Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction Article Swipe
S. Karthik
,
Robin Singh Bhadoria
,
Jeong-Gon Lee
,
Arun Kumar Sivaraman
,
Sovan Samanta
,
A. Balasundaram
,
Brijesh Kumar Chaurasia
,
K. K. Thyagharajan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2022.023864
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2022.023864
Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data. The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables. This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach. It also demonstrates the generative function for Kalman-filer based prediction model and its observations. This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration (CPE) for Python.
Related Topics
Concepts
Computer science
Python (programming language)
Machine learning
Kalman filter
Probabilistic logic
Artificial intelligence
Bayesian programming
Bayesian probability
Data mining
Bayesian inference
Algorithm
Bayesian statistics
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2022.023864
- https://www.techscience.com/cmc/v72n1/46868/pdf
- OA Status
- diamond
- Cited By
- 24
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4213450216
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4213450216Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/cmc.2022.023864Digital Object Identifier
- Title
-
Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy PredictionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
S. Karthik, Robin Singh Bhadoria, Jeong-Gon Lee, Arun Kumar Sivaraman, Sovan Samanta, A. Balasundaram, Brijesh Kumar Chaurasia, K. K. ThyagharajanList of authors in order
- Landing page
-
https://doi.org/10.32604/cmc.2022.023864Publisher landing page
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https://www.techscience.com/cmc/v72n1/46868/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
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-
diamondOpen access status per OpenAlex
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https://www.techscience.com/cmc/v72n1/46868/pdfDirect OA link when available
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Computer science, Python (programming language), Machine learning, Kalman filter, Probabilistic logic, Artificial intelligence, Bayesian programming, Bayesian probability, Data mining, Bayesian inference, Algorithm, Bayesian statistics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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24Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2023: 11, 2022: 10Per-year citation counts (last 5 years)
- References (count)
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63Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.helps | 20 |
| abstract_inverted_index.issue | 5 |
| abstract_inverted_index.least | 47 |
| abstract_inverted_index.model | 107, 147 |
| abstract_inverted_index.noise | 114 |
| abstract_inverted_index.other | 116 |
| abstract_inverted_index.paper | 18, 56, 152 |
| abstract_inverted_index.piece | 169 |
| abstract_inverted_index.small | 81 |
| abstract_inverted_index.using | 67, 118, 126, 156 |
| abstract_inverted_index.Common | 173 |
| abstract_inverted_index.Filter | 72 |
| abstract_inverted_index.Kalman | 71 |
| abstract_inverted_index.Python | 161 |
| abstract_inverted_index.always | 2 |
| abstract_inverted_index.during | 9 |
| abstract_inverted_index.method | 59, 76 |
| abstract_inverted_index.reduce | 93 |
| abstract_inverted_index.source | 158 |
| abstract_inverted_index.Python. | 178 |
| abstract_inverted_index.applied | 78 |
| abstract_inverted_index.concern | 7 |
| abstract_inverted_index.crucial | 4 |
| abstract_inverted_index.dataset | 82 |
| abstract_inverted_index.digital | 15 |
| abstract_inverted_index.exactly | 19 |
| abstract_inverted_index.machine | 128 |
| abstract_inverted_index.modules | 167 |
| abstract_inverted_index.unknown | 119 |
| abstract_inverted_index.Bayesian | 38, 65, 104, 134 |
| abstract_inverted_index.K-means. | 74 |
| abstract_inverted_index.Platform | 174 |
| abstract_inverted_index.accurate | 27 |
| abstract_inverted_index.analysis | 39 |
| abstract_inverted_index.clusters | 99 |
| abstract_inverted_index.function | 142 |
| abstract_inverted_index.learning | 24, 105, 129 |
| abstract_inverted_index.platform | 159 |
| abstract_inverted_index.presents | 57 |
| abstract_inverted_index.proposed | 90, 103 |
| abstract_inverted_index.reducing | 30 |
| abstract_inverted_index.scenario | 122 |
| abstract_inverted_index.algorithm | 91, 130, 155 |
| abstract_inverted_index.approach. | 136 |
| abstract_inverted_index.computing | 97 |
| abstract_inverted_index.different | 166 |
| abstract_inverted_index.discusses | 36 |
| abstract_inverted_index.efficient | 23, 127 |
| abstract_inverted_index.improving | 61 |
| abstract_inverted_index.mechanism | 25 |
| abstract_inverted_index.providing | 22 |
| abstract_inverted_index.redundant | 31 |
| abstract_inverted_index.especially | 8 |
| abstract_inverted_index.generative | 141 |
| abstract_inverted_index.implements | 153 |
| abstract_inverted_index.integrates | 164 |
| abstract_inverted_index.parametric | 49 |
| abstract_inverted_index.perpetuate | 132 |
| abstract_inverted_index.prediction | 11, 146 |
| abstract_inverted_index.variables. | 120 |
| abstract_inverted_index.Enumeration | 175 |
| abstract_inverted_index.combination | 69 |
| abstract_inverted_index.computation | 13 |
| abstract_inverted_index.conditional | 43 |
| abstract_inverted_index.efficiently | 163 |
| abstract_inverted_index.implemented | 125 |
| abstract_inverted_index.performance | 63 |
| abstract_inverted_index.predictions | 51 |
| abstract_inverted_index.probability | 44 |
| abstract_inverted_index.revolution. | 16 |
| abstract_inverted_index.statistical | 113 |
| abstract_inverted_index.Kalman-filer | 144 |
| abstract_inverted_index.demonstrates | 139 |
| abstract_inverted_index.establishing | 85 |
| abstract_inverted_index.inaccuracies | 117 |
| abstract_inverted_index.observations. | 150 |
| abstract_inverted_index.probabilistic | 106, 135 |
| abstract_inverted_index.classification | 66 |
| abstract_inverted_index.communication. | 33 |
| abstract_inverted_index.predictability | 28 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.94334476 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |