Download Advances in Intelligent Informatics by El-Sayed M. El-Alfy, Sabu M. Thampi, Hideyuki Takagi, Selwyn PDF

By El-Sayed M. El-Alfy, Sabu M. Thampi, Hideyuki Takagi, Selwyn Piramuthu, Thomas Hanne

This publication encompasses a choice of refereed and revised papers of clever Informatics song initially awarded on the 3rd foreign Symposium on clever Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers chosen for this song conceal numerous clever informatics and comparable themes together with sign processing, development acceptance, photograph processing facts mining and their applications.

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During the training phase the SVM classifier is trained with a training data set which contains feature vectors extracted from the training images and their respective class labels. During the testing phase if an unknown image’s feature vector is given as an input to the trained classifier, it classifies the test image as belonging to one of classes [18]. Experiments are conducted with support vector machine classifier using quadratic kernel function and RBF kernel function. 1 Experimental Results Data Set Used The images for this work are taken from Cancer imaging archive [22].

3 Classification Classification is the next step after feature extraction and it is a supervised learning procedure. It involves two steps training and testing. During the training phase, the classifier is trained with features from training images. In testing phase, an unknown image’s features are given to the classifier and it has to classify the image as ‘tumor affected’ or ‘tumor not affected’. In this project two classifier models are used namely Support vector machine and neural network. S.

It is also worth noting that ridgelet coefficients include the information about the smoothness in t and φ of the Radon transform. Radon transform exhibits a certain degree of smoothness in particular; we can instantly see that the ridgelet coefficients decay rapidly as φ and/or j moves away from the singularities (kindly refer equation 7). 3 Classification Classification also plays a very important role in face recognition [23]. For classification, we use five distance measures such as: Weighted angle based Distance, Weighted Modified Manhattan Distance, Minkowski Distance, Canberra Distance, and Mahalanobis Distance to obtain a mean classification rate.

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