Informatics Publishing Limited

P. Aiswariya ¹, C. Bhuvaneswari ²

Affiliations
1 Mahavidyalayam Arts and Science College for Women, Ulundurpet, Tamil Nadu, IN
2 Department of Computer Science and Applications, Tiruvalluvar University College for Arts and Science, Thiruvennainallur, IN

Abstract

Carotid ultrasound plaque classification using a combination of .region of interest, discrete wavelet transform and neural networks are used to classify the symptomatic or asymptomatic ultrasound plaque images. The system involves three steps: 1) the preprocessing step using the ROI technique. 2) The method Feature extraction is done by averaging values and discrete wavelet transform.3) Classification using a Neural Network. In this work Multilayer feed-forward neural network is adapted for training and testing the images. The accuracy of 70% is obtained in proposed work using neural network classifier.

Keywords

Atherosclerosis, Carotid Ultrasound, Classification, Discrete Wavelet Transform (DWT), Region of Interest (ROI) Technique, Neural Network.

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C. Bhuvaneswari ¹, P. Aruna ¹, D. Loganathan ²

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, IN
2 Department of Computer Science and Engineering, Pondicherry Engineering College, IN

Abstract

Lung diseases are one of the most common diseases that affect the human community worldwide. When the diseases are not diagnosed they may lead to serious problems and may even lead to transience. As an outcome to assist the medical community this study helps in detecting some of the lung diseases specifically bronchitis, pneumonia and normal lung images. In this paper, to detect the lung diseases feature extraction is done by the proposed shape based methods, feature selection through the genetics algorithm and the images are classified by the classifier such as MLP-NN, KNN, Bayes Net classifiers and their performances are listed and compared. The shape features are extracted and selected from the input CT images using the image processing techniques and fed to the classifier for categorization. A total of 300 lung CT images were used, out of which 240 are used for training and 60 images were used for testing. Experimental results show that MLP-NN has an accuracy of 86.75 % KNN Classifier has an accuracy of 85.2 % and Bayes net has an accuracy of 83.4% of classification accuracy. The sensitivity, specificity, F-measures, PPV values for the various classifiers are also computed. This concludes that the MLP-NN outperforms all other classifiers.

Keywords

Feature Extraction, Multilayer Perceptron, Neural Networks, Bayes Net, Sensitivity, Specificity, F-Measure.

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K. Aiswarya ¹, C. Bhuvaneswari ¹, R. Sowmiya ¹

Affiliations
1 Department of Computer Applications, Pioneer College of Arts and Science, Jothipuram, IN

Abstract

In a nutshell, Ambient Computational Environments is the integration of computational resources into a robust, secure, and pervasive network where users can easily access and co-opt devices, services, and applications via spoken, graphical, or gesture commands. ACE is ubiquitous and accessing information and computational processing power is easy and fast, independent of the user’s location within the environment.An Ambient Computational Environment (ACE) is a set of Devices and services that allow any user to operate in the Environment without the usage of too many cumbersome devices attached to the users person. Security in such an infrastructure is a must for apparent reasons. This paper addresses the design and implementation issues related to securing an ACE, namely identification of a user and a service in the ACE and ensuring proper encrypted communications between users and services alike. A limited form of Public Key Infrastructure has been put in place in the ACE. The simulation results reveal that the proposed Authentication and secure communication is one of the best alternative in the ACE Infrastructure.

Keywords

Ambient Computational Environment (ACE), Host Resource Monitor (HRM's), Remote Connection Manager (RCM).

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