Generating Cancelable Fingerprint Template Using Triangular Structures

Reddy, A. Mallikarjuna; Kinnera, G. Rupa; Reddy, T. Chandrasekhara; Murthy, G. Vishnu

doi:10.1166/jctn.2019.7830

Cited by 6 publications

(2 citation statements)

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“…The estimation error can be reduced as the computing rate occurs by organizing the multiple criterion models. This unique study makes the distribution [14] of cluster adapted for deep learning schematics. Combining the decision-making analysis makes the process entirely multiple criteria under technical issues for providing the researchers with a concrete idea for future development in the analysis of the big data sets.…”

Section: Existing Methodsmentioning

confidence: 99%

“…On the other hand, the unessential data significantly diminish the viability interruption discovery framework. To improve the interruption discovery execution of the progressive effects for the profoundly prepared bunch staggered group dependent mixed on conducting highlights for the substance highlights [13][14]. Various decision-making support systems exist in the real-world environment that improve potentiality in achieving this concerning issue.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms

Papineni¹,

Yarlagadda²,

Akkineni³

et al. 2021

IJETT

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Data is evolving with the rapid progress of population and communication for various types of devices such as networks, cloud computing, Internet of Things (IoT), actuators, and sensors. The increment of data and communication content goes with the equivalence of velocity, speed, size, and value to provide the useful and meaningful knowledge that helps to solve the future challenging tasks and latest issues. Besides, multicriteria based decision making is one of the key issues to solve for various issues related to the alternative effects in big data analysis. It tends to find a solution based on the latest machine learning techniques that include algorithms like decision making and deep learning mechanism based on multicriteria in providing insights to big data. On the other hand, the derivations are made for it to go with the approximations to increase the duality of runtime and improve the entire system's potentiality and efficacy. In essence, several fields, including business, agriculture, information technology, and computer science, use deep learning and multicriteria-based decision-making problems. This paper aims to provide various applications that involve the concepts of deep learning techniques and exploiting the multicriteria approaches for issues that are facing in big data analytics by proposing new studies with the fusion approaches of data-driven techniques.

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Section: Existing Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%