Rule Learning in a Fuzzy Decision Support System for the Environmental Risk Assessment of GMOs

Camastra, Francesco; Ciaramella, Angelo; Giovannelli, Valeria; Lener, Matteo; Rastelli, Valentina; Sposato, Salvatore; Staiano, Antonino; Staiano, Giovanni; Starace, Alfredo

doi:10.1007/978-3-319-03200-9_23

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…For decision support, the unique method, to correctly predict the risk of activities is by congregation as much of related historical data and analyzes the correlation between the features which contribute to the activities occurrences [12]. A Fuzzy Rule Learning in a Fuzzy Decision Support System incorporates the knowledge in the design of a system whose input-output relationships are defined by a set of fuzzy rules [13].…”

Section: Decision Supportmentioning

confidence: 99%

Risk assessment optimization for decision support using intelligent model based on fuzzy inference renewable rules

Radhi

2020

IJEECS

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Due to the unreliability of wired communications and the risks of controlling the process of transmitting data besides the complications that affecting data protection and the high costs of systems infrastructure, led to use wireless communications instead of wires media, but these networks are vulnerable towards illegal attacks. The side effects of these attacks are modifying data or penetrate the security system and discover its weaknesses, which leads to great material losses. These risks and difficulties led to the reluctance of wires communications and propose intelligent techniques and robust encryption algorithms for preventing data transmitted over wireless networks to keep it safe from cyber security attacks. So, there is a persistent need for providing intelligent techniques and robust algorithms to preserve conveyed information using wireless network. This paper introduces scenario for proposing intelligent technique to increase data reliability and provides a new way to improve high level of protection besides reduces infrastructure cost. The proposed system relies on two models, where the first model based on producing a knowledge base of risk rules while the aim of the second module is a risk assessment outcomes and encryption process according to attacks type. In this system, reducing risks levels based on renewable rules whereas a novel security system established on non-periodic keys with unsystematic operations using fuzzy system. We concluded that the proposed system has the ability to protect the transmitted data, increases its reliability and reduce the potential risks. MATLAB Toolkit 2014 then Weka open source package was used in encryption and data mining for the proposed system.<iframe class="ginger-extension-definitionpopup" style="left: -91.5px; top: -78px; z-index: 100001; display: none;" src="chrome-extension://kdfieneakcjfaiglcfcgkidlkmlijjnh/content/popups/definitionPopup/index.html?title=Abstract&description=a%20concept%20or%20idea%20not%20associated%20with%20any%20specific%20instance"></iframe>

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Section: Decision Supportmentioning

confidence: 99%

Risk assessment optimization for decision support using intelligent model based on fuzzy inference renewable rules

Radhi

2020

IJEECS

View full text Add to dashboard Cite

show abstract

Rule Learning in a Fuzzy Decision Support System for the Environmental Risk Assessment of GMOs

Cited by 1 publication

References 16 publications

Risk assessment optimization for decision support using intelligent model based on fuzzy inference renewable rules

Risk assessment optimization for decision support using intelligent model based on fuzzy inference renewable rules

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