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In this volatile, uncertain, complex, and ambiguous (VUCA) era, resilient and sustainable construction methods, such as prefabricated construction, are essential for addressing the planet's sustainability challenges. However, disruptions in the prefabricated construction supply chain (PCSC) frequently arise, seriously impeding the performance of prefabricated building projects. Therefore, this study aims to identify the factors influencing the prefabricated construction supply chain (RPCSC) and analyze their intrinsic interconnections. Initially, an exhaustive literature review was conducted to identify the primary factors affecting the RPCSC. Subsequently, the Delphi technique was applied to validate and refine the list of factors, resulting in the identification of 11 key concepts. Finally, the impact of these concepts on the RPCSC, along with their interactions, was assessed using the fuzzy cognitive map (FCM) approach. The results indicate that these factors can be ranked by their degree of effect on the RPCSC: information exchange/sharing, research and development, the performance of prefabricated components, decision alignment, the construction of prefabricated buildings, relationship quality among members, professional management personnel/labor quality, supply–demand consistency, cost/profit sharing, policies and regulations, and transport risk. Furthermore, this study elucidates both the individual and synergistic effects of these factors on the RPCSC by constructing a pathway map. [ABSTRACT FROM AUTHOR]

Among the 40 identified natural hazards, Iran is vulnerable to 30. Tehran, as the largest metropolitan in Iran, was fraught with perils of which drought and water stress are the most significant. Apart from, damaging the environment and creating or intensifying secondary natural calamities, drought can impair social, economic, political, and physical aspects of life. Unfortunately, the management based on the development of grey infrastructure has exacerbated the vulnerabilities and diminished Tehran’s resilience. This research aims to address the effective factors causing the risk of drought and water stress in the city of Tehran and asses these factors. The study attempts to scrutinize what are the most important priorities of vulnerability. Addressing priorities can serve as a basis for decision-making and multi-scale planning of drought risk reduction. Such urgencies contribute to adopting preventive measures rather than crisis management. The theoretical literature of the current research is analytical-descriptive, for which data was collected from books, articles, reports, and upstream projects. Also, to evaluate the risk landscape, the risk matrix was developed based on the interactive model, and then the most important causes of vulnerability were extracted, assessed, and analyzed using fuzzy cognitive maps (FCMs). Research findings and the main drought risk scenarios of Tehran show that at the micro level, “high waste of water resources”, “change of land use and destruction of natural infrastructure”, and “urban development disproportionate to water capacities” are the main drivers. At the macro level, this issue is caused by repeating the cycle of “physical-natural-perceptual” damage and discounting the city as a human-environmental system.

In the prevailing technological paradigm, there exists various toolkits which researchers and practitioners can leverage to use Fuzzy Cognitive Maps (FCMs). However, a systematic management toolkit that encompasses security mechanisms, Kosko simulations, and what-if scenario analysis with AI-enabled inference remains scarce. To address this, we introduce FCM-VSS (Fuzzy Cognitive Maps – Visualizer, Simulator, and Summarizer), a robust web application that integrates Advanced Encryption Standard - Galois Counter Mode (AES-GCM) security, locally hosted Ollama-based AI agents for FCM summarization, and customizable Kosko inference mechanisms. This paper outlines FCM-VSS's architecture and web implementation, emphasizing on its potential as a secure, AI-powered FCM management tool.

In this paper, we develop a Fuzzy Cognitive Map (FCM)-based framework for investigating user satisfaction in information services and further focus on how various affecting factors are connected to the overall user service experience. Conventional approaches usually perform poorly with extensive uncertainties or difficult, complex relationships between service quality, response time, usability and personalization. As a case in point, FCMs give us an appropriate mathematical framework to model causal relationships and simulate dynamic interplays between these factors. In the study, critical factors are taken as nodes in FCM and then it establishes causal weights between these so that their importance can be quantified. The state of every concept is then updated via matrix-vector operation and iterative updates with sigmoid activation function until convergence. A comprehensive case study illustrates an actual usage of the FCM framework, and highlights its ability to isolate key drivers that impact satisfaction and suggests avenues for improvement. Next steps include integrating IoT sensors for real-time monitoring, hybrid models with machine learning to improve predictions and relevant applications in different fields which goes from e-commerce to healthcare. This experimentation underscores FCMs potential in decision-making procedures which presents good insight towards enhancing users experience when dealing with information service. [ABSTRACT FROM AUTHOR]

