A fuzzy logic based neural network classifier for qualitative classification of odors/gases

Author(s):  
R. Kumar ◽  
R.R. Das ◽  
V.N. Mishra ◽  
R. Dwivedi
2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
I. Jasmine Selvakumari Jeya ◽  
S. N. Deepa

A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.


2017 ◽  
Vol 1 (4) ◽  
pp. 271-277 ◽  
Author(s):  
Abdullah Caliskan ◽  
Mehmet Emin Yuksel

Abstract In this study, a deep neural network classifier is proposed for the classification of coronary artery disease medical data sets. The proposed classifier is tested on reference CAD data sets from the literature and also compared with popular representative classification methods regarding its classification performance. Experimental results show that the deep neural network classifier offers much better accuracy, sensitivity and specificity rates when compared with other methods. The proposed method presents itself as an easily accessible and cost-effective alternative to currently existing methods used for the diagnosis of CAD and it can be applied for easily checking whether a given subject under examination has at least one occluded coronary artery or not.


Author(s):  
Brijesh Verma ◽  
Siddhivinayak Kulkarni

This chapter introduces neural networks for Content-Based Image Retrieval (CBIR) systems. It presents a critical literature review of both the traditional and neural network based techniques that are used in retrieving the images based on their content. It shows how neural networks and fuzzy logic can be used in interpretation of queries, feature extraction and classification of features by describing a detailed research methodology. It investigates a neural network based technique in conjunction with fuzzy logic to improve the overall performance of the CBIR systems. The results of the investigation on a benchmark database with a comparative analysis are presented in this chapter. The methodologies and results presented in this chapter will allow researchers to improve and compare their methods and it will also allow system developers to understand and implement the neural network and fuzzy logic based techniques for content based image retrieval.


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