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Lakshmi Devi, R.
- A Survey on Resource Management in Cloud Computing Using Ontology Approach
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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 :112 |
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 :178 |
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
References
- Cardiovascular diseases (CVDs) Fact sheet [Internet]. [cited 2017 May 17]. Available from: http://www.who.int/news-room/ fact-sheets/detail/cardiovascular-diseases-(cvds).
- Smartphone Use in 2015. Pew Research Center [Internet]. [cited 2017 Jun 28]. Available from: http://www.pewresearch.org/fact-tank/2017/06/28/10-facts-about-smartphones/.
- Hamed I, Owis MI. Automatic arrhythmia detection using support vector machine based on discrete wavelet transform. Journal of Medical Imaging and Health Informatics. 2016; 6:1–6. https://doi.org/10.1166/jmihi.2016.1611.
- Chao Li, Hu X, Zhang L. The IoT-based heart disease monitoring system for pervasive healthcare service. Procedia Computer Science. 2017; 112:2328–34. https://doi.org/10.1016/j.procs.2017.08.265.
- Azzawi MA, Hassan R, Abu Bakar KA. A Review on Internet of Things (IoT) in healthcare. International Journal of Applied Engineering Research, 2016; 11(20):10216–21.
- Caliskan A, Yuksel ME. Classification of coronary artery disease data sets by using a deep neural network. The Euro Biotech Journal. 2017; 1(4):271–7. https://doi.org/10.24190/ISSN2564-615X/2017/04.03.
- Dimitrov DV. Medical Internet of Things and big data in healthcare. Healthcare informatics Research. 2016; 22(3). https://doi.org/10.4258/hir.2016.22.3.156. PMid:27525156. PMCid:PMC4981575.
- Olaniyi EO, Oyedotun OK, Adnan K. Heart diseases diagnosis using neural networks arbitration. International Journal of Intelligent Systems and Applications. 2015; 12:75–82. https://doi.org/10.5815/ijisa.2015.12.08.
- Kim JK, Kang S. Neural network-based coronary heart disease risk prediction using feature correlation analysis. Journal of Healthcare Engineering. 2017. https://doi.org/10.1155/2017/2780501.
- Devi MN, Meena S, Balamurugan SA. Healthcare diagnosis by using computational intelligence algorithms. International Journal of Engineering Technology Science and Research. 2017; 4(11).
- Atkov OY, Gorokhova SG, Sboev AG, Generozov EV, Muraseyeva EV, Moroshkina SY, Cherniy NN. Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters. Journal of Cardiology. 2012; 59:190–4. https://doi.org/10.1016/j.jjcc.2011.11.005. PMid:22218324 .
- Awni Y. Hannun PR, Haghpanahi M, Bourn C, Ng AY. Cardiologist-level arrhythmia detection with convolution neural networks. Stanford; 2017.
- Pan J, Tompkins WJ. A real-time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering. 1985; 32(3):230–6. https://doi.org/10.1109/TBME.1985.325532. PMid:3997178 .
- Pecchia L, Melillo P, Sansone M, Bracale M. Discrimination power of short-term heart ratevariability measures for CHF assessment. IEEE Transactions on Information Technology in Biomedicine. 2011; 15 (1):40–6. https://doi.org/10.1109/TITB.2010.2091647. PMid:21075731 .
- Melillo P, Fusco R, Sansone M, Marcello Bracale, Leandro Pecchia, Discrimination power of long-term heart rate variability measures for chronic heart failure detection. Medical and Biological Engineering and Computing. 2011; 49:67–74. https://doi.org/10.1007/s11517-010-0728-5. PMid:21203855
- Narain R, Saxena S, Goyal AK. Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach. Patient Preference and Adherence; 2016. p. 1259–70. https://doi.org/10.2147/PPA.S108203. PMid:27486312. PMCid:PMC4958363.
- Kalaivani V, Devi RL, Devi VA. Phonocardiographic signal and electrocardiographic signal analysis for the detection of cardiovascular diseases. Biosciences Biotechnology Research Asia. 2018; 15(1):79–86. https://doi.org/10.13005/bbra/2610.
- Kalaivani V, Devi RL. Extraction of R-Peak from photoplethysmographic signal for diagnosis of cardiac diseases. International Journal of Pure and Applied Mathematics. 2018; 118(18):2979–84.
- Kalaivani V. Big data analysis for heart disease detection system using map reduce technique, IEEE digital library. IEEE proceedings on Computing Technologies and Intelligent Data Engineering; 2016.
- Kalaivani V. Diagnosis of arrhythmia diseases using heart sounds and ECG signals. Russian Journal of Cardiology. 2014; 1(105):35-41. https://doi.org/10.15829/1560-4071-2014-1-ENG-35-41.
- Kalaivani V. A remote healthcare monitoring system for faster identification of cardiac abnormalities from compressed ECG using advanced data mining approach. Lecture Notes in Electrical Engineering, Springer. 2013; 222:227–36. https://doi.org/10.1007/978-81-322-10009_21.