Transhumance and its associated heritage are extremely complex and dynamic systems, and their conservation requires the analysis of interdisciplinary factors. To this end, fuzzy cognitive maps (FCMs) and Delphi surveys were applied for the first time in the field of heritage conservation. The model was applied to the tangible and intangible transhumance heritage of Andorra to determine its current state of conservation and to evaluate strategies for its preservation. Two panels of experts worked on the development of the model. Five experts with profiles related to conservation and transhumance heritage formed the first panel, which designed the preliminary FCMs, while seven experts in Andorran cultural heritage (panel 2) adapted the preliminary FCM model to Andorran transhumance heritage using Delphi surveys. The FCM model allowed us to analyze the influence of different variables on the conservation of transhumance heritage and to assess policy decisions. Further studies will focus on the implementation of this model in other countries to establish common recommendations for the conservation of the cultural heritage of transhumance. [ABSTRACT FROM AUTHOR]

In this volatile, uncertain, complex, and ambiguous (VUCA) era, resilient and sustainable construction methods, such as prefabricated construction, are essential for addressing the planet’s sustainability challenges. However, disruptions in the prefabricated construction supply chain (PCSC) frequently arise, seriously impeding the performance of prefabricated building projects. Therefore, this study aims to identify the factors influencing the prefabricated construction supply chain (RPCSC) and analyze their intrinsic interconnections. Initially, an exhaustive literature review was conducted to identify the primary factors affecting the RPCSC. Subsequently, the Delphi technique was applied to validate and refine the list of factors, resulting in the identification of 11 key concepts. Finally, the impact of these concepts on the RPCSC, along with their interactions, was assessed using the fuzzy cognitive map (FCM) approach. The results indicate that these factors can be ranked by their degree of effect on the RPCSC: information exchange/sharing, research and development, the performance of prefabricated components, decision alignment, the construction of prefabricated buildings, relationship quality among members, professional management personnel/labor quality, supply–demand consistency, cost/profit sharing, policies and regulations, and transport risk. Furthermore, this study elucidates both the individual and synergistic effects of these factors on the RPCSC by constructing a pathway map.

Exercise has a major impact on quality of life, as it improves mental health by reducing stress and negative emotions. Exercise should be personalized in order to be both effective and safe for the target individual, as excessive and high-intensity training may cause health problems, such as immune or respiratory system dysfunction. Although deep learning approaches have been proposed for exercise recommendations, it still remains a challenge to design a personalized coaching system that provides interpretations about its decisions, thereby gaining user trust. This paper introduces a novel user-centered virtual coaching (VC) framework for physical exercise. In this framework a Fuzzy Cognitive Map (FCM) plays the role of a virtual coach; it configures suitable paths for users to navigate, based on their desired exercise intensity, as well as their physiological measurements obtained from wearable sensors, during their exercise. The contributions of the proposed framework, named FCMCoach, include the following: i) a novel VC FCM that automatically determines its structure through a supervised process; ii) a dynamic personalized adaptation mechanism enabling the FCM graph to change its parameters based on the evoked physiological measurements and adapt to any desired path the users want to navigate, iii) an inherent interpretation mechanism capable of explaining the decisions of the FCM to the users, thus gaining their trust. The effectiveness of the proposed framework is demonstrated on a dataset representing a population of 200 subjects, created using measurements from a real recreational area in Greece, with several nature trails. The results validate its capacity to determine suitable paths personalized to the users’ needs with an accuracy of 83%.

The steady turning motion of merchant ships is modeled according to industry specifications. However, challenges arise when motorboats are modeled. This study proposes a novel data-driven multi-block fuzzy cognitive map (FCM) model trained based on sea trials with four ship states. The optimal positions sampled by two different types of sensors were modeled using inverse variance weighting, which takes both Cartesian coordinate transformation and the constraints of the locations of the apparatuses into consideration. Combining these samplings with a scheme of multi-block FCMs, we conducted a study on data from motorboat trials. Our results closely approximate the data from motorboat trials at sea and are validated by a generated dataset of the classical model. Furthermore, we reveal the characteristics of our scheme, including the number of data blocks, the boundary of each block, and the parameters of the FCM for each block. As opposed to the classical method, the proposed scheme is insensitive to speed. This study presents a promising step toward mining modeling information from ship trials. [ABSTRACT FROM AUTHOR]

