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Lakshmi Devi, R.
- A Survey on Resource Management in Cloud Computing Using Ontology Approach
Abstract Views :169 |
PDF Views:3
Authors
Affiliations
1 Research and Development Center, Bharathiar University, Coimbatore, IN
2 Department of Computer Science and Engineering, RVS College of Engineering and Technology, Sulur, IN
1 Research and Development Center, Bharathiar University, Coimbatore, IN
2 Department of Computer Science and Engineering, RVS College of Engineering and Technology, Sulur, IN
Source
Networking and Communication Engineering, Vol 3, No 14 (2011), Pagination: 915-919Abstract
Cloud computing is a new computing technology of this Internet World. Clouds are a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services) and accessed via standard protocols and interface with respect to the cloud architecture. Aggregating and monitoring these resources and matching suitable resources for the application become a challenging issue. This paper suggests a semantic component in the cloud architecture to support ontology-based representation and facilitates context-based information retrieval that complements cloud schedulers for effective resource management.Keywords
Cloud Computing, Semantics, Ontology, Resource Management, Resource Allocation.
- An Efficient Unsupervised Clustered Adaptive Antihub Technique for Outlier Detection in High Dimensional Data
Abstract Views :164 |
PDF Views:0
Authors
Affiliations
1 Mother Teresa Women’s University, Kodaikanal, IN
2 Department of Computer Science, Sri Vasavi College, Erode, IN
1 Mother Teresa Women’s University, Kodaikanal, IN
2 Department of Computer Science, Sri Vasavi College, Erode, IN
Source
Indian Journal of Science and Technology, Vol 9, No 19 (2016), Pagination:Abstract
Objective: The objective of this paper is to find the inconsistent objects in data which has high dimension through reduced computation time and increased accuracy. Methods: Hubness specifically Antihubs (points that rarely occur in k nearest neighbor lists) is the newly recognized concept for handling data which has high dimension. The advanced version of Antihub is Antihub2 which is for reconsidering the outlier score of a point obtained by the Antihub method. However, regarding computation time, Antihub2 runs slower. This paper institutes an approach called AdaptiveAntihub2Clust, which is a clustered Adaptive Antihub technique for unsupervised outlier detection to reduce computation time and to improve the accuracy. Findings: The results of an existing Antihub2 method is compared with the proposed method called AdaptiveAntihub2Clust. The experimental results elucidate that AdaptiveAntihub2Clust outperforms well than Antihub2 and also resolved that there is not only a substantial decrease in computation time but also progress in accuracy occurred while the newly built approach is practically used for finding outliers. Applications: The irrelevant objects may ascend due to numerous faults. Detection of such objects identifies the mistakes and fraud before they deteriorate with terrible significances and cleanses the data for further processing.Keywords
Adaptive Antihub, Antihub, Antihub2, Outliers, Unsupervised.Full Text
- A Neural Network based Cardiac Arrhythmia Diagnosis system from Dynamic Features of Electrocardiogram Signal
Abstract Views :256 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, National Engineering College, Kovilpatti, Nallatinputhur – 628503, Tamil Nadu, IN
1 Department of Computer Science and Engineering, National Engineering College, Kovilpatti, Nallatinputhur – 628503, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 11, No 43 (2018), Pagination: 1-9Abstract
Objectives: Cardiac arrhythmia is a type of disorder where the heartbeat is irregular, too slow, or too fast. As a result of heart diseases, there is an increase in death yearly. The early detection of cardiac diseases is important for preventing the deaths due to the cardiac diseases. Methods: The Electrocardiogram (ECG) is used to record the electrical activity of the heart for physician to diagnose the heart diseases. In this study, we propose a cardiac diagnosis system for diagnosing cardiac arrhythmia disease. It will be most helpful for the patients who undergone a heart surgery for continuous monitoring of post-surgical status. Findings: The major objective of this paper is to implement an effective algorithm to discriminate between the normal and diseased persons. The Monitoring process includes the following tasks, such as preprocessing and feature extraction by Pan Tompkin’s algorithm and the features are classified using neural network and support vector machine. The performance of the classifiers was evaluated using the parameters such as sensitivity, specificity and accuracy. The Accuracy of the neural network algorithm is 88.54% and the accuracy of the Support Vector Machine is 84.37%. Application/Improvements: The Neural network classifier shows better performance compared to support vector machine. In future the classifier is trained using the best set of features using feature selection techniques. *Full Text
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