Fog computing based information classification in sensor cloud-agent approach

Sutagundar, Ashok V.; Sangulagi, Prashant

doi:10.1016/j.eswa.2021.115232

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…It can be seen from Table II that under different dimensions, the lowest RAND index of this method is 0.96, close to 1. The RAND index of literature [6] method and literature [7] method is 0.89 and 0.88 respectively, which is far lower than that of this method. This shows that the adaptive division accuracy of this method is the highest and has certain applicability.…”

Section: Networkmentioning

confidence: 67%

“…In order to further verify the performance of the algorithm studied in this paper, the algorithm in the literature [6] and the algorithm in the literature [7] introduced in the introduction are used as the comparison method to test the RAND index results in different dimensions, and the test is repeated five times. The results are shown in Table II.…”

Section: Networkmentioning

confidence: 99%

“…Using random forest classifier and genetic algorithm to divide the information and introducing Agent paradigm, not only saved the energy of physical sensor nodes, but also could quickly analyze and divide the information on the fog server. The results show that the algorithm has better effect in partition accuracy, delay and energy consumption [6]; Flisar, J., et al used DBpedia ontology knowledge base to divide information. This algorithm can effectively divide information sets [7], but it cannot adapt to all data distribution well.…”

Section: Introductionmentioning

confidence: 99%

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Information Classification Algorithm based on Project-based Learning Data-driven and Stochastic Grid

Qin¹,

Zhang²

2022

IJACSA

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The adaptive partitioning algorithm of information set in simulation laboratory based on project-based learning data-driven and random grid is studied to effectively preprocess the information set and improve the adaptive partitioning effect of the information set. Using the improved fuzzy C-means clustering algorithm driven by project-based learning data, the fuzzy partition of information set in simulation laboratory is carried out to complete preprocessing of information set; The pre-processing information set space is roughly divided by the grid partitioning algorithm based on the data histogram; A random mesh generation algorithm based on uniformity is used to finely divide the coarse mesh cells; Taking the representative points of grid cells as the clustering center, the pre-processing information set is clustered by the density peak clustering algorithm to complete the adaptive partitioning of the information set in simulation laboratory. Experimental results show that this algorithm can effectively preprocess and adaptively partition the information set of simulation laboratory; For different dimension information sets, the evaluation index values of Rand index, Purity, standard mutual information, interval and Dunn index of the algorithm are all high, and the evaluation index values of compactness and Davidson's banding index are all low, so the algorithm has a high accuracy of adaptive partitioning of information sets.

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Section: Networkmentioning

confidence: 67%

Section: Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Information Classification Algorithm based on Project-based Learning Data-driven and Stochastic Grid

Qin¹,

Zhang²

2022

IJACSA

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“…Obviously, type-II right censoring method the study adopted allowed investigators to make the best of eighty samples out of one hundred valid ones to conduct the survey, which saved twenty percent of experimental testing time and its cost. Therefore, the result was totally correspondent with the needs of pursuing enterprises' requirements for prompt responses and efficiently making wise decision in no time [12,27].…”

Section: Numerical Examplementioning

confidence: 76%

Untitled

2021

EiN

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In practice, lifetime performance index CL has been a method commonly applied to the evaluation of quality performance. L is the upper or lower limit of the specification. The product lifetime distribution is mostly abnormal distribution. This study explored that the lifetime of commodities comes from exponential distribution. Complete data collection is the primary goal of analysis. However, the censoring type is one of the most commonly used methods due to considerations of manpower and material cost or the timeliness of product launch. This study adopted Type-II right censoring to find out the uniformly minimum variance unbiased (UMVU) estimator of the lifetime performance index CL and its probability density function. Afterward this study obtained the 100×(1-α)% confidence interval of the lifetime performance index CL as well as created the uniformly most powerful (UMP) test and the power of the test for the product lifetime performance index. Last, this study came up with a numerical example to demonstrate the suggested method as well as the application of the model.

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