/cmg2b ◽

2020 ◽

Author(s):

Satya Kumara

Keyword(s):

Neural Network ◽

Image Processing ◽

Feature Extraction ◽

Brassica Juncea ◽

Back Propagation ◽

Texture Feature ◽

Back Propagation Neural Network ◽

Texture Feature Extraction ◽

Nitrogen Status ◽

Color Moment

Vegetables cultivation using hydroponic is becoming popular now days because of its irrigation and fertilizer efficiency. One type of vegetable which can be cultivated using hydroponic is green mustard (Brassica juncea L.) tosakan variety. This vegetable is harvested in the vegetative phase, approximately aged of 30 days after planting. In addition, during the vegetative phase, this plant requires more nitrogen for growth of vegetative organs. The lack of nitrogen will lead to slow growth and the leaves turn yellow. In this study, non-destructive technology was developed to identify nitrogen status through the image of green mustard leaf by using digital image processing and artificial neural network. The image processing method used was the color moment for color feature extraction, gray level co-occurrence matrix (GLCM) for texture feature extraction and back propagation neural network to identify nitrogen status from the image of leaf. The input image data resulted from acquisition process was RGB color image which was converted to HSV. Prior to the color and texture feature extraction and texture, acquisition image was segmented and cropped to get the leaf image only. Next Step was to conduct training using back propagation neural network with two hidden layer combinations, 20,000 iteration epoch. Accuracy of the test results using those methods was 97.82%. The result indicates those three methods is reliable to identify nitrogen status in the leaf of green mustard.

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