A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference

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

doi:10.1016/j.eswa.2014.09.041

Cited by 90 publications

(44 citation statements)

References 36 publications

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“…Finally, the defuzzifier block transforms the fuzzy output into a crisp value. The inference engine is the FIS heart, and can reproduce the human decision-making process by performing approximate reasoning in order to achieve a control strategy [10]. The inference stage utilizes the fuzzy input values to activate the inference rules and generate the fuzzy output value.…”

Section: Fuzzy Inference System: a Theoretical Reviewmentioning

confidence: 99%

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A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass

Cavallaro

2015

Sustainability

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Abstract:One aspect of the use of biomass for energy purposes which remains controversial concerns their full environmental sustainability. Considering the crucial importance of this problem, numerous authors have carried out evaluations of the environmental impact of the various types of biomass by means of several approaches. Although some of these methods are excellent environmental evaluation tools, they are unfortunately unable to manage uncertain input data. Instead, fuzzy-set based methods have proven to be able to deal with uncertainty in environmental topics. The original contributions proposed by fuzzy logic relate, on the one hand, to the representation of uncertain and vague information, and, on the other, to handling such information using fuzzy rules. A fuzzy inference system (FIS) constitutes the practice of framing mapping from the input to an output using fuzzy logic. In this paper, we propose an application of Takagi-Sugeno fuzzy inference modelling to build a synthetic index to assess the sustainability of production of the biomass for energy purposes.

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Section: Fuzzy Inference System: a Theoretical Reviewmentioning

confidence: 99%

“…An Fuzzy Inference System (FIS) contains the knowledge and experience of an expert, in the design of a system that controls a process whose input-output relations are defined by a set of fuzzy control rules, e.g., IF-THEN rules [10]. Fuzzy logic-reasoning contains two types of information.…”

Section: Fuzzy Inference System: a Theoretical Reviewmentioning

confidence: 99%

A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass

Cavallaro

2015

Sustainability

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“…In simple terms, a FIS is a system which can obtain new knowledge from existing knowledge by using fuzzy logic (Camastra et al 2015;Cavallaro 2015). A fuzzy inference system is made up of three sections: the first section is the fuzzification process when all crisp values are converted to a linguistic input value using a MF of the system (Tahmasebi and Hezarkhani 2012).…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

et al. 2018

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In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while onethird of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.

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“…The variables processed are introduced in linguistic form. A rule base links the input variables to the output variable of the form (IF ... THEN) (Camastra et al, 2015). Each numerical value is expressed by its degree of belonging to the membership function of the variable in question.…”

Section: Fuzzy Logic Modelingmentioning

confidence: 99%

Pesticides in Drinking Water and Public Health: Modeling Using Artificial Intelligence Techniques

Benzidane¹,

Saddek²,

Mohamed³

et al. 2017

IJSRES

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Abstract. The causes of chronic diseases, including cancers, are extremely complex. Throughout their lives, individuals who differ in genetic makeup and sensitivity are exposed to a wide variety of carcinogens. Some chemicals by themselves are safe but can act as synergists or promoters together with other chemicals to cause the disease. The problem of the harmful effects of pesticides is the long-term consequences. If the acute effects are well known, the long-term projections remain to be elucidated. Surface water has a direct influence on the degree of contamination by pesticides in groundwater. These are contaminated by significant infiltrations. In order to establish a direct relationship between the possible chronic effects of pesticides on public health, it is essential to consider a set of factors that intervene in the process. Some factors are related to climatic conditions, others to the nature of the soil and others to the nature of the pesticide used. Other factors are related to the people who consume these waters in terms of period, age, sex ... etc. Making a classical mathematical analysis becomes very difficult and even impossible. We propose in this study the analysis of these variables by the techniques of artificial intelligence mainly fuzzy inference. The variables analyzed are considered as fuzzy variables. This makes it possible to take care of these uncertainties and vagueness. A database is established, the resulting algorithm will allow us to read instantly the outcome which expresses the degree of contamination on public health from the introduction of the random variables to the input of the system.

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A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference

Cited by 90 publications

References 36 publications

A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass

A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

Pesticides in Drinking Water and Public Health: Modeling Using Artificial Intelligence Techniques

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