Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region

Jayashree, L. S.; Palakkal, Nidhil; Papageorgiou, Elpiniki I.; Παπαγεωργίου, Κ.

doi:10.1007/s00521-015-1864-5

Cited by 32 publications

(8 citation statements)

References 22 publications

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“…The FCM is a soft computing technique able to model system behaviour by means of a set of concepts and causal relationships between them. The FCM development method is based on fuzzy rules that can either be proposed by human experts or derived by means of knowledge extraction methods [17], in such a way that the accumulated experience and knowledge are integrated in the causal relationships between factors of the modeled process or system [28]. An FCM consists of nodes that represent the factors most relevant to the decisional environment and arrows that represent different causal relationships among factors.…”

Section: A the Relationship Quality-based Student Loyalty Modelmentioning

confidence: 99%

“…In general, the FCM is easy to model and use and by virtue of its flexibility, as highlighted in [12], [13], [14]. [15], applied to many scientific areas from knowledge modeling to prediction and decision-making, in a very wide range of domains, such as: emergency management [16], yield management [17], medical risk prediction [18], etc. In [19], the authors have used FCM to design EDI controls modeling casual relationships between factors in partner relationships in the context of airline service companies.…”

Section: Related Studiesmentioning

confidence: 99%

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What-if analysis combining Fuzzy Cognitive Map and Structural Equation Modeling

Maio

Botti

Fenza

et al. 2015

2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI)

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Nowadays, public funded universities have to increase their traction in acquiring students and in gaining and retaining their commitment, satisfaction and loyalty. In the area of information management, the Structural Equation Modeling (SEM) technique has been widely applied to perform inter-construct causal analysis (i.e. Commitment, Quality of Services, Loyalty, etc.) on specific observed indicators in order to model and analyze student loyalty in educational institutions. Nevertheless, SEM does not support what-if analysis contrived to consider decision-making scenarios, such as: what happens to student loyalty if managers adopt a quality-based strategy, and so on. This work proposes to combine SEM results with a Fuzzy Cognitive Map (FCM) to support what-if analysis and to determine the best strategy to adopt for the case study of the Relationship Quality-Based Student Loyalty Model (RQSL). Specifically, the causal models retrieved from SEM will be exploited as the input map of concepts enabling FCM. Subsequently, FCM is performed considering different input configurations corresponding to specific managing strategies (e.g. investing in quality of service, or quality of teaching, etc.) activating/deactivating various input concepts in order to simulate implications on the output constructs (e.g., Loyalty, Commitment, etc.). The results of the experiments provide reasonably good estimates of the impact on student loyalty deriving from investing in each specific factor and provides a helpful analysis that will support decision making. Furthermore, this study highlights the opportunities deriving from the cross-fertilization between the management and computer science domains

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Section: A the Relationship Quality-based Student Loyalty Modelmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

What-if analysis combining Fuzzy Cognitive Map and Structural Equation Modeling

Maio

Botti

Fenza

et al. 2015

2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI)

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“…For the development of dynamic financial multi-variable scenarios that allow to describe the EIRR behaviour over time according to the FCM structure (22), a novel stochastic fuzzy logistic map (S FLM) is proposed. This proposal allows to integrate in a single structure each of the aspects that define a financial scenario and the variables expressed as linguistic random rates, based on Definitions 3 and 5, which can be expressed as follows [37,53,52]:…”

