Prediction of Severity of Diabetes Mellitus using Fuzzy Cognitive Maps

The objective to develop this research paper is concerned with a system which helps diagnose the severity of diabetes. The disease named diabetes mellitus makes the body unable to handle sugar so it causes thirst, frequency of urination, tiredness and many other symptoms. The diabetes mellitus describes a metabolic disorder characterized by chronic hyperglycemia with disturbances of carbohydrate, fat and protein metabolism resulting from defects in insulin secretion, insulin action, or both. It can be caused by number of factors like pancreatic dysfunction, obesity, hereditary, stress, drugs, alcohol etc. It includes long term damage, dysfunction and failure of various organs. The effects of diabetes mellitus include long term damage and failure of various organs. Diabetes mellitus may present with characteristic symptoms such as thirst, polyuria, blurring of vision, and weight loss. This Paper is implemented on soft computing technique, namely Fuzzy Cognitive Maps (FCM) to find out the presence or absence of diabetes mellitus based on the input of sign/symptoms recorded at three fuzzy levels developed by the domain experts. The large amount of data and information that needs to be handled and integrated requires specific methodologies and tools. The FCM based decision support system was developed with a view to help medical and nursing personnel to assess patient status assist in making a diagnosis. The software tool was tested on 50 cases, showing results with an accuracy of 96%. The analysis of experimental results of different applicants checks the correctness and consistency of decision Support system for correct decision making. Keywords: Fuzzy Logic, FCM, Diabetes Mellitus, Prediction, Symptoms

Методический аппарат когнитивного моделирования социально-экономической системы (университета)

Purpose of the study. The aim of the study is to improve the methodological apparatus of cognitive modeling of socio-economic systems (SES) and predicting the indicators of their functioning and development, which ensures an increase in the accuracy and reliability of the results obtained. Existing models and methods do not fully provide the necessary accuracy and reliability of models that requires the development of the mathematical apparatus of cognitive modeling in terms of improving the quality of the developed cognitive models.Materials and methods. To achieve this goal, methods of an integrated approach to solving the problem, decomposing it into interrelated stages, describing the content of each stage in their relationship and presenting a generalized version of the methodology, taking into account the characteristics of the object of study, were used. The developed approach provides creating a more accurate and reliable cognitive model. The effectiveness of the developed methodological apparatus is shown.Results. A detailed analysis of the existing criteria and approaches to solving the problem of verification of cognitive models was carried out, which showed the absence of a unified methodology and an integrated approach in solving problems of cognitive modeling of SES based on cognitive maps. A set of techniques that implement the stages of cognitive modeling has been developed. The results of a comparative analysis of the developed approach with the existing ones are presented.Conclusion. A comprehensive solution to the problem of creating a cognitive model for analyzing and predicting the activities of a university is proposed, which includes a set of stages: the stage of creating the problem field of the situation; identification of factors and relationships between them; the stage of making a cognitive map and its verification, as well as the stage of analyzing the system characteristics of the cognitive model, validating the cognitive model. The developed methodological apparatus includes a set of techniques aimed at obtaining an adequate model that provides more accurate and reliable results of modeling the object of study.Цель исследования. Целью исследования является совершенствование методического аппарата когнитивного моделирования социально-экономических систем (СЭС) и прогнозирования показателей их функционирования и развития, обеспечивающего повышение точности и достоверности получаемых результатов. Существующие модели и методики не в полной мере обеспечивают необходимую точность и достоверность моделей, что требует развития математического аппарата когнитивного моделирования в части повышения качества разрабатываемых когнитивных моделей.Материалы и методы. Для достижения поставленной цели использованы методы комплексного подхода к решению поставленной задачи, декомпозиции ее на взаимосвязанные этапы, описание содержания каждого этапа в их взаимосвязи и представление обобщенного варианта методики с учетом особенностей объекта исследования. Разработанный подход обеспечивает построение более точной и достоверной когнитивной модели. Показана эффективность разработанного методического аппарата.Результаты. Проведен детальный анализ существующих критериев и подходов к решению задачи верификации когнитивных моделей, который показал отсутствие единой методики и комплексного подхода в решении задач когнитивного моделирования СЭС на основе когнитивных карт. Разработана совокупность методик, реализующих этапы когнитивного моделирования: методика построения проблемного поля ситуации; методика синтеза когнитивной карты, ее структурно-целевого анализа и анализа системных характеристик, а также методика верификации когнитивной модели.Заключение. Предложено комплексное решение задачи построения когнитивной модели анализа и прогнозирования деятельности университета, включающее совокупность этапов: этап построения проблемного поля ситуации; идентификации факторов и связей между ними; этапе построения когнитивной карты и ее верификации, а также этап анализа системных характеристик когнитивной модели, валидации когнитивной модели. Разработанный методический аппарат предназначен для получения адекватной модели, обеспечивающей более точные и достоверные результаты моделирования объекта исследования

