By Sabu M. Thampi, El-Sayed M. El-Alfy, Hideyuki Takagi, Selwyn Piramuthu, Thomas Hanne
This e-book features a choice of refereed and revised papers of clever Informatics tune initially awarded on the 3rd foreign Symposium on clever Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers chosen for this tune hide numerous clever informatics and similar issues together with sign processing, trend acceptance, photograph processing, facts mining and their purposes.
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This publication constitutes the refereed lawsuits of the sixth overseas convention on Geographic details technological know-how, GIScience 2010, held in Zurich, Switzerland, in September 2010. The 22 revised complete papers provided have been conscientiously reviewed and chosen from 87 submissions. whereas conventional study issues equivalent to spatio-temporal representations, spatial relatives, interoperability, geographic databases, cartographic generalization, geographic visualization, navigation, spatial cognition, are alive and good in GIScience, learn on easy methods to deal with immense and quickly growing to be databases of dynamic space-time phenomena at fine-grained solution for instance, generated via sensor networks, has in actual fact emerged as a brand new and well known examine frontier within the box.
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Extra resources for Advances in Intelligent Informatics (Advances in Intelligent Systems and Computing, Volume 320)
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 . 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 .
S. Natteshan · J. edu © Springer International Publishing Switzerland 2015 El-Sayed M. El-Alfy et al. S. Natteshan and J. Angel Arul Jothi Introduction Brain tumor is a solid neoplasm of uncontrolled cell division. CAD is a process of using computation capacity effectively to diagnose a condition from medical images or other medical related data. CAD is mainly used to provide a second opinion thereby helping doctors while performing diagnosis. Image processing is a technique where the input is an image and the output being certain parameters related to image.
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. Natteshan and J. 1 Neural Network Classifier A Neural network classifier mimics the processing ability of biological brain. They are mainly divided into two types namely feed forward network and recurrent or feedback network.