Classification of multispectral satallite images by using adaptive neuro-fuzzy classifier with linguistic hedges

2011 ◽  
Author(s):  
Bayram Cetisli ◽  
Habil Kalkan
Keyword(s):  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Linguistic Hedges

scholarly journals Classification of Horse Gaits Using FCM-Based Neuro-Fuzzy Classifier from the Transformed Data Information of Inertial Sensor

Sensors ◽  
2016 ◽  
Vol 16 (5) ◽  
pp. 664 ◽  
Author(s):  
Jae-Neung Lee ◽  
Myung-Won Lee ◽  
Yeong-Hyeon Byeon ◽  
Won-Sik Lee ◽  
Keun-Chang Kwak
Keyword(s):  
Inertial Sensor ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy

Fuzzy-NN approach with statistical features for description and classification of efficient image retrieval

2019 ◽  
Vol 34 (03) ◽  
pp. 1950022 ◽  
Author(s):  
Meenakshi Garg ◽  
Harpal Singh ◽  
Manisha Malhotra
Keyword(s):  
Image Retrieval ◽  
Statistical Testing ◽  
Neural Network Classifier ◽  
Statistical Features ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Efficiency And Effectiveness ◽  
F Measure ◽  
Skewness And Kurtosis

Image retrieval based on content not only relies heavily upon the type of descriptors, but on the steps taken further. This has been an extensively utilized methodology for finding and fetching out images from the big database of images. Nowadays, a number of methodologies have been organized to increase the CBIR performance. This has an ability to recover pictures relying upon their graphical information. In the proposed method, Neuro-Fuzzy classifier and Deep Neural Network classifier are used to classify the pictures from a given dataset. The proposed approach obtained the highest accuracy in terms of Precision, Recall, and F-measure. To show the efficiency and effectiveness of proposed approach, statistical testing is used in terms of standard deviation, skewness, and kurtosis. The results reveal that the proposed algorithm outperforms other approaches using low computational efforts.

scholarly journals A Class of Neuro-Computational Methods for Assamese Fricative Classification

2015 ◽  
Vol 5 (1) ◽  
pp. 59-70 ◽  
Author(s):  
Chayashree Patgiri ◽  
Mousmita Sarma ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Linear Prediction ◽  
Fuzzy Inference ◽  
Self Organizing Map ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Feature Sets ◽  
Neuro Fuzzy ◽  
And Performance ◽  
Assamese Language

Abstract In this work, a class of neuro-computational classifiers are used for classification of fricative phonemes of Assamese language. Initially, a Recurrent Neural Network (RNN) based classifier is used for classification. Later, another neuro fuzzy classifier is used for classification. We have used two different feature sets for the work, one using the specific acoustic-phonetic characteristics and another temporal attributes using linear prediction cepstral coefficients (LPCC) and a Self Organizing Map (SOM). Here, we present the experimental details and performance difference obtained by replacing the RNN based classifier with an adaptive neuro fuzzy inference system (ANFIS) based block for both the feature sets to recognize Assamese fricative sounds.

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj
Keyword(s):  
Intravascular Ultrasound ◽  
Human Error ◽  
Human Life ◽  
Classification Performance ◽  
Atheromatous Plaque ◽  
Ultrasound Images ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Calcified Tissues

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.

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