An Ontology-Based Approach for IoT Data Processing Using Semantic Rules

Bali, Ahmed; Al-Osta, Mahmud; Gherbi, Abdelouahed

doi:10.1007/978-3-319-68015-6_5

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…Figure 9, 10, 11 and 12 shows the comparison of the results of different artificial machines with and without proposed system processing concerning nature, value, quality, ability, the significance of the services and performance. After balancing the trade-off between the artificial machines and labor performance capabilities, the proposed method is checked with other existing methods in terms of accuracy, time and throughput computation to check its feasibility against proposed separation based ontology arrangement of images by an ontology tree [23,31,33]. The result from Figs.…”

Section: Resultsmentioning

confidence: 99%

“…All those schemes and processing aspects are not ensuring the spotting of affected and unaffected area fruits. Hence proposed processes are described as the separation of affected and unaffected sekai-ichi apple fruit by using ontology-based Recognition and Isolation Guarantee detection factors [24,33,42]. At the same time, the spotting of affected sekai-ichi apple fruit location is somewhat Opposition preserving incompatible factors amongst segmentation and dimension extraction the spotting of unaffected sekai-ichi apple fruits.…”

Section: Boosted Continuous Non-spatial Whole Attribute Extraction (Bmentioning

confidence: 99%

“…Finally, the ontology level qualities and performance checking was performed for confirmation affected and unaffected sekai-ichi apple separation in the post-harvest [33,34]. The Boosted Continuous Non-spatial whole Attribute Extraction (BCNAE) is characterized by the following delimiter such as a non-spatial of an image, real-time spotting of a Sekai-ichi apple separation without loss of any authenticity.…”

Section: Introductionmentioning

confidence: 99%

“…The 3D sensor image comprises identifying the micro affected spot on a sekai-ichi apple with a continuous scanned image. In that consideration, this influenced distinguishes are named concerning recognition and isolation [33,38,39]. Finally, the optimized hierarchical model within ontology enabled IoT in intelligent agriculture for preventing postharvest losses is respond in a quick, clever humorous way of separation of affected and unaffected sekai-ichi apple based on the Smallest S-vertex tetragon based recognition and isolation guarantee spotting algorithm [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

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An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

Sanjeevi

Kumar

Prasanna

et al. 2020

Complex Intell. Syst.

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Constituting the agriculture solid substance manufacture, the post-harvest sector processing schema is direct to preventing reduce the losses in intelligent agriculture. Many processing schemata will be preventing post-harvest losses on the agriculture solid substance manufacture, especially sekai-ichi apple is the regularly used fruit also used to make active in human-related activities of the sensory and control function consisting of an agricultural industry. Sekai-ichi apple is being a definite number of diseases induce, but it is to the highest degree of wastage involving in the Post-Harvest process. Especially sekai-ichi apple count loss is an unsafe many time because it not critically post-harvest. Regardless of consideration, the existing hierarchical model specified post-harvest losses prevention research has deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. This paper suggests a "Hierarchical Model within Ontology Enabled IoT" for distinguishable healthy separation of sekai-ichi apple by using Boosted Continuous Non-spatial whole Attribute Extraction (BCNAE). Sekai-ichi apple count loss is always safe on critically post-harvest. Proposed Post-Harvest hierarchical model specified post-harvest losses prevention and deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. In these suggestions, the separation cognitive operation takes the three levels of processing schemes such as lower level, middle level, and higher level. Firstly, the lower level is express agreements with the dynamic functioning for maintaining the definite number of manual induces. This lower level showing an absorption with the activity of manual separation by the human reliability determination. Secondly, the middle level is an express arrangement with the dynamic functioning for reducing the overfitting and accommodate to fitting the right shape deliberation. Middle level is establishing being generalized by concentrating the time-varying features in the occurrence of a change for the worse identification. Finally, the upper level is express for features refining with the help of the function of sekai-ichi apple image segmentation connection. This interpretability process helps to make the proven position of a prominent classification in a particular fruit on the agriculture solid substance. These three processing flow constructs the ontology structure with manually collected sekai-ichi apple images from a 3D sensor. The observational consequences express that the proposed BCNAE framework recognizes a detection performance carrying out with an optimized-separation ratio for time-variant of the separation process.

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Section: Resultsmentioning

confidence: 99%

Section: Boosted Continuous Non-spatial Whole Attribute Extraction (Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

Sanjeevi

Kumar

Prasanna

et al. 2020

Complex Intell. Syst.

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“…In [14], the authors present a new approach which leverages semantic models and rules to enable selective data filtering to sending the cloud. They propose the use of Platform Independent Model, based on semantic web technologies to facilitate sharing and reusing semantic rules in IoT gateways.…”

Section: Research Background and Related Workmentioning

confidence: 99%

Big Data Management and Analytics Metamodel for IoT-Enabled Smart Buildings

et al. 2020

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Big data management and analytics, in the context of IoT (Internet of Things)-enabled smart buildings, is a challenging task. It is a diffused and complex area of knowledge due to the diversity of IoT devices and the nature of data generated by the IoT devices. Many international bodies have developed metamodels for IoT-enabled ecosystems to allow knowledge sharing. However, these are often narrow in focus and deal with only the IoT aspects without taking into account the management and analytics of big data generated by the IoT devices. Hence, in this paper we propose a metamodel for the Integrated Big Data Management and Analytics (IBDMA) framework for IoT-enabled smart buildings. The IBDMA Metamodel can be used to facilitate interoperability between existing big data management and analytics ecosystems deployed in smart buildings or other smart environments. We import the metamodel into a knowledge graph management tool and by considering a case study we validate the metamodel using this tool. The evaluation results demonstrate that IBDMA Metamodel is indeed suitable for its intended purpose.

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Methodology for Data Processing in Modular IoT System

Dineva

Atanasova

2019

Distributed Computer and Communication Networks

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An Ontology-Based Approach for IoT Data Processing Using Semantic Rules

Cited by 11 publications

References 12 publications

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

Big Data Management and Analytics Metamodel for IoT-Enabled Smart Buildings

Methodology for Data Processing in Modular IoT System

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