Section: Stochastic Fuzzy Logistic Map (S Flm)mentioning

confidence: 99%

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

González-Ruiz

Peña

Grisales

et al. 2019

Applied Soft Computing

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In general, the development of economic infrastructure systems requires a behavioural comprehensive analysis of different financial variables or rates to establish its long-term success with regards to the Equity Internal Rate of Return (EIRR) expectation. For this reason, several financial organizations have developed economic scenarios supported by computational techniques and models to identify the evolution of these financial rates. However, these models and techniques have shown a series of limitations with regard to the financial management process and its impact on EIRR over time. To address these limitations in an inclusive way, researchers have developed different approaches and methodologies focused on the development of financial models using stochastic simulation methods and computational intelligence techniques. This paper proposes a Stochastic Fuzzy Logistic Model (S-FLM) inspired by a Fuzzy Cognitive Map (FCM) structure to model financial scenarios. Where the input consists in financial rates that are characterized as linguistic rates through a series of adaptive logistic functions. The stochastic process that explains the behaviour of the financial rates over time and their partial effects on EIRR is based on a Monte Carlo sampling process carried out on the fuzzy sets that characterize each linguistic rate. The S-FLM was evaluated by applying three financing scenarios to an airport infrastructure system (pessimistic, moderate/base, optimistic), where it was possible to show the impact of different linguistic rates on the EIRR. The behaviour of the S-FLM was validated using three different models: (1) a financial management tool; (2) a general FCM without pre-loaded causalities among the variables; and (3) a Statistical S-FLM model (S-FLMS), where the causalities between the concepts or rates were obtained as a result of an independent effects analysis applying a cross modelling between variables and by using a statistical multi-linear model (statistical significance level) and a multi-linear neural model (MADALINE). The results achieved by the S-FLM show a higher EIRR than expected for each scenario. This was possible due to the incorporation of an adaptive multi-linear causality matrix and a fuzzy credibility matrix into its structure. This allowed to stabilize the effects of the financial variables or rates on the EIRR throughout a financing period. Thus, the S-FLM can be considered as a tool to model dynamic financial scenarios in different knowledge areas in a comprehensive manner. This way, overcoming the limitations imposed by the traditional computational models used to design these financial scenarios.

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“…Bireylerin ve organizasyonların karşılaştığı problemlerin yapılandırılması ve analiz edilebilmesi için kullanılan yöntemin [30] klasik BH'den temel farkı, karmaşık bir sistemin değişkenleri arasındaki nedensel ilişkiler üzerinden sistemin davranışını dinamik olarak modelleyebilmesidir [31]. BBH'nin getirdiği bir başka yenilik ise incelenen değişkenlerin değer alabilmesine olanak tanımasıdır [32]. Klasik BBH yönteminde sistemin davranışı kavramlar (concept) bazında açıklanır ve bu kavramlar sistemin elemanları, durumu, değişkenleri ya da belirli özellikleri olabilir [33].…”

Section: Bulanik Bi̇li̇şsel Hari̇talama (Fuzzy Cognitive Mapping)unclassified

Bulanık bilişsel haritalara dayalı yeni bir ürün konumlandırma yaklaşımı

Asan

Kadaifçi

2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 Dynamic positioning that considers the change of customer perceptions over time  A new fuzzy cognitive mapping approach that is sensitive to initial conditions  A normative approach that predicts the required initial conditions necessary to achieve a desired position Figure A. Proposed Method Purpose: The purpose of this study is to introduce a new positioning analysis method that is able to model the change of customer perceptions over time and predict the initial conditions necessary to achieve a desired future position. Theory and Methods: In this study, a new dynamic positioning method with less restrictive assumptions is proposed. The new method offers an improved fuzzy cognitive mapping (FCM) approach. The classical FCM is able to provide only a limited solution to the positioning problem due to producing similar future steady state values under different initial conditions and not allowing backcasting. To overcome these drawbacks, a hybrid approach, which combines the advantages of the classical FCM and the cognitive mapping (CM) methods, is proposed. Results: Integrating the reachability matrix of CM with the classical FCM offers more effective and distinctive results and enables to observe the necessary conditions to achieve a desired position in future. In order to examine the effectiveness and applicability of the proposed method, an application to the game console market is presented. The proposed method also differs from other positioning methods by considering expert opinions and customer preferences together. Conclusion: The proposed method has a critical role on providing medium and long-term positioning strategies considering the changing perceptions over time. Besides, the desired future can be achieved by taking the necessary actions determined systematically by the proposed method.

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Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region

Cited by 32 publications

References 22 publications

What-if analysis combining Fuzzy Cognitive Map and Structural Equation Modeling

What-if analysis combining Fuzzy Cognitive Map and Structural Equation Modeling

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

Bulanık bilişsel haritalara dayalı yeni bir ürün konumlandırma yaklaşımı

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