Venue : Coral Deira - Dubai, Deira, Dubai, UAE..  &  Date : April 24 ~ 25, 2015

Accepted papers

  • Hysteresis Strategy for Multipoint Link Sensing in Wireless Sensor Networkslink State Protocol using Default Forwarding Algorithm
    S. Vijayanad,T. Bhaskara Reddy, B.G. Prashanthi,Sri Krishnadevaraya University,India.
    Empowering multiple minuscule sensors to communicate with one another in the vicinity wirelessly in ad hoc manner for fulfilling certain spatial as well as time-centric requirements with all precision and perfection has been a hot topic for research among worldwide networking professionals. However there are several flaws and drawbacks. In this paper, we have described available Hysteresis Strategy for effective multi point link sensing in the link state protocol for next-generation wireless sensor networks (WSNs). We have derived this pragmatic strategy using the default forwarding algorithm.
  • Image Encryption Using User Private Key Generation
    G. Thippanna1 and T. Bhaskara Reddy,Sri Krishnadevaraya University,India.
    With the increasing applications of image processing techniques is various areas contrast image masking methods. Among the availability of image masking methods is encryption with different keys is the one of the most efficient approach. This entitles “Image Encryption Using Used defined Private Key” work to encrypt the image. In this work we make a random numbers and few operations applies on images we get Masking images in the form of image encryption. Finally the Experiments are conducted to demonstrate the feasibility of the security is providing to the image in network security.
  • Classification of Brain Tissues Using A Hybrid Model Based on FCMPSO algorithm and Possibility Theory Context
    Lamiche Chaabane,M?sila University,Algeria.
    In this research work, we propose an unsupervised method for the classification of the human brain tissues. The developed approach is based on particle swarm optimization (PSO) algorithm, fuzzy c-means clustering algorithm (FCM) and some of ideas of the possibility theory context. The fusion methodology is decomposed into three fundamental phases. We modeling information coming from T2 and PD weighted images in a common framework, in this step an hybridization between PSO and FCM algorithms is used. In the second phase, we combine the extracted data by an operator of fusion. Finally, an image of fusion is generated when a decision rule is applied. To validate the effectiveness of the proposed method, some results are presented using a set of simulated MRI image.
  • Detection and Classification of Mammogram Images Using Principle Component Analysis and Lazy Classifiers
    Rajkumar K.K,Kannur University,India
    Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.