scholarly journals Assessment of Food Security Risk Level Using Type 2 Fuzzy System

2016 ◽  
Vol 102 ◽  
pp. 547-554 ◽  
Author(s):  
Rahib H. Abiyev ◽  
Kaan Uyar ◽  
Umit Ilhan ◽  
Elbrus Imanov
Keyword(s):  
Food Security ◽  
Fuzzy System ◽  
Risk Level ◽  
Security Risk

scholarly journals Estimation of Food Security Risk Level UsingZ-Number-Based Fuzzy System

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Rahib H. Abiyev ◽  
Kaan Uyar ◽  
Umit Ilhan ◽  
Elbrus Imanov ◽  
Esmira Abiyeva
Keyword(s):  
Food Security ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Risk Level ◽  
Uncertain Information ◽  
Security Risk ◽  
Fuzzy Logic Systems ◽  
Reliability Parameters ◽  
Basic Parameters

Fuzzy logic systems based on If-Then rules are widely used for modelling of the systems characterizing imprecise and uncertain information. These systems are basically based on type-1 fuzzy sets and allow handling the uncertain and imprecise information to some degree in the developed models. Zadeh extended the concept of fuzzy sets and proposedZ-number characterized by two components, constraint and reliability parameters, which are an ordered pair of fuzzy numbers. Here, the first component is used to represent uncertain information, and the second component is used to evaluate the reliability or the confidence in truth.Z-number is an effective approach to solving uncertain problems. In this paper,Z-number-based fuzzy system is proposed for estimation of food security risk level. To construct fuzzy If-Then rules, the basic parameters cereal yield, cereal production, and economic growth affecting food security are selected, and the relationship between these input parameters and risk level are determined through If-Then fuzzy rules. The fuzzy interpolative reasoning is proposed for construction of inference mechanism of aZ-number-based fuzzy system. The designed system is tested using Turkey cereal data for assessing food security risk level and prediction periods of the food supply.

scholarly journals FOOD SECURITY RISK LEVEL ASSESSMENT: A FUZZY LOGIC-BASED APPROACH

2013 ◽  
Vol 27 (1) ◽  
pp. 50-61 ◽  
Author(s):  
Muhd Khairulzaman Abdul Kadir ◽  
Evor L. Hines ◽  
Kefaya Qaddoum ◽  
Rosemary Collier ◽  
Elizabeth Dowler ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Food Security ◽  
Risk Level ◽  
Security Risk

scholarly journals FPGA Synthesis of SIRM Fuzzy System-Classification Of Diabetic Epilepsy Risk Level

2012 ◽  
Vol 38 ◽  
pp. 391-404
Author(s):  
N.B. Balamurugan ◽  
M. Jothi ◽  
R. Harikumar
Keyword(s):  
Fuzzy System ◽  
Risk Level

Recent Advancement in Fuzzy System

2014 ◽  
pp. 158-168
Author(s):  
İ. Burhan Türkşen ◽  
İbrahim Özkan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Interdisciplinary Research ◽  
Decision Process ◽  
Fuzzy System ◽  
Research Field ◽  
Decision Under Uncertainty ◽  
Recent Advancement ◽  
Human Decision

Decision under uncertainty is an active interdisciplinary research field. A decision process is generally identified as the action of choosing an alternative that best suites our needs. This process generally includes several areas of research including but not limited to Economics, Psychology, Philosophy, Mathematics, Statistics, etc. In this chapter the authors attempt to create a framework for uncertainties which surrounds the environment where human decision making takes place. For this purpose, the authors discuss how one ought to handle uncertainties within Fuzzy Logic. Furthermore, they present recent advances in Type 2 fuzzy system studies.

A Technoethical Study of Ethical Hacking Communication and Management Within a Canadian University

2018 ◽  
pp. 307-326 ◽  
Author(s):  
Baha Abu-Shaqra ◽  
Rocci Luppicini
Keyword(s):  
Decision Making ◽  
Information Security ◽  
Ethical Decision Making ◽  
Information Environment ◽  
Risk Level ◽  
Security Risk ◽  
Study Approach ◽  
Security Awareness Training ◽  
Security Risk Management ◽  
Decision Making Model

Ethical hacking is an important information security risk management strategy within higher education applied against the growing threat of hacking attacks. Confusion regarding the meaning and ethics of ethical hacking within broader society and which resonates within organizations undermines information security. Confusion within organizations increases unpredictably (equivocality) in the information environment, which raises risk level. Taking a qualitative exploratory case study approach, this chapter pairs technoethical inquiry theory with Karl Weick's sensemaking model to explore the meanings, ethics, uses and practices, and value of ethical hacking in a Canadian university and applies technoethical inquiry decision-making grid (TEI-DMG) as an ethical decision-making model. Findings point to the need to expand the communicative and sociocultural considerations involved in decision making about ethical hacking organizational practices, and to security awareness training to leverage sensemaking opportunities and reduce equivocality in the information environment.

A New Optimization Approach to Clustering Fuzzy Data for Type-2 Fuzzy System Modeling

2012 ◽  
pp. 499-508
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Milad Avazbeigi
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy System ◽  
Distance Measure ◽  
System Model ◽  
Rule Base ◽  
Optimization Approach ◽  
Fuzzy Data ◽  
System Models ◽  
Fuzzy System Models

This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new distance measure for calculating the (dis)similarity between fuzzy data is proposed. Then, based on the proposed distance measure, Fuzzy c-Mean (FCM) clustering algorithm is modified. Next, Xie-Beni cluster validity index is modified to be able to valuate Type-2 fuzzy clustering approach. In this index, all operations are fuzzy and the minimization method is fuzzy ranking with Hamming distance. The proposed Type-2 fuzzy clustering method is used for development of indirect approach to Type-2 fuzzy modeling, where the rules are extracted from clustering fuzzy numbers (Zadeh, 1965). Then, the Type-2 fuzzy system is tuned by an inference algorithm for optimization of the main parameters of Type-2 parametric system. In this case, the parameters are: Schweizer and Sklar t-Norm and s-Norm, a-cut of rule-bases, combination of FATI and FITA inference approaches, and Yager parametric defuzzification. Finally, the proposed Type-2 fuzzy system model is applied in prediction of the steel additives in steelmaking process. It is shown that, the proposed Type-2 fuzzy system model is superior in comparison with multiple regressions and Type-1 fuzzy system model, in terms of the minimization the effect of uncertainty in the rule-base fuzzy system models an error reduction.

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