Data Fault Detection Using Minimum Redundancy Maximum Relevance in Combination with Support Vector Article Swipe
indira priya
,
S. Muthurajkumar
,
Sheeba Daisy S
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1829920/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1829920/v1
Wireless Sensor Network (WSN) refers to a network of devices that can communicate the informa- tion gathered from a monitored field through wireless links. WSNs measures environmental conditions like temperature, sound, pollution levels, humidity, wind, etc. WSN’s data fault detection is a challenging problem due to presence of sensors in unpredictable areas. The data fault detection has to be precise and accurate, so that it can be used for weather prediction, disease prediction etc. In recent years, Machine Learning plays a vital role in accurate fault Detection. To make the detection process accurate it is mandatory to reduce the number of input features to the Support Vector Machine (SVM). Minimum Redundancy Maximum Relevance (MRMR) which is an efficient feature selection algorithm is proposed in this work. The feature set is used to train the Support Vector Machine (SVM) to detect data faults. The data fault detection accuracy is calculated which shows the accuracy of 92.9% for smaller test data and accuracy of 97.2% for larger test data.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1829920/v1
- https://www.researchsquare.com/article/rs-1829920/latest.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285726463
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285726463Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-1829920/v1Digital Object Identifier
- Title
-
Data Fault Detection Using Minimum Redundancy Maximum Relevance in Combination with Support VectorWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-18Full publication date if available
- Authors
-
indira priya, S. Muthurajkumar, Sheeba Daisy SList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-1829920/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-1829920/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-1829920/latest.pdfDirect OA link when available
- Concepts
-
Redundancy (engineering), Relevance (law), Computer science, Support vector machine, Data mining, Fault detection and isolation, Pattern recognition (psychology), Artificial intelligence, Reliability engineering, Engineering, Political science, Operating system, Law, ActuatorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.calculated | 149 |
| abstract_inverted_index.conditions | 28 |
| abstract_inverted_index.prediction | 73 |
| abstract_inverted_index.challenging | 43 |
| abstract_inverted_index.communicate | 13 |
| abstract_inverted_index.prediction, | 71 |
| abstract_inverted_index.temperature, | 30 |
| abstract_inverted_index.environmental | 27 |
| abstract_inverted_index.unpredictable | 51 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.63391618 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |