Uncertainty-based test planning using dempster-shafer theory of evidence

Kukulies, Jan; Schmitt, Robert

doi:10.1109/icsrs.2017.8272829

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Knowledge inaccuracies can be attributed to slightly detailed information or the existence of new unknown events. Knowledge uncertainty has been represented using fuzzy systems [17], evidence theory [18] [19] [20], or hybrid systems [21] [22]. The next step in consolidating acquired knowledge is to define a strategy for knowledge uncertainty quantification.…”

Section: A Extraction Of Tacit Knowledge and Uncertainty Quantificationmentioning

confidence: 99%

Production Assessment Using a Knowledge Transfer Framework and Evidence Theory

Piolo

Tahasanul

et al. 2022

IEEE Access

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Operational knowledge is one of the most valuable assets in a company, as it provides a strategic advantage over competitors and ensures steady and optimal operation in machines. An (interactive) assessment system on the shop floor can optimize the process and reduce stopovers because it can provide constant valuable information regarding the machine condition to the operators. However, formalizing operational (tacit) knowledge to explicit knowledge is not an easy task. This transformation considers modeling expert knowledge, quantification of knowledge uncertainty, and validation of the acquired knowledge. This study proposes a novel approach for production assessment using a knowledge transfer framework and evidence theory to address the aforementioned challenges. The main contribution of this paper is a methodology for the formalization of tacit knowledge based on an extended failure mode and effect analysis for knowledge extraction, as well as the use of evidence theory for the uncertainty definition of knowledge. Moreover, this approach uses primitive recursive functions for knowledge modeling and proposes a validation strategy of the knowledge using machine data. These elements are integrated into an interactive recommendation system hosted on a backend that uses HoloLens as a visual interface. We demonstrate this approach using an industrial setup: a laboratory bulk good system. The results yield interesting insights, including the knowledge validation, uncertainty behavior of knowledge, and interactive troubleshooting for the machine operator.

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Section: A Extraction Of Tacit Knowledge and Uncertainty Quantificationmentioning

confidence: 99%

Production Assessment Using a Knowledge Transfer Framework and Evidence Theory

Piolo

Tahasanul

et al. 2022

IEEE Access

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“…As illustrated in earlier works, 'Baysian Probability Theorem' has been the choice for handling uncertainty in datasets. However, the work of Zhang et al [177] rather argued the 'Dempster-Shafer (D-S) theory of evidence' [178] is better equipped than Baysian probability while using statistical classification. Uncertainty can arise in number of regards in the analysis of spam corpuses such as assigning missing values to features.…”

Section: K: Supervised Systems Discussing Performance Of Different Almentioning

confidence: 99%

A Comprehensive Survey for Intelligent Spam Email Detection

et al. 2019

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The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject-appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection. INDEX TERMS Machine learning, phishing attack, spear phishing, spam detection, spam email, spam filtering.

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Uncertainty-based test planning using dempster-shafer theory of evidence

Cited by 2 publications

References 25 publications

Production Assessment Using a Knowledge Transfer Framework and Evidence Theory

Production Assessment Using a Knowledge Transfer Framework and Evidence Theory

A Comprehensive Survey for Intelligent Spam Email Detection

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