Ідентифікація та керування складними системами на основі моделей імпульсних процесів когнітивних карт

У дисертаційній роботі запропоновано системний підхід до проблеми динамічного прийняття рішень у складних системах, що описуються моделями імпульсних процесів когнітивних карт. А саме, розроблено принципи, підходи та методи ідентифікації та керування цими системами на основі застосування та адаптації методів теорії ідентифікації систем та теорії автоматичного керування. При цьому тестові сигнали або керування подаються безпосередньо на деякі вершини когнітивної карти, або, у разі недостатності таких вершин, в ролі керувань можуть виступати змінні ваги ребер карти. У випадку, коли кількість вершин і/або вагові коефіцієнти ребер когнітивної карти невідомі, запропоновано методи ідентифікації розмірності та параметричної ідентифікації системи. Для стабілізації нестійкої когнітивної карти запропоновано ряд методів на основі методів модального керування та керування по еталонних моделях, інші методи пропонуються для приведення координат вершин когнітивної карти на задані рівні. Практичне значення отриманих результатів проілюстровано на численних прикладах когнітивних карт реальних систем – ІТ компанії, комерційного банку, ринку криптовалюти тощо. Результати впроваджено у компанії “Noosphere” та можуть бути застосовані в подальшому для інших складних систем, представлених за допомогою когнітивних карт.In the thesis the system approach to a problem of dynamic decision - making in the complex systems described by models of impulse processes in cognitive maps is suggested. Cognitive maps are a popular and convenient tool for describing, modelling, and analyzing complex multidimensional multiconnected systems of different origin. From a mathematical point of view, a cognitive map is a weighted directed graph, the nodes of which represent the main components (concepts) of a complex system, and the edges are the relationships between them. The dynamics of a complex system described by a cognitive map can be represented as a so-called impulse process, which (in the form of Roberts) is a first-order vector difference equation. To date, many studies are known on the construction and analysis of cognitive maps, but there is almost no work that would systematically address the problems of identification (evaluation) and control of systems represented by cognitive maps. Here the principles, approaches and methods of identification and control of these systems based on the application and adaptation of methods of the theory of system identification and the theory of automatic control are developed. In this case, test signals or controls are fed directly to some nodes of the cognitive map, or, in case of insufficiency of such nodes, the variable weights of the map edges can act as controls. In the case when the number of nodes and /or weight coefficients of the cognitive map edges are unknown, methods for dimension identification and parametric identification of systems based on data from the measured cognitive map nodes are proposed, with measurement errors considered. To stabilize the unstable cognitive map, a number of methods are proposed based on the methods of modal control and control using reference models; other methods are suggested to set the nodes coordinates of the cognitive map to a given level. Cases of multirate impulse process in the cognitive map, of the presence of unmeasured constrained disturbances of arbitrary nature, of the presence of delays, of the need to control the ratios etc. are considered separately. For control of complex systems, the dynamics of which is presented in the form of impulse processes in cognitive maps, in this thesis the adaptation of methods of the automatic control theory is suggested. The dynamics of the controlled system is written by introducing a control vector that acts directly on the nodes of the cognitive map through the variation of its resources. In simpler cases, it is recommended to use methods based on reference models (if you can vary all nodes) or modal control. Also in the case of stable impulse processes, methods based on minimizing the quadratic optimality criterion can be used. But controls by varying the resources of cognitive map nodes may not be sufficient, because in practice there are often few nodes that a decision maker can actually vary. For this case, the paper first proposes a method of control by varying the weights of the edges of the cognitive map, i.e., essentially by changing the degree of influence of some nodes on others. Then the control vector is the vector of increments of weights of some edges of the cognitive map. The design of a discrete controller for such a controlled impulse process is based on the quadratic optimality criterion for this vector. We also consider the case when a decision maker can use both types of control, i.e. variation of nodes resources, and the degree of their influence on each other. For this purpose the method of combined control is developed. Particular attention is paid to the disclosure of uncertainties that arise in the presence of unmeasurable disturbances of arbitrary nature, acting on the coordinates of the nodes of the cognitive map. These can be both external perturbations (including the influence of unmeasured nodes) and internal perturbations caused by inaccurate identification or timevarying weights of the map edges. It is assumed that nothing is known about these disturbances, except that they are limited. From the control theory point of view, this is the problem of robust control. In this research, two methods of robust control of impulse processes of the cognitive map are developed and investigated - on the basis of the method of invariant ellipsoids and on the basis of the H theory. The practical significance of the obtained results is illustrated by numerous examples of cognitive maps of real systems - an IT company, its human resources department, a commercial bank, a cryptocurrency market, a socio-educational student’s process. The results have been implemented by the IT company “Noosphere”, by the Department of dynamic systems control of Space Research Institute of NASU, in the educational process of Igor Sikorsky KPI and can be used in the future for many complex systems represented by cognitive maps

