Fuzzy Cognitive Maps Optimization for Decision Making and Prediction

Poczęta, Katarzyna; Papageorgiou, Elpiniki I.; Gerogiannis, Vassilis C.

doi:10.3390/math8112059

Cited by 23 publications

(14 citation statements)

References 30 publications

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“…The relationship among these nodes can take values in the range [0, 1]. The directed links that define concepts & associations can be positive or negative, with values ranging from − 1 to 1 [78]. Though efficient these FCMs do not take into consideration the indeterminate relationship among concepts.…”

Section: Materials and Methodologymentioning

confidence: 99%

Violence Detection Approach based on Cloud Data and Neutrosophic Cognitive Maps

et al. 2022

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Violence has remained a momentous problem since time immemorial. Various scientific studies are conducted in the recent past to identify the stimuli causing violent behavior among the masses and to achieve the target of cloud data protection. Given the inherent ambiguity or indeterminacy in human behaviour, this study in the area of violence detection appears to be effective, as it finds a variety of stimuli and character qualities that contribute to violent conduct among masses. This uncertainty of traits causing violence can easily be seen in surveillance data present over the cloud and also from the data collected using academic research. Therefore, for the purpose of identifying violent behavior we have considered the factors (data) from existing research and from data over clouds. The factors that lead to violent behavior and are identified by algorithms running over clouds are termed as determinate or certain factors. The factors that were not considered and least identified by the cloud algorithms and given less importance are termed indeterminate factors or uncertain factors. The indeterminate factors are also considered based on the expert’s opinion where the experts are not in a condition to provide a clear stance or when they are neutral in their opinion. Tests are performed using Neutrosophic Cognitive Maps (NCMs) to model the violent behavior taking into consideration both determinate and indeterminate factors. Earlier these tests were performed using Fuzzy Cognitive Maps (FCMs) where indeterminate or uncertain factors were not considered. Therefore, we provide a brief comparison between NCMs and FCMs and show how effective NCMs are when we need to consider the uncertainty of concepts while carrying out tests for identifying violent behavior. Later results are obtained by forming a Neutrosophic adjacency matrix which is evaluated using the concepts of linear algebra. The obtained results in the form of 1 ∗ n vector (1 I I I I 1 I 1 I I I I I I I I I I I ) clearly shows the presence of indeterminate factor ‘I’ in the vector which was absent in earlier models when designed using FCMs. This shows how these indeterminate or uncertain factors play a significant role in cultivating violent behavior which was not shown in the previous study. The study is significant since it takes into account factors from cloud data, experts’ opinions, and also from literature, and shows how these factors are taken into consideration at the data level itself so that they will not impact the modeling stage, and machine learning algorithms will perform well because uncertain and indeterminate information is taken care of at training phase itself. Hence uncertainty could be reduced in machine learning algorithms and in the overall recognition of violent behavior.

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Section: Materials and Methodologymentioning

confidence: 99%

Violence Detection Approach based on Cloud Data and Neutrosophic Cognitive Maps

et al. 2022

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“…In certain domains of applications, modelers either optimize the FCMs constructed by the experts and/or constructing FCMs entirely based on the data collected about the systems. A set of machine learning algorithms were developed to meet these tasks and have previously been applied to numerous fields such as the optimization of industrial processes ( Papageorgiou, Stylios & Groumpos, 2006 ; Stach, Kurgan & Pedrycz, 2008 ; Papageorgiou, 2011a ), decision making ( Poczeta, Papageorgiou & Gerogiannis, 2020 ), and classification ( Nápoles et al, 2014 ; Nápoles, Jastrzębska & Salgueiro, 2021 ). In the proposed library, we include three types of algorithms used for edge optimization, FCM generation, and classification.…”

Section: Learning Algorithms For Fcmsmentioning

confidence: 99%

FCMpy: a python module for constructing and analyzing fuzzy cognitive maps

Mkhitaryan

Giabbanelli

Wozniak

et al. 2022

PeerJ Computer Science

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FCMpy is an open-source Python module for building and analyzing Fuzzy Cognitive Maps (FCMs). The module provides tools for end-to-end projects involving FCMs. It is able to derive fuzzy causal weights from qualitative data or simulating the system behavior. Additionally, it includes machine learning algorithms (e.g., Nonlinear Hebbian Learning, Active Hebbian Learning, Genetic Algorithms, and Deterministic Learning) to adjust the FCM causal weight matrix and to solve classification problems. Finally, users can easily implement scenario analysis by simulating hypothetical interventions (i.e., analyzing what-if scenarios). FCMpy is the first open-source module that contains all the functionalities necessary for FCM oriented projects. This work aims to enable researchers from different areas, such as psychology, cognitive science, or engineering, to easily and efficiently develop and test their FCM models without the need for extensive programming knowledge.

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“…The FCM is a system that is suitable for decision making, whose usefulness, given in the modeling of decision making for different areas, for example: for recognition patterns, in risk analysis and crisis management, as a support tool for decision-making decisions, to model an underwater virtual world of dolphins, fish, and sharks, for socio-economic development planning, and to support the decisionmaking process for photovoltaic solar energy sector development, numerical and linguistic forecasting, among others [18].…”

Section: Figure 1 Classification Of Companies In Mexicomentioning

confidence: 99%

Technology and Innovation in Organizations Using Fuzzy Systems

Hernández¹,

Cruz²,

Olmos-Peña³

2022

TEM Journal

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The economic importance of SMEs in the world is transcendental, since the union of their local profits generates global benefits; however, managers do not know how to visualize their problems to implement automated processes. There are solutions on the market, but they are expensive and with unnecessary modules that are not useful for SME leaders. Systems based on fuzzy models provide technology that allows, through Fuzzy Cognitive Maps (FCMs synthesizing problems and show solution strategies; In this case, the problems became Items in an FCM to simulate the entry and exit of causes and effects in the company, converted into proposals for automated processes.

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Fuzzy Cognitive Maps Optimization for Decision Making and Prediction

Cited by 23 publications

References 30 publications

Violence Detection Approach based on Cloud Data and Neutrosophic Cognitive Maps

Violence Detection Approach based on Cloud Data and Neutrosophic Cognitive Maps

FCMpy: a python module for constructing and analyzing fuzzy cognitive maps

Technology and Innovation in Organizations Using Fuzzy Systems

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