Extension Of Fuzzy Cognitive Maps Research Articles

PRV-FCM: An extension of fuzzy cognitive maps for prescriptive modeling

PRV-FCM: An extension of fuzzy cognitive maps for prescriptive modeling

Case studies of clinical decision-making through prescriptive models based on machine learning

BackgroundThe development of computational methodologies to support clinical decision-making is of vital importance to reduce morbidity and mortality rates. Specifically, prescriptive analytic is a promising area to support decision-making in the monitoring, treatment and prevention of diseases. These aspects remain a challenge for medical professionals and health authorities. Materials and MethodsIn this study, we propose a methodology for the development of prescriptive models to support decision-making in clinical settings. The prescriptive model requires a predictive model to build the prescriptions. The predictive model is developed using fuzzy cognitive maps and the particle swarm optimization algorithm, while the prescriptive model is developed with an extension of fuzzy cognitive maps that combines them with genetic algorithms. We evaluated the proposed approach in three case studies related to monitoring (warfarin dose estimation), treatment (severe dengue) and prevention (geohelminthiasis) of diseases. ResultsThe performance of the developed prescriptive models demonstrated the ability to estimate warfarin doses in coagulated patients, prescribe treatment for severe dengue and generate actions aimed at the prevention of geohelminthiasis. Additionally, the predictive models can predict coagulation indices, severe dengue mortality and soil-transmitted helminth infections. ConclusionsThe developed models performed well to prescribe actions aimed to monitor, treat and prevent diseases. This type of strategy allows supporting decision-making in clinical settings. However, validations in health institutions are required for their implementation.

A Novel Fuzzy Inference Approach: Neuro-fuzzy Cognitive Map

In this study, a new approach based on fuzzy cognitive map (FCM) and neuro-fuzzy inference system (NFIS), called the neuro-fuzzy cognitive map (NFCM), is proposed. Here, the NFCM is used for diagnosis of autoimmune hepatitis (AIH). AIH is a chronic inflammatory liver disease. AIH primarily affects women and typically responds to immunosuppressive therapy with clinical, biochemical, and histological remission. An untreated AIH can lead to scarring of the liver and ultimately to liver failure. If rapidly diagnosed, AIH can often be controlled by medication. NFCM is a new extension of FCM, which employs a NFIS to determine the causal relationships between concepts. In the proposed approach, weights are calculated using the knowledge and experience of experts as well as the advantages of NFIS. This makes the presented model more accurate. Having a high convergence speed, the proposed NFCM model performs well by achieving an AIH diagnosis accuracy of 89.81%. The superiority of the proposed NFCM model over the conventional FCM is that, it uses the NFIS to determine the link weights which train system parameters.

Generalized fuzzy cognitive maps: a new extension of fuzzy cognitive maps

A fuzzy cognitive maps (FCM) is a cognitive map within the relations between the elements. FCM has been widely used in many applicati

Generalized fuzzy cognitive maps: a new extension of fuzzy cognitive maps

A fuzzy cognitive maps (FCM) is a cognitive map within the relations between the elements. FCM has been widely used in many applications such as experts system and knowledge engineering. However, classical FCM is inherently short of sufficient capability of representing and aggregating uncertain information. In this paper, generalized FCM (GFCM) is proposed based on genetic algorithm and interval numbers. An application frame of GFCM is detailed. At last, a numerical example about socio-economic system is used to illustrate the effectiveness of the proposed methodology.

A Study on Problems of Hijras in West Bengal Based on Neutrosophic Cognitive Maps

This paper deals with the problems faced by Hijras in West Bengal in order to find its solutions using neutrosophic cognitive maps. Florentin Smarandache and Vasantha Kandasamy studied neutrosophic cognitive map which is an extension of fuzzy cognitive map by incorporating indeterminacy.Hijras is considered as neither man nor woman in biological point of view. They are in special gender identity (third gender) in Indian society. In their daily life, they have to face many of problems in social aspects. Some of the problems namely, absence of social security, education problem, bad habits, health problem, stigma and discrimination, access to information and service problem, violence, Hijra community issues, and sexual behavior problem are considered in this study. Based on the expert's opinion and the notion of indeterminacy, we formulate neutrosophic cognitive map. Then we studied the effect of two instantaneous state vectors separately on connection matrix E and neutrosophic adjacency matrix N(E).

Bi-linear adaptive estimation of Fuzzy Cognitive Networks

Fuzzy Cognitive Networks (FCNs) have been introduced by the authors as an operational extension of Fuzzy Cognitive Maps (FCMs), initially introduced by Kosko to model complex behavioral systems in various scientific areas. FCNs rely on the admission that the underlying cognitive graph reaches a certain equilibrium point after an initial perturbation. Weight conditions for reaching equilibrium points have been recently derived in [54] along with an algorithm for weight estimation. In this paper, the conditions are extended to take into account not only the weights of the map but also the inclination parameters of the involved sigmoid functions, increasing the structural flexibility of the network. This in turn gives rise to the development of a new adaptive bilinear weight and sigmoid parameter estimation algorithm, which employs appropriate weight projection criteria to assure that the equilibrium is always achieved.

Distributing emotional services in Ambient Intelligence through cognitive agents

Ambient Intelligence (AmI) is a pervasive computing paradigm whose main aim is to design smart environments composed of invisible, connected, intelligent and interactive systems, which are naturally sensitive and responsive to the presence of people, providing advanced services for improving the quality of life. Nevertheless, AmI systems are more than a simple integration among computer technologies; indeed, their design can strongly depend upon psychology and social sciences aspects describing, analysing and forecasting the human being status during the system's decision making. This paper introduces a novel methodology for AmI systems designing that exploits a service-oriented architecture whose functionalities are performed by a collection of so-called cognitive agents. These agents exploit a novel extension of Fuzzy Cognitive Maps benefiting on the theory of Timed Automata and a formal method for representing human moods in order to distribute emotional services able to enhance users' comfort and simplify the human/systems interactions. As will be shown in experimental results, where a usability study and a confirmation of expectations test have been performed, the proposed approach maximizes the system's usability in terms of efficiency, accuracy and emotional response.

Dynamical cognitive network - an extension of fuzzy cognitive map

We present the dynamic cognitive network (DCN) which is an extension of the fuzzy cognitive map (FCM). Each concept in the DCNs can have its own value set, depending on how precisely it needs to be described in the network. This enables the DCN to describe the strength of causes and the degree of effects that are crucial to conducting meaningful inferences. The arcs in the DCN define dynamic, causal relationships between concepts. Structurally, DNCs are scalable and more flexible as compared to FCMs. A DCN can be as simple as a cognitive map and FCM, or as complex as a nonlinear dynamic system. To demonstrate the potential applications of DCNs, we present some simulation results. This paper represents our first attempt to develop a dynamic fuzzy inference system using causal relationships. There are many interesting and challenging theoretical and practical issues in DCNs open to further research.