Modélisation multi-agents pour systèmes émergents et auto-organisés

Dans ce travail, une architecture multi-agents pour systèmes émergents et auto-organisés (MASOES) est définie. Cette architecture permet la possibilité de modéliser une système émergent et auto-organisés à travers une société d'agents (homogène ou hétérogène), qui travaillent de manière décentralisée, avec différents types de comportement: réactive, imitative et cognitive. En outre, ils sont capables de modifier dynamiquement leur comportement en fonction de leur état émotionnel, de sorte que les agents peuvent s'adapter dynamiquement à leur environnement, en favorisant l'émergence de structures. Pour cela, un modèle à deux dimensions affectives avec des émotions positives et négatives est proposé. L'importance de ce modèle affectif, c'est qu'il y a pas des modèles émotionnels pour étudier et comprendre comment modéliser et simuler émergentes et auto-organisés des processus dans un environnement multi-agent et aussi, son utilité pour étudier certains aspects de l'interaction sociale multi-agent (influence des émotions dans les comportements individuels et collectifs des agents).Leer fonéticamente D'autre part, une méthodologie pour faire la modélisation avec MASOES est spécifiée, elle explique comment décrire les éléments, relations et mécanismes au niveau individuel et collectif de la société d'agents, qui favorisent l'analyse de phénomène auto-organisatif et émergent sans modéliser le système mathématiquement. Il est également proposé une méthode de vérification pour MASOES basée sur le paradigme de la sagesse des foules et de cartes cognitives floues (CCFs), pour testé les spécifications de design et les critères de vérification établis, tels que: la densité, la diversité, l'indépendance, l'émotivité, l'auto-organisation et émergence, entre autres. Il montre également l'applicabilité de MASOES par des études de cas diverses dans différents contextes comme : Wikipedia, développement de logiciel gratuit et comportement collectif des piétons par le modèle de forces sociales. Finalement, les deux modèles proposés dans MASOES: le modèle multi-agent initiale et le modèle avec CCFs basé sur ce modèle multi-agent initiale se complètent mutuellement. Cela signifie qu'il est possible de tester le modèle multi-agent à travers le méta-modèle basé sur las CCFs. En outre, il représente une nouvelle alternative pour étudier, tester, vérifier ou valider l'auto-organisation et émergence dans les systèmes complexes et de tester le modèle multi-agent, car il est difficile de faire des tests dans ces systèmes pour le niveau d'incertitude et de complexité qu'ils traitent.In this work a multi-agent architecture for self-organizing and emergent systems (MASOES) is defined. This architecture allows the possibility of modeling a self-organizing and emergent system through a society of agents (homogenous or heterogeneous), who work in a decentralized way, with different types of behavior: reactive, imitative or cognitive. Also they are able to dynamically change their behavior according to their emotional state, so that the agents can adapt dynamically to their environment, favoring the emergence of structures. For it, a two-dimensional affective model with positive and negative emotions is proposed. The importance of this affective model is that there are not emotional models for studying and understanding how to model and simulate emergent and self-organizing processes in a multi-agent environment and also, its usefulness to study some aspects of social interaction multi-agent (e.g. the influence of emotions in individual and collective behavior of agents). On the other hand, a methodology for modeling with MASOES is specified, it explains how to describe the elements, relations and mechanisms at individual and collective level of the society of agents, that favor the analysis of the self-organizing and emergent phenomenon without modeling the system mathematically. It is also proposed a verification method for MASOES based on the paradigm of wisdom of crowds and fuzzy cognitive maps (FCMs), for testing the design specifications and verification criteria established such as: density, diversity, independence, emotiveness, self-organization and emergence, among others. It also shows the applicability of MASOES for modeling diverse case studies (in a diversity of contexts) such as: Wikipedia, Free Software Development and collective behavior of pedestrians through the Social Force Model. Finally, the two models proposed in MASOES: the initial multi-agent model and the model with FCMs based on that initial multi-agent model complement each other. This means that it is possible to test the multi-agent model through the meta-model based on FCMs. Besides, it represents a novel alternative to study, test, verify or validate self-organization and emergence in complex systems and test the multi-agent model, since it is difficult to make tests in these systems directly, given the level of uncertainty and complexity they manage