Fuzzy Cognitive Map

Differential Hebbian Learning (DHL) was proposed by Kosko as an unsupervised learning scheme for Fuzzy Cognitive Maps (FCMs). DHL can be used with a sequence of state vectors to adapt the causal link strengths of an FCM. However, it does not guarantee learning of the sequence by the FCM and no concrete procedures for the use of DHL has been developed. In this paper a formal methodology is proposed for using DHL in the development of FCMs in a decision support context. The four steps in the methodology are: (1) Creation of a crisp cognitive map; (2) Identification of event sequences for use in DHL; (3) Event sequence encoding using DHL; (4) Revision of the trained FCM. Feasibility of the proposed methodology is demonstrated with an example involving a dynamic system with feedback based on a real-life scenario.

Improving knowledge worker productivity has been one of the most important tasks of the century. However, we have few measures or management interventions to make such improvement possible, and it is difficult to identify patterns that should be followed by knowledge ...

In this paper, we introduce an extension of our presented cognitive-based emotion model [27][28]and [30], where we enhance our knowledge-based emotion unit of the architecture by embedding a fuzzy rule-based system to it. The model utilizes the cognitive parameters dependency and their corresponding weights to regulate the robot's behavior and fuse their behavior data to achieve the final decision in their interaction with the environment. Using this fuzzy system, our previous model can simulate linguistic parameters for better controlling and generating understandable and flexible behaviors in the robots. We implement our model on an assistive healthcare robot, named Robot Nurse Assistant (RNA) and test it with human subjects. Our model records all the emotion states and essential information based on its predefined rules and learning system. Our results show that our robot interacts with patients in a reasonable, faithful way in special conditions which are defined by rules. T...

Agent Based Modeling and Simulation & Usage of Oral and Dental Diseases to predict criminals & crimes: in part one (this paper) of our research description we only try to describe the knowledge of Cognitive Science, Agent-Based Simulation, Social Simulation, Artificial Intelligence and Multi Agent Systems. In the other hand in the second part (next paper) we will show how we can use the oral & dental diseases as a most important data for the Agent-Based Social Simulation, to early predict the criminals and crimes. In social simulating to create social behavioral modeling we need to apply the cognitive abilities. The cognitive ability is the most important property for the social agent. But the agent architecture is not included this ability. The agent with the cognitive ability will be able to improve the models of the behaviors in the society. This ability not only will be useful in the area of the society security and immigrant’s behavior in the target society, but also it is very...

The Psychological Basis of Cognitive Modeling.- Parallel and Distributed Logic Programming.- Distributed Reasoning by Fuzzy Petri Nets: A Review.- Belief Propagation and Belief Revision Models in Fuzzy Petri Nets.- Building Expert Systems Using Fuzzy Petri Nets.- Distributed Learning Using Fuzzy Cognitive Maps.- Unsupervised Learning by Fuzzy Petri Nets.- Supervised Learning by a Fuzzy Petri Net.- Distributed Modeling of Abduction, Reciprocity, and Duality by Fuzzy Petri Nets.- Human Mood Detection and Control: A Cybernetic Approach.- Distributed Planning and Multi-agent Coordination of Robots.

Reengineering consists of conceiving the radical improvements that must be introduced into the organization of a manufacturing system so that the product managerial attributes satisfy a target market. Fuzzy cognitive maps are fuzzy signed digraphs that cannot justify their outputs because they are full of feedback loops. This paper presents an intelligent system that supports reengineering. The suggested system uses a new type of neural network that has a fuzzy cognitive map structure but justifies its outputs. The intelligent system is applied to identify the target organization for a problematic manufacturing system. In addition, it is used to generate new types of organizations for manufacturing systems that feed new types of markets.

Every development and production process needs to operate within a circular economy to keep the human being within a safe limit of the planetary boundary. Policymakers are in the quest of a powerful and easy-to-use tool for representing the perceived causal structure of a complex system that could help them choose and develop the right strategies. In this context, fuzzy cognitive maps (FCMs) can serve as a soft computing method for modelling human knowledge and developing quantitative dynamic models. FCM-based modelling includes the aggregation of knowledge from a variety of sources involving multiple stakeholders, thus offering a more reliable final model. The average aggregation method for weighted interconnections among concepts is widely used in FCM modelling. In this research, we applied the OWA (ordered weighted averaging) learning operators in aggregating FCM weights, assigned by various participants/ stakeholders. Our case study involves a complex phenomenon of poverty eradi...

The features of a dataset play an important role in the construction of a machine learning model. Because big datasets often have a large number of features, they may contain features that are less relevant to the machine learning task, which makes the process more time-consuming and complex. In order to facilitate learning, it is always recommended to remove the less significant features. The process of eliminating the irrelevant features and finding an optimal feature set involves comprehensively searching the dataset and considering every subset in the data. In this research, we present a distributed fuzzy cognitive map based learning-based wrapper method for feature selection that is able to extract those features from a dataset that play the most significant role in decision making. Fuzzy cognitive maps (FCMs) represent a hybrid computing technique combining elements of both fuzzy logic and cognitive maps. Using Spark’s resilient distributed datasets (RDDs), the proposed model ...

Research suggests that doctors are failing to make use of technologies designed to optimize their decision-making skills in daily clinical activities, despite a proliferation of electronic tools with the potential for decreasing risks of medical and diagnostic errors. This paper addresses this issue by exploring the cognitive basis of medical decision making and its psychosocial context in relation to technology. We then discuss how cognitive-led technologies – in particular, decision support systems and artificial neural networks – may be applied in clinical contexts to improve medical decision making without becoming a substitute for the doctor’s judgment. We identify critical issues and make suggestions regarding future developments.

Strategic thinking is one of the most important capabilities which managers of today’s organizations must possess. Holding companies, due to the kind of problems that they experience, are in serious need of managers capable of strategic thinking. The present research has been conducted with the aim of identifying the individual dimensions of strategic thinking in holding companies’ managers in Iran. In this regard, a number of managers who have had the experience of being members of the board of directors or working as CEOs of these firms have been asked to express their opinions about strategic thinking and their views have been analyzed using fuzzy cognitive maps. Results suggest that having vision, ability to analyze, having systems thinking, ability to question, creativity, ability to make synergy and ability to create advantage are the main elements of strategic thinking in successful managers of holding companies. In addition, the relationships among these variables have been ...

Structured Clinical Documentation is a fundamental component of the healthcare enterprise, linking both clinical (e.g., electronic health record, clinical decision support) and administrative functions (e.g., evaluation and management coding, billing). One of the challenges in creating good quality documentation templates has been the inability to address specialized clinical disciplines and adapt to local clinical practices. A one-size-fits-all approach leads to poor adoption and inefficiencies in the documentation process. On the other hand, the cost associated with manual generation of documentation templates is significant. Consequently there is a need for at least partial automation of the template generation process. We propose an approach and methodology for the creation of structured documentation templates for diabetes using Natural Language Processing (NLP).

In this paper, we introduce an novel emotional agent system in 3D virtual world based on OCC (Ortony, Clore and Collins) theory, FCM (Fuzzy Cognitive Map) and GoalNet. The agent system is designed based on Goal Net model. Emotional modeling and decision making are based on OCC and FCM inference. Emotions modeled by the OCC model are incorporated into FCM

To develop comprehensive models for predicting the pH and electrical conductivity of surface water in Maiganga coal mine and environs affected by mining activities. Methodology. The research utilizes a combination of in-situ measurement, laboratory analysis, modeling technique using Ansys Workbench and Linear Regression for predicting the content of pollutants. In-situ measurement/data collection in the upstream and downstream were carried out to evaluate the potential impact of mining activities on surface and ground water quality. Electrical conductivity and pH were measured on the samples that were collected using Oakton 5/6 pH meter and TDS/EC meter. Findings. According to the results, the regression statistics model of pH and electrical conductivity (EC) shows that the predicted values have a pH range of 4.7-7.05 and a mean pH value of 5.5. In contrast, while the EC ranges from 454.52 to 2,720.68 µs/cm (EC) with a mean value of 905 µs/cm of the downstream flow which is completely dependent on the mine inlet (pH-in and EC-in). The findings show a direct correlation between surface water pH, electrical conductivity, and mining activities in the Maiganga coal mine area and their detrimental effects on the ecosystem and water quality. Originality. The results were obtained directly from the mine site during field visit and can be compared to data from active coal mine sites. Practical value. The detrimental effect of the results of mining activities can be controlled if monitoring sensors are introduced at mines' effluent outlet to alert the mine management of possible danger in real time.

A method of complex analysis and multidimensional forecasting of the state of intelligence objects is proposed to increase the accuracy of their state assessment. The object of research is decision support systems. The subject of research is the process of decision-making in management problems using artificial intelligence methods. The hypothesis of research is to increase the efficiency of decision-making with a given assessment reliability. The proposed method is based on a combination of fuzzy cognitive and temporal models, an advanced cat swarm optimization algorithm and evolving artificial neural networks. The method has the following sequence of actions: ‒ input of initial data; ‒ processing of initial data taking into account uncertainty about the state of heterogeneous intelligence objects; ‒ construction of a fuzzy temporal ontological model of heterogeneous intelligence objects; ‒ conclusion on the state of heterogeneous intelligence objects; ‒ correction of the fuzzy tem...

In this work, we introduce EnsembleForge, a versatile framework designed to streamline machine learning experimentation and simplify classification tasks. Leveraging the stacking ensemble method, EnsembleForge offers an intuitive platform built upon the Scikit-learn library. This framework facilitates seamless model implementation and evaluation, supporting both Randomized and Grid Search for hyperparameter optimization. Our experiments with publicly available datasets demonstrate the ease of use and effectiveness of EnsembleForge in experimenting with various algorithms. With its adaptability and innovation, EnsembleForge showcases promising potential to serve as an asset for researchers and practitioners seeking to achieve optimal model performance in their machine learning endeavors.