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2. Basics of Cloud Management

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2023
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6. Cloud Networking

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2023
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8. Cloud Security

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2023
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20. DNA: Dynamically Negotiable Approach—A P2P-based Overlay for Live Multimedia Streaming

Author

Evangeline, Preetha, Palanisamy, Anandhakumar, Chelliah, Pethuru Raj, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Peter, J. Dinesh, editor, Fernandes, Steven L., editor, and Alavi, Amir H., editor

Published
2021
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22. Disaster Recovery

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Gries, David, Series Editor, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2019
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23. Basics of Cloud Management

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Gries, David, Series Editor, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2019
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24. Cloud Monitoring

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Gries, David, Series Editor, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2019
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25. Cloud Security

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Gries, David, Series Editor, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2019
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27. Cloud Networking

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Gries, David, Series Editor, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2019
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29. Fundamentals of Cloud Computing

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Gries, David, Series Editor, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2019
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30. Introduction to Cloud Computing

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Gries, David, Series Editor, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2019
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31. Cloud Service Brokerage

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Gries, David, Series Editor, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2019
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32. Cloud Orchestration

Author

Surianarayanan, Chellammal, Chelliah, Pethuru Raj, Gries, David, Series Editor, Hazzan, Orit, Series Editor, Surianarayanan, Chellammal, and Chelliah, Pethuru Raj

Published
2019
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44. A high-throughput architecture for anomaly detection in streaming data using machine learning algorithms

Author

Surianarayanan, Chellammal, Kunasekaran, Saranya, and Chelliah, Pethuru Raj

Abstract

Detection of anomaly in streaming data requires continuous analysis of the stream in real time. This process turns out to be difficult due to varied volume and velocity of data streams. The purpose of this work is to propose a streaming architecture capable of consuming bulky and high speed streams and ingesting them for continuous anomaly detection using machine learning algorithms. A high-throughput architecture has been created by leveraging the proven publish-subscribe model. The publisher is designed to discretize the incoming stream and to ingest the discretized streams to two subscribers which are designed to work alternately with discretized streams for continuous machine learning based anomaly detection. The inherent computation time associated with machine learning algorithm for learning and detection of anomaly is addressed by using two subscribers. The architecture is implemented using Apache Kafka which is a distributed messaging system, capable of communicating high volume of data from one end-point to another. Detection of anomalies is performed using Random Forest (RF) algorithm with an extended, threshold-value based point anomaly detection method. While checking for presence of anomalies in the instances of target attribute, the instances of its strongly correlated attributes (if any) are also tested for anomaly to provide additional knowledge. The proposed architecture has been tested with a case study where streams having different speeds and sizes are ingested to subscribers and the performance of the algorithm has been analyzed in terms of accuracy, computation time and data-miss, in two different configurations of the architecture, namely, single node and distributed node.

Published
2024
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48. ILLUSTRATING A SCALABLE ARCHITECTURE-POWERED DISEASE PREDICTION USING MACHINE LEARNING TECHNIQUES.

Author

Surianarayanan, Chellammal, Rengasamy, Sharmila, Nirmala, M. Baby, and Chelliah, Pethuru Raj

Subjects
SCALABILITY, MACHINE learning, DATA warehousing, DECISION making, MACHINE performance, HEART diseases, FORECASTING, FEATURE selection
Abstract

Healthcare information systems typically collect, store and manage various kinds of data such as illness details, clinical history, essential body parameters, health insurance plans, and other related data towards enabling data processing and analytics to arrive at better decision making with all the clarity and alacrity. To reduce the mortality rate due to heart diseases, it is essential to predict the presence of disease in its budding stage itself. Manual extraction of the useful knowledge from historical data is practically tedious and timeconsuming. Machine learning (ML) algorithms are being used to detect and predict something useful out of both historical and current data. Despite the applicability of machine learning algorithms for prediction, the accuracy of prediction is significantly influenced by features used for prediction. Moreover, to meet the needs of evolving data sizes, suitable technologies for data storage also become essential. Based on these two aspects, a comparative analysis has been performed for feature selection using four filter methods, namely, correlation measure, information gain, gain ratio and relief. Further, a scalable architecture using Hadoop framework has been proposed to enable the machine learning algorithms to handle larger datasets while performing prediction task. The impact of the proposed architecture on the performance of machine learning algorithm has been evaluated with benchmark dataset and found to have improved scalability and accuracy. [ABSTRACT FROM AUTHOR]

Published
2023
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