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on ResearchGate | On Jan 1, , Michael Glykas and others published Fuzzy cognitive maps. Advances in theory, methodologies, tools and applications .
Table of contents
- FCMapper-package: Fuzzy Cognitive Mapping in FCMapper: Fuzzy Cognitive Mapping
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Furthermore, the knowledge of a programming methodology i. W-matrix with knowledge dependencies among the programming structures. The knowledge dependencies and the values of them have been defined by experts of the domain knowledge of computer programming. The mean that were given by the ten domain experts are the values which are labeled on the directed arcs of the FCM. It has to be made clear that the value 1 of a knowledge dependency does not mean that the two dependent concepts are the same. In this way, the values of concepts change. However, concerning the FCM, the alterations of the knowledge level value of a concept causes change on the knowledge level value of its related concepts.
Increase of the value of a concept causes increase of the value of its related concepts or decrease of the value of a concept causes decrease on the value of its related concepts. These alterations are conducted according to the transformation function of the FCM, and indicate the progress or no-progress of the learner.
In order to provide the evidence that the proposed approach is of potential value, evaluation is required. An evaluation offers information to make decision about using the product or not Phillips and Gilding Empirical evaluations refer to the appraisal of a theory by observation in experiments Mulwa et. Therefore, an experiment was conducted, in order to compare the effectiveness of the navigation support that is offered by two different systems: LeCP-A and LeCP-B.
Both systems are adaptive tutors of computer programming. They intend to teach learners either the principles and structures of the computer programming, or the logic of programming and algorithms including calculating sums, averages and maximums or minimums. The learner meets these concepts in sequence. A difference between the two tutors is the knowledge representation technique. They do not depict how the knowledge level of a domain concept is affected by the knowledge level of another concept. However, this does not happened in LeCP-B, since in this system the knowledge level of the concepts are depicted independent from each other.
For example, at the moment t, Mike is reading the concept C 3. Furthermore, LeCP-A recognizes the domain concepts that a learner has forgotten, concerning the lack of knowledge on other related concepts. Consequently, a learner who uses LeCP-A is advised to read a concept no one, one, or more times. On the other hand, a learner who uses LeCP-B is advised to read a concept at least one time. The criterion for the evaluation of the knowledge representation technique through FCMs is the mean number of times that a learner is advised to read a domain concept, until it is considered as learned.
The fewer the times are, the better navigation support is provided. Otherwise, the learner is advised to repeat the particular concept. A group of 50 students of a postgraduate program in the field of informatics at the University of Piraeus group A used LeCP-A in order to learn computer programming, and a group of 36 students of the same postgraduate program group B used LeCP-B for the same purpose.
That means that FCMs help the system to provide more efficient navigation support. However, how can we be sure that the different average scores are not occurred by chance, or due to differences on the education, knowledge level and abilities of the learners of the two groups, or due to the different amount of participants in the two groups?
Consequently, the difference of the means of the two groups is not occurred due to chance and, also, they are statistically significant. The target of this paper was to present a domain knowledge representation method that can contribute to the improvement of the navigation support that an adaptive learning system provides. More specifically, the progress or no-progress of a learner indicates the need for omission or repetition of some domain concepts.
This can be done through Fuzzy Cognitive Maps. In particular, the nodes of the FCM, which represent the domain concepts of the learning material, are defined by domain experts. Also, the contribution of domain experts is significant for the definition of the knowledge dependencies between the domain concepts of the learning material and their strength of impact on each other.
In other words, they define the values of the w-matrix of the FCM. The presented knowledge representation approach was compared with the most common used technique for representing the domain knowledge, which is called network of concepts technique. The domain knowledge representation has to be combined with a well-designed student model, which will be is responsible for how the system will utilize the information which is included in the domain knowledge module, in order to make the right decisions for offering personalized instruction and support.
Konstantina Chrysafiadi born in Athens, Greece, the year She received a B. She is currently preparing her Ph. She spent three years as a teacher of informatics in a private educational institution, and five years as administrative staff in the Ministry of Education, Greece. She is currently a teacher of computer science in secondary schools in East Attica, Greece.
Her fields of interest include e-learning, student modeling and teaching of programming. Maria Virvou was born in Athens, Greece. She has authored three books in computer science, and over published papers. She has graduated 12 Ph. Her research interests are in the area of computers and education, artificial intelligence in education, user and student modeling, e-learning and m-learning, knowledge-based software engineering and human-computer interaction. Competing interests. KC has made contribution to conception and design of the paper, to the acquisition, analysis and interpretation of data.
Also, she has been involved in drafting the paper. MV has made contribution to the design of the paper. She has involved in revising the manuscript critically for important intellectual content and has given final approval of the version to be published. Konstantina Chrysafiadi, Email: rg. Maria Virvou, Email: rg.
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Published online Mar 5. Konstantina Chrysafiadi and Maria Virvou. Author information Article notes Copyright and License information Disclaimer. Corresponding author. Received Nov 10; Accepted Feb 7. This article is published under license to BioMed Central Ltd.
Keywords: Knowledge representation, Knowledge dependencies, Fuzzy cognitive maps. Background Recent technological developments facilitate the provision of individually customized instruction to large audiences Akbulut and Cardak , and lead to rapid growth of Adaptive Learning Systems ALSs. Fuzzy cognitive maps Fuzzy Cognitive Mapping is a combination of fuzzy logic and cognitive mapping, and it is a way to represent knowledge of systems which are characterized of uncertainty and complex processes. Open in a separate window. Figure 1. Domain knowledge representation with FCMs The domain knowledge representation plays an important role in the adaptation of a tutoring system.
Figure 2. Domain knowledge representation using a FCM. Table 1 W-matrix with knowledge dependencies. Implementation of FCMs in a computer programming tutor The knowledge representation technique that is presented in this paper has been implemented in an e-learning environment for individualized instruction on the domain of programming languages Chrysafiadi and Virvou, , leading to a new improved version of the particular computer programming tutor.
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Figure 3. Table 2 W-matrix with knowledge dependencies among the programming structures. Table 3 Increase on knowledge level of the depended concepts.
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Table 5 Decrease on knowledge level of the depended concepts. Evaluation In order to provide the evidence that the proposed approach is of potential value, evaluation is required. Table 7 The learning material. Basics 1. Iteration Structure Unknown no of loops 5.
Sequence structure 2.http://ypadubyqej.cf
FCMapper-package: Fuzzy Cognitive Mapping in FCMapper: Fuzzy Cognitive Mapping
Arrays 6. Conditional Structures 3. Iteration Structure Concrete no of loops 4. An FCM represents an expert's domain knowledge in a form that lends itself to relatively easy integration into a collective knowledge base for a group involved in a decision process. The resulting group FCM has the potential to serve as a useful tool in a group decision support environment. An appropriate methodology for the development and analysis of group FCMs is required. A framework for such a methodology consisting of the development and application phases is presented.
Application of fuzzy sets and cognitive maps to incorporate social science scenarios in integrated assessment modelsA case study of urbanization in Ujung Pandang, Indonesia Tags: Integrated Assessment, decision—support systems, fuzzy set theory, fuzzy cognitive maps, scenario analysis Authors: Jean-Luc de Kok, Milan Titus and Herman G. Wind Journal: Integrated Assessment Decision—support systems in the field of integrated water management could benefit considerably from social science knowledge, as many environmental changes are human-induced.
Unfortunately the adequate incorporation of qualitative social science concepts in a quantitative modeling framework is not straightforward. The applicability of fuzzy set theory and fuzzy cognitive maps for the integration of qualitative scenarios in a decision—support system was examined for the urbanization of the coastal city of Ujung Pandang, Indonesia.
The results indicate that both techniques are useful tools for the design of integrated models based on a combination of concepts from the natural and social sciences. Banini and R. Bearman Journal: International Journal of Mineral Processing We propose fuzzy cognitive maps, a branch of fuzzy logic, to study interaction of factors affecting processes and details of the approach are discussed. Application of the technique to discriminate between factors affecting slurry rheology is demonstrated. It has been shown that hydrodynamic interaction, effective particle concentration, shape and size, temperature and shear rate have a significant influence on the slurry viscosity.
The complex interaction of the various factors delineated by previous workers is also presented.