One drawback of using the existing one-step forecasting models for long-term time series prediction is the cumulative errors caused by iterations. In order to overcome this shortcoming, this article proposes a trend-fuzzy-granulation-based adaptive fuzzy cognitive map (FCM) for long-term time series forecasting. Different from the original FCM-based forecasting models, a class of trend fuzzy information granules is built to represent the trend, fluctuation range, and trend persistence of various segments of time series, which are more instrumental and comprehensive than simple magnitude information. Thus, the proposed forecasting model is a granular model according to the form of its inputs and outputs. In an original FCM-based forecasting model, the causal relationships among concepts remain unchanged throughout the training of the whole dataset, however, in reality, the causal relationships may change with the state of concepts. Therefore, it is unreasonable to use the invariable causal relationships which often result in poor predictions. In view of this, we construct an adaptive FCM where different causal relationships are built to forecast concepts of different states. This is the first time to forecast trend fuzzy information granules using an adaptive FCM. Compared with the existing classical forecasting models, the proposed forecasting model achieves superior performance which is verified through a series of experimental studies. [ABSTRACT FROM AUTHOR]

One of the essential factors of project success is selecting the proper delivery method. This study aimed to provide a new hybrid decision-making framework to assist project stakeholders in evaluating and selecting the most appropriate Project Delivery System (PDS) and documenting the decision process. For this purpose, the selection factors of PDSs were obtained from a literature review, and critical selection factors were screened based on the fuzzy Delphi method, whereby expert feedback was on Information and Communication Technology (ICT) projects was obtained. Subsequently, the ICT project risks were identified and categorized into six competitive constraints, including time, cost, quality, reputation, value, and scope, and the risk factors were prioritized in each area. Then, the effect of project risks on the decision criteria was investigated using a fuzzy cognitive map (FCM). Finally, the PDSs were ranked through Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). This article researched a novel multi-layer decision system combining the FCM and FTOPSIS techniques. The decision criteria received their weights from the evaluation of the causal relationships between PDS selection factors and project risks. Thus, PDSs were ranked based on different project characteristics, the opinions of stakeholders, and the effect of project risks on the decision-making process; this increased the likelihood of project success. The results showed that the impact of the most critical project risks on the selection factors was so severe that they changed the weight of the criteria in the decision matrix and, subsequently, changed the ranking of decision options. [ABSTRACT FROM AUTHOR]

The steady turning motion of merchant ships is modeled according to industry specifications. However, challenges arise when motorboats are modeled. This study proposes a novel data-driven multi-block fuzzy cognitive map (FCM) model trained based on sea trials with four ship states. The optimal positions sampled by two different types of sensors were modeled using inverse variance weighting, which takes both Cartesian coordinate transformation and the constraints of the locations of the apparatuses into consideration. Combining these samplings with a scheme of multi-block FCMs, we conducted a study on data from motorboat trials. Our results closely approximate the data from motorboat trials at sea and are validated by a generated dataset of the classical model. Furthermore, we reveal the characteristics of our scheme, including the number of data blocks, the boundary of each block, and the parameters of the FCM for each block. As opposed to the classical method, the proposed scheme is insensitive to speed. This study presents a promising step toward mining modeling information from ship trials.

Most time series clustering methods mainly focus on univariate time series (UTS). Compared with UTS, multivariate time series (MTS) consists of multiple components. Although interest in MTS clustering is increasing, its performance is far from satisfactory. Most traditional MTS clustering methods may have two limitations. First, they do not consider both the temporal features of each component and the relationship between the components. Second, they can only identify the local relationship between adjacent data, but cannot obtain long-distance global relationships and capture clusters of arbitrary shapes. In this paper, we develop a method for MTS clustering based on fuzzy cognitive maps (FCMs) and community detection, termed as MTSC-FCM-CD. To overcome the first limitation, we use FCM to represent MTS; FCM can extract temporal features while preserving the relationship between components in MTS. To overcome the second limitation, we use the community detection algorithm to cluster the global relations, which is different from the traditional nearest neighbor distance-based method. In the calculation process, the similarity between FCMs should be used to build a complex network. Existing methods calculate the similarity between FCMs, only considering the numerical characteristics but ignoring the topological structure. To solve this problem, we design a two-stage similarity measure to build a complex network. In comparison to the existing methods, the experimental results on twenty benchmark datasets demonstrate the effectiveness of MTSC-FCM-CD in MTS clustering. [ABSTRACT FROM AUTHOR]

Abstract This paper explores the power of stock trading trend using an integrated New Threshold Fuzzy Cognitive Maps (NTFCMs) Markov chain model. This new model captures the positive as well as the negative jumps and predicts the trend for different stocks over 4 months which follow an uptrend, downtrend and a mixed trend. The mean absolute per cent error (MAPE) tolerance limits, the root mean square error (RMSE) tolerance limits are determined for various stock indices over a multi-timeframe period and observed for the existing methods lying within the defined limits. The results show for every ‘n’ number of predictions made, the predicted close value of the day’s stock price was within tolerance limit with 0 % error and with 100% accuracy in predicting the future trend.

Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods. [ABSTRACT FROM AUTHOR]

The problem of time series prediction based on fuzzy cognitive maps (FCMs) is unresolved. Although many methods have been proposed to cope with this issue, the performance of these methods is far from satisfactory. Traditional FCM-based predictors have three limitations. First, current feature extraction operators are incapable of learning good representations of original time series. Second, current methods use just the output of FCMs to predict the next value; they do not directly utilize the important information of the latent features. Third, current FCM-based predictors optimize each component individually, thereby leading to low prediction accuracy. For example, these methods first optimize the feature extraction operator and then learn the FCMs from the latent features; they do not simultaneously optimize the whole prediction model. In this article, we develop a framework based on a sparse autoencoder (SAE) and a high-order FCM (HFCM) to address the time series prediction problem; we refer this framework as SAE-FCM. To overcome the first limitation of current methods, an SAE is employed to extract features from original time series. Unlike current FCM-based predictors, our method combines the output of both the SAE and the HFCM to calculate the predicted value, thereby overcoming the second limitation of traditional FCM-based predictors. In an application of the idea of “fine tuning” in deep learning, the weights of SAE-FCM can be updated by the batch gradient descent method if the prediction errors are great. Thus, we can optimize SAE-FCM as a whole and overcome the third limitation. We validate the performance of SAE-FCM on ten datasets. Compared with the experimental results obtained by using state-of-the-art methods, the experimental results obtained by using SAE-FCM demonstrate the effectiveness of our method. Extensive experiments also show that SAE-FCM can effectively overcome the above limitations. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive maps (FCMs) play an important role in high-level reasoning but are limited in their ability to model complex systems with singularities. We are interested in systems that exhibit discontinuous behaviors as one or more of their internal node states approach a threshold. In a new approach to FCM dynamics, we define general classes of aggregation functions which “jump” to a boundary value when any input crosses a threshold, or when all inputs do. The threshold value is a context-dependent parameter which can be readily understood by subject matter experts. Aggregation functions are applied separately to positively and negatively causal antecedents to each node then combined to form the nodal state. This modeling is applied in Computing with Words (CWW) settings, in which link strengths and activation levels are elicited using vocabulary words represented by interval type-2 fuzzy membership functions. We illustrate the behaviors of these novel FCM systems in comparison with their nonsingularity versions. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive maps (FCMs) as a kind of knowledge-based tools are widely applied to model complex dynamical systems using causal relations. Besides the representation and reasoning of systems behaviors, how to control the given systems into a desirable target by causal objects established by FCMs is also an open problem. Although, so far, there are some existing works about the applications of FCMs on the control-related problems, it is still a lack of the theoretical analysis in this domain. In this paper, the controllability of k-valued FCMs is studied. To improve the universality of models, a temporal extension of generalized FCMs is implemented. By means of semitensor product, the algebraic representation of k-valued FCMs with controls is established and a generalized formula of control-depending network transition matrices is achieved. A necessary and sufficient condition is proved to determine the control-depending fixed points of k-valued FCMs with temporalization. By utilizing three kinds of controls, the controllability of the discrete FCMs is discussed, respectively. The reachability condition of a specific target state from a given initial state at time s is studied, and the reachable set along with the corresponding reachable probability are also provided by analytic formula. Results provide a way to make FCMs evolving into the designed states by controls, which can further conduct the behaviors of the modeled systems in reality. Examples are shown to demonstrate the effectiveness and feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents a time-series classification method based on fuzzy cognitive maps. We advocate that fuzzy cognitive maps provide a sound representation of time series, and we can construct a classification mechanism based on them. The classifier has to distinguish maps constructed for time series belonging to different classes. The proposed classification procedure evaluates similarity of fuzzy cognitive maps, and it is done by comparing weight matrices based on the same set of concepts. A weight matrix describes relationships between concepts in a map. Concepts represent the underlying data, because they are extracted via a data-driven clustering procedure. Each data point of a time series is related to each concept, and we evaluate the strength of relationships with a membership function. This paper investigates performance of the proposed approach on a suite of real-world datasets. We compare classification accuracy of our method with 37 state-of-the-art time-series classification methods. Experiments show that the proposed method is performing well. In many cases, it is better than its competitors. [ABSTRACT FROM AUTHOR]

Learning large-scale fuzzy cognitive maps (FCMs) with the sparse attribute automatically from time series without prior knowledge remains a challenging problem. Most existing automated learning methods were applied to learn small-scale FCMs, and the learned FCMs are much denser than the maps constructed by human experts. Learning FCMs is the procedure of judging whether there are connecting edges and determining the weights of connecting edges. Thus, we transform the problem of learning FCMs into a biobjective optimization problem with two objects of minimizing the measure error and the number of nonzero entries, respectively. To solve this optimization problem, a preference-based iterative thresholding evolutionary biobjective optimization algorithm for learning FCMs is proposed. The strategy focuses on the knee area of the Pareto front (PF) with preference on the solutions near the true sparsity. Moreover, an initialization operator based on random forest is proposed to increase the speed of convergence toward the PF. The experiments on large-scale synthetic data with varying sizes and densities and the application to the gene regulatory network reconstruction problem have been conducted to demonstrate that our proposal matches or exceeds the existing state-of-the-art FCM learning approaches in most cases in terms of four measures, namely, Data_Error, Out_of_Sample_Error, Model Error, and SS_Mean. The Data_Error obtained by the proposed method can achieve 3.07E-06 even when the number of nodes reaches 200. We also demonstrate the effectiveness of the proposed initialization operator and the preference-based strategy, which can result in a fast convergence speed and higher accuracy. [ABSTRACT FROM AUTHOR]

Reliability is the fundamental element of safety operation of all systems. Human performance plays a significant role in the development and operation of complex systems so it is obvious that human errors have serious effects on the complex system performance. Healthcare services sector is one of the major fields that require human reliability assessment as most of the applications involve human handling, decisions and processing. This study aims to draw a complete representation of doctors' behavior leading to clinical error by acquiring a complete causal relation model between all possible performance-influencing factors (PIFs) in healthcare operations which have been determined and analyzed for various healthcare operations. Fuzzy Cognitive Maps (FCM) has been used to procure an explicit understanding of human behavior and all of the reasons relying under his behavior. In this respect, four doctors working in different high-risk involving healthcare fields evaluated all PIFs. The causal relationships are obtained and evaluated through a sensitivity analysis using different alpha-cuts. In real-life decisions, decision-makers may have different confidence levels on expert judgments. The sensitivity analysis procures to the decision-makers a perspective that explains how the fuzziness in judgment may affect the solution robustness. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive maps (FCMs) have been successfully used to model and predict stationary time series. However, it still remains challenging to deal with large-scale nonstationary time series, which have trend and vary rapidly with time. In this paper, we propose a time-series prediction model based on the hybrid combination of high-order FCMs (HFCMs) with the redundant wavelet transform to handle large-scale nonstationary time series. The model is termed as Wavelet-HFCM. The redundant Haar wavelet transform is applied to decompose original nonstationary time series into multivariate time series; then, the HFCM is used to model and predict multivariate time series. In learning HFCMs to represent large-scale multivariate time series, a fast HFCM learning method is designed on the basis of ridge regression to reduce the learning time. Finally, summing multivariate time series up yields the predicted time series at each time step. Compared with existing classical methods, the experimental results on eight benchmark datasets show the effectiveness of our proposal, indicating that our prediction model can be applied to various prediction tasks. [ABSTRACT FROM AUTHOR]

Recently, a learning procedure to improve the overall convergence of sigmoid fuzzy cognitive maps used in pattern classification was proposed. The algorithm estimates the slope of each sigmoid