Chrysostomos D. Stylios

Modeling complex systems using fuzzy cognitive maps

This research deals with the soft computing methodology of fuzzy cognitive map (FCM). Here a mathematical description of FCM is presented and a new methodology based on fuzzy logic techniques for developing the FCM is examined. The capability and usefulness of FCM in modeling complex systems and the application of FCM to modeling and describing the behavior of a heat exchanger system is presented. The applicability of FCM to model the supervisor of complex systems is discussed and the FCM-supervisor for evaluating the performance of a system is constructed; simulation results are presented and discussed.

A fuzzy cognitive map approach to differential diagnosis of specific language impairment

This paper presents a computer-based model for differential diagnosis of specific language impairment (SLI), a language disorder that, in many cases, cannot be easily diagnosed. This difficulty necessitates the development of a methodology to assist the speech therapist in the diagnostic process. The methodology tool is based on fuzzy cognitive maps and constitutes a qualitative and quantitative computer model comprised of the experience and knowledge of specialists. The development of the model was based on knowledge from the literature and then it was successfully tested on four clinical cases. The results obtained point to its final integration in the future and to its valid contribution as a differential diagnosis model of SLI.

Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule

Fuzzy Cognitive Map (FCM) is a soft computing technique for modeling systems. It combines synergistically the theories of neural networks and fuzzy logic. The methodology of developing FCMs is easily adaptable but relies on human experience and knowledge, and thus FCMs exhibit weaknesses and dependence on human experts. The critical dependence on the expert’s opinion and knowledge, and the potential convergence to undesired steady states are deficiencies of FCMs. In order to overcome these deficiencies and improve the efficiency and robustness of FCM a possible solution is the utilization of learning methods. This research work proposes the utilization of the unsupervised Hebbian algorithm to nonlinear units for training FCMs. Using the proposed learning procedure, the FCM modifies its fuzzy causal web as causal patterns change and as experts update their causal knowledge.

Augmented Fuzzy Cognitive Maps Supplemented with Case Based Reasoning for Advanced Medical Decision Support

Fuzzy Cognitive Maps (FCMs) have been used to design Decision Support Systems and particularly for medical informatics to develop Intelligent Diagnosis Systems. Even though they have been successfully used in many different areas, there are situations where incomplete and vague input information may present difficulty in reaching a decision. In this chapter the idea of using the Case Based Reasoning technique to augment FCMs is presented leading to the development of an Advanced Medical Decision Support System. This system is applied in the speech pathology area to diagnose language impairments..

Fuzzy Cognitive Maps Structure for Medical Decision Support Systems

Fuzzy Cognitive Maps (FCMs) are a soft computing technique that follows an approach similar to human reasoning and human decision-making process, considering them a valuable modeling and simulation methodology. FCMs can successfully represent knowledge and experience, introducing concepts for the essential elements and through the use of cause and effect relationships among the concepts Medical Decision Systems are complex systems consisting of irrelevant and relevant subsystems and elements, taking into consideration many factors that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall diagnosis with a different degree. Thus, FCMs are suitable to model Medical Decision Support Systems and the appropriate FCM structures are developed as well as corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.

Decision making in external beam radiation therapy based on fuzzy cognitive maps

This work introduces the use of the soft computing technique of Fuzzy Cognitive Maps to model the decision-making process of radiation therapy and develop an advanced system to estimate the delivered dose to the target volume. During radiotherapy planning numerous factors are taking into consideration that increase the complexity of the decision-making problem. The modeling methodology of FCM has the ability to integrate and consider different, discipline and conflicting factors to determine the dose. A Fuzzy Cognitive Map Model is developed, that can handle imprecise and uncertain information and is used as the decision-making model determining the radiation dose and the complex radiation therapy system. The proposed FCM model is implemented for a practical radiotherapy treatment planning case of gynecological cancer.

Timed Fuzzy Cognitive Maps for Supporting Obstetricians’ Decisions

A crucial decision during delivery is whether a pregnant woman is going to have a physiological delivery or a caesariansection. This decision depends on many factors that regard both the woman and the fetus. An obstetrician continuously monitors the health status of both mother and fetus and he regularly has to make a decision estimating all the possible cases/alternatives, but always eliminating the possibility of setting the woman’s life or the fetus at risk. Medical Decision Support Systems (MDSS) offer doctors a tool to evaluate and/or dispute their decision. This work introduces the use of Timed Fuzzy Cognitive Map (T-FCM) for the case of obstetrics, where time is necessary to be included, as it can influence and change and the overall procedure, leading to a different decision.

The challenge of using unsupervised learning algorithms for fuzzy cognitive maps

Fuzzy cognitive maps is a hybrid method based on fuzzy systems and neural networks and belonging in soft computing. The methodology of developing fuzzy cognitive maps (FCMs) is easily adaptable and relies on human expert experience and knowledge, but it exhibits weaknesses in utilization of learning methods. The external intervention (typically from experts) for the determination of FCM parameters and the convergence to undesired steady states are significant FCM deficiencies. Thus, it is necessary to overcome these deficiencies in order to improve efficiency and robustness of FCM. Weight adaptation methods can alleviate these problems by allowing the creation of less error prone FCMs where causal links-weights are adjusted through a learning process.

Mainstreaming innovation policy in less favoured regions: the case of Patras Science Park, Greece

Creativity and human capital are increasingly being recognised by an expanding body of work on regional economics, and policy and innovative workspaces. A short review of this literature provides the theoretical base for discussing a number of challenges related to mainstreaming creativity in regional and urban economies. Implementing innovation policies in peripheral, less favoured contexts is challenging and requires specific adaptations. This paper argues that a science park and triple-helix institutions can act to animate regional creativity in Europes less favoured regions. It illustrates this point with a case study of the regional economic and policy environment for innovation, creativity and entrepreneurship, in Patras, Greece. Lessons learnt include: the need for consistency and continuity in planning, local ownership of the initiatives, multilevel collaboration in the governance and effective collective learning channels and processes between academia, business and state government. Copyright , Beech Tree Publishing.

The Challenge of Soft Computing Techniques for Tumor Characterization

Computational diagnosis tools are becoming indispensable to support modern medical diagnosis. This research work introduces an hybrid soft computing scheme consisting of Fuzzy Cognitive Maps and the effective Active Hebbian Learning (AHL) algorithm for tumor characterization. The proposed method exploits human experts’ knowledge on histopathology expressed in descriptive terms and concepts and it is enhanced with Hebbian learning and then it classifies tumors based on the morphology of tissues. This method was validated in clinical data and the results enforce the effectiveness of the proposed approach.