Using fuzzy cognitive maps as a system model for failure modes and effects analysis Tags: Authors: C. FMEAs are used in reliability and safety evaluations of complex systems to determine the effects of component failures on the system operation. FCMs use a digraph to show cause and effect relationships between concepts; thus, they can represent the causal relationships needed for the FMEA and provide a new strategy for predicting failure effects in a complex system. Application of fuzzy cognitive maps for cotton yield management in precision farming Tags: Fuzzy cognitive maps; Modeling; Expert knowledge; Learning algorithm; Unsupervised learning; Decision making; Cotton; Yield; Soil Authors: Elpiniki I.
Papageorgiou, Athanasios Markinos, and Theofanis Gemptosb Journal: Expert Systems with Applications Abstract: The management of cotton yield behavior in agricultural areas is a very important task because it influences and specifies the cotton yield production. An efficient knowledge-based approach utilizing the method of fuzzy cognitive maps FCMs for characterizing cotton yield behavior is presented in this research work.
FCM is a modelling approach based on exploiting knowledge and experience. The advent of precision farming generates data which, because of their type and complexity, are not efficiently analyzed by traditional methods. The FCM model developed consists of nodes linked by directed edges, where the nodes represent the main factors in cotton crop production such as texture, organic matter, pH, K, P, Mg, N, Ca, Na and cotton yield, and the directed edges show the cause—effect weighted relationships between the soil properties and cotton field.
The proposed method was evaluated for cases measured for three subsequent years , and in a 5 ha experimental cotton yield. The proposed FCM model enhanced by the unsupervised nonlinear Hebbian learning algorithm, was achieved a success of The main advantage of this approach is the sufficient interpretability and transparency of the proposed FCM model, which make it a convenient consulting tool in describing cotton yield behavior. Brain tumor characterization using the soft computing technique of fuzzy cognitive maps Tags: Soft computing; Computational intelligence; Fuzzy logic; Fuzzy cognitive maps; Classification; Brain tumors Authors: E.
Papageorgioua, P. Spyridonosc, D. Glotsosc, C. Styliosb, , P. Ravazoulad, G. Nikiforidisc and P. Groumposa Journal: Applied Soft Computing The characterization and accurate determination of brain tumor grade is very important because it influences and specifies patient's treatment planning and eventually his life. A new method for characterizing brain tumors is presented in this research work, which models the human thinking approach and the classification results are compared with other computational intelligent techniques proving the efficiency of the proposed methodology.
The FCM grading model classification ability was enhanced introducing a computational intelligent training technique, the Activation Hebbian Algorithm.
Citations per year
The proposed method was validated for clinical material, comprising of cases. FCM grading model achieved a diagnostic output of accuracy of The results of the proposed grading model present reasonably high accuracy, and are comparable with existing algorithms, such as decision trees and fuzzy decision trees which were tested at the same type of initial data. The main advantage of the proposed FCM grading model is the sufficient interpretability and transparency in decision process, which make it a convenient consulting tool in characterizing tumor aggressiveness for every day clinical practice.
Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links Tags: Authors: Elpiniki I. Papageorgiou, Chrysostomos Styliosb and Peter P. Groumpos Journal: International Journal of Human-Computer Studies Fuzzy Cognitive Maps FCMs constitute an attractive knowledge-based methodology, combining the robust properties of fuzzy logic and neural networks.
FCMs handle available information and knowledge from an abstract point of view. They develop behavioural model of the system exploiting the experience and knowledge of experts. But this methodology may not be a sufficient model of the system because the human factor is not always reliable. Thus the FCM model of the system may requires restructuring which is achieved through adjustment the weights of FCM interconnections using specific learning algorithms for FCMs. In this article, two unsupervised learning algorithms are presented and compared for training FCMs; how they define, select or fine-tuning weights of the causal interconnections among concepts.
The implementation and results of these unsupervised learning techniques for an industrial process control problem are discussed. The simulations results of training the process system verify the effectiveness, validity and advantageous characteristics of those learning techniques for FCMs. By: Glykas, Michael [editor. Contributor s : SpringerLink Online service. Tags from this library: No tags from this library for this title. Log in to add tags. Holdings 0 Title notes Comments 0 No physical items for this record. Log in to your account to post a comment.