Noise Filtering for Big Data Analytics by Souvik Bhattacharyya
This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.
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Metaverse and Digital Twins
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Reengineering Cyber Security Process
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Operational Perspective of Modeling System Reliability
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Digital Blockchain
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Artificial Intelligence for Healthcare
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Distributed Transfer Function Method
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Machine Learning for Cyber Security
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Soft Computing
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Supply Chain Sustainability
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Linear Integer Programming
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Systems Reliability Engineering
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Mathematics for Reliability Engineering
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Computational Intelligence
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Distributed Denial of Service Attacks
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Mathematical Fluid Mechanics
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Systems Performance Modeling
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Integral Transforms and Applications
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Multiple Criteria Decision-Making Methods
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Artificial Intelligence for Signal Processing and Wireless Communication
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Meta-heuristic Optimization Techniques
Souvik Bhattacharyya, Koushik Ghosh, University of Burdwan,West Bengal, India.
| SKU | Unavailable |
| ISBN 13 | 9783110697094 |
| ISBN 10 | 3110697092 |
| Title | Noise Filtering for Big Data Analytics |
| Author | Souvik Bhattacharyya |
| Series | De Gruyter Series On The Applications Of Mathematics In Engineering And Information Sciences |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | De Gruyter |
| Year published | 2022-06-21 |
| Number of pages | 164 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |