Neural network based data validation algorithm for pressure processes

Mounika, B.; Raghu, G Raj; Sreelekha, Surabhi; Jeyanthi, R.

doi:10.1109/iccicct.2014.6993147

Cited by 6 publications

(2 citation statements)

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“…SEVA are widely researched in literature. An example, using a Back-Propagation (BP) model, is applied into a system to obtain an estimated value and then a fault detection method called SPRT (sequential probability ratio test), identifying the validity of the system [33]. For the use of SEVA technologies, the authors of [34] also proposed the validated random fuzzy variable (VRFV) based uncertainty evaluation strategy for the online validated uncertainty (VU) estimation.…”

Section: Data Validation Methodsmentioning

confidence: 99%

Validation Techniques for Sensor Data in Mobile Health Applications

et al. 2016

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Mobile applications have become a must in every user’s smart device, and many of these applications make use of the device sensors’ to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the applications use can be relied upon and, therefore, to which extent the output of a given application is trustworthy or not. To help developers and researchers and to provide a common ground of data validation algorithms and techniques, this paper presents a review of the most commonly used data validation algorithms, along with its usage scenarios, and proposes a classification for these algorithms. This paper also discusses the process of achieving statistical significance and trust for the desired output.

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Section: Data Validation Methodsmentioning

confidence: 99%

Validation Techniques for Sensor Data in Mobile Health Applications

et al. 2016

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“…In this paper, we describe a back-propagation neural network model that was developed to estimate the BSU units CPU load [12]. This work was done as part of an internship project at Nokia Solutions and Networks and hence, the model is specific to the Nokia BSC.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model For Gsm Bsc Control Plane Units

Aswathy¹,

Saha²,

Pardhasaradhi³

2019

IJEAT

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At maximum traffic intensity i.e. during the busy hour, the GSM BSC signalling units (BSU) measured CPU load will be at its peak. The BSUs CPU load is a function of the number of transceivers (TRXs) mapped to it and hence the volume of offered traffic being handled by the unit. The unit CPU load is also a function of the nature of the offered load, i.e. with the same volume of offered load, the CPU load with the nominal traffic profile would be different as compared to some other arbitrary traffic profile. To manage future traffic growth, a model to estimate the BSU unit CPU load is an essential need. In recent times, using Machine Learning (ML) to develop such a model is an approach that has gained wide acceptance. Since, the estimation of CPU load is difficult as it depends on large set of parameters, machine learning approach is more scalable. In this paper, we describe a back-propagation neural network model that was developed to estimate the BSU unit CPU load. We describe the model parameters and choices and implementation architecture, and estimate its accuracy of prediction, based on an evaluation data set. We also discuss alternative ML architectures and compare their relative prediction accuracies, to the primary ML model

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