Dynamic Compilation of Pattern based clustering and Volumetric Probabilistic Mining for Network Routing in Cognitive Radio Sensor Networks Article Swipe
Kumaresh Sheelavant
,
R. Sumathi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.17485/ijst/v14i41.1838
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.17485/ijst/v14i41.1838
Objectives: The key objective is to investigate the current state of the art in web service prediction systems and also to improve the retrieving process with improved accuracy and to reduce the searching time. As well as to enhance the performance of data validation, quality of services and speed of the process. Method: In this study, an advanced model of the Dynamic Compilation of Pattern (DCP) method with a Lexical Subgroup (LS) system was used to estimate the similarity between the request data and the entire network. These are all indexed and grouped as a cluster to form a paged format of network structure which can reduce the computation time during the searching period. Also, with the help of prediction, the relevancy of feature attributes in the network is predicted, and the matching index is sorted to provide the recommended data for given request data. This was achieved by using Volumetric Probabilistic Mining (VPM). Findings: The performance of the proposed DCP-VPM is proved through extensive simulations and compared to those of the state-of-the-art methods. On the average, it is realized that the DCP-VPM always outperforms EACRP, ERP, ESUCR and ESAC related to minimizing average energy consumption, packet delivery ratio, end-to-end delay at different number of clusters by 10.2%, 18.6%, 11.3%, 12.5% compared to EACRP, ERP, ESUCR and ESAC respectively. Proposed cluster-based routing technique out performs all other routing techniques. Novelty: Route based request prediction system was focused to predict and analyse data from the network. That is why enhanced clustering, distance-based similarity and retrieving mechanism are used. Irrelevant parameters removal and ordering are performed using DCP with LS system. Then nodes are processed for learning model using VPM prediction model. Finally, as the recommended result for the routing application, the matched data that is related to the request input is listed. Keywords: Dynamic Compilation of Pattern; Lexical Subgroup system; Volumetric Probabilistic Mining; Request prediction; Cognitive Radio
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17485/ijst/v14i41.1838
- https://sciresol.s3.us-east-2.amazonaws.com/IJST/Articles/2021/Issue-41/IJST-2021-1838.pdf
- OA Status
- diamond
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200596409
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200596409Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.17485/ijst/v14i41.1838Digital Object Identifier
- Title
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Dynamic Compilation of Pattern based clustering and Volumetric Probabilistic Mining for Network Routing in Cognitive Radio Sensor NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-03Full publication date if available
- Authors
-
Kumaresh Sheelavant, R. SumathiList of authors in order
- Landing page
-
https://doi.org/10.17485/ijst/v14i41.1838Publisher landing page
- PDF URL
-
https://sciresol.s3.us-east-2.amazonaws.com/IJST/Articles/2021/Issue-41/IJST-2021-1838.pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://sciresol.s3.us-east-2.amazonaws.com/IJST/Articles/2021/Issue-41/IJST-2021-1838.pdfDirect OA link when available
- Concepts
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Computer science, Data mining, Cluster analysis, Probabilistic logic, Routing (electronic design automation), Network packet, Key (lock), Artificial intelligence, Computer network, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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27Number 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.result | 278 |
| abstract_inverted_index.sorted | 133 |
| abstract_inverted_index.study, | 53 |
| abstract_inverted_index.system | 70, 228 |
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| abstract_inverted_index.Lexical | 67, 299 |
| abstract_inverted_index.Mining; | 304 |
| abstract_inverted_index.Pattern | 62 |
| abstract_inverted_index.Request | 305 |
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| abstract_inverted_index.improve | 21 |
| abstract_inverted_index.indexed | 88 |
| abstract_inverted_index.matched | 284 |
| abstract_inverted_index.network | 100, 125 |
| abstract_inverted_index.period. | 111 |
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| abstract_inverted_index.quality | 44 |
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| abstract_inverted_index.removal | 253 |
| abstract_inverted_index.request | 79, 141, 226, 291 |
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| abstract_inverted_index.service | 15 |
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| abstract_inverted_index.system; | 301 |
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| abstract_inverted_index.Finally, | 274 |
| abstract_inverted_index.Pattern; | 298 |
| abstract_inverted_index.Proposed | 215 |
| abstract_inverted_index.Subgroup | 68, 300 |
| abstract_inverted_index.accuracy | 27 |
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| abstract_inverted_index.advanced | 55 |
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| abstract_inverted_index.clusters | 201 |
| abstract_inverted_index.compared | 163, 207 |
| abstract_inverted_index.delivery | 193 |
| abstract_inverted_index.enhanced | 242 |
| abstract_inverted_index.estimate | 74 |
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| abstract_inverted_index.matching | 130 |
| abstract_inverted_index.methods. | 169 |
| abstract_inverted_index.network. | 84, 238 |
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| abstract_inverted_index.realized | 175 |
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| abstract_inverted_index.extensive | 160 |
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| abstract_inverted_index.searching | 32, 110 |
| abstract_inverted_index.structure | 101 |
| abstract_inverted_index.technique | 218 |
| abstract_inverted_index.Irrelevant | 251 |
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| abstract_inverted_index.attributes | 122 |
| abstract_inverted_index.end-to-end | 195 |
| abstract_inverted_index.minimizing | 188 |
| abstract_inverted_index.parameters | 252 |
| abstract_inverted_index.predicted, | 127 |
| abstract_inverted_index.prediction | 16, 227, 272 |
| abstract_inverted_index.retrieving | 23, 247 |
| abstract_inverted_index.similarity | 76, 245 |
| abstract_inverted_index.<div> | 0 |
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| abstract_inverted_index.clustering, | 243 |
| abstract_inverted_index.computation | 106 |
| abstract_inverted_index.investigate | 6 |
| abstract_inverted_index.outperforms | 180 |
| abstract_inverted_index.performance | 40, 152 |
| abstract_inverted_index.prediction, | 117 |
| abstract_inverted_index.prediction; | 306 |
| abstract_inverted_index.recommended | 137, 277 |
| abstract_inverted_index.simulations | 161 |
| abstract_inverted_index.validation, | 43 |
| abstract_inverted_index.</div> | 309 |
| abstract_inverted_index.application, | 282 |
| abstract_inverted_index.consumption, | 191 |
| abstract_inverted_index.Probabilistic | 149, 303 |
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| abstract_inverted_index.respectively. | 214 |
| abstract_inverted_index.distance-based | 244 |
| abstract_inverted_index.Radio</p> | 308 |
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| abstract_inverted_index.listed.</p> | 294 |
| abstract_inverted_index.<div></div> | 310 |
| abstract_inverted_index.<div> </div> | 311 |
| abstract_inverted_index.<p><strong>Objectives:</strong> The | 1 |
| abstract_inverted_index.process. <strong>Method:</strong> In | 51 |
| abstract_inverted_index.(VPM). <strong>Findings:</strong> The | 151 |
| abstract_inverted_index.<p><strong>Keywords:</strong> Dynamic | 295 |
| abstract_inverted_index.techniques. <strong>Novelty:</strong> Route | 224 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.9100000262260437 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.23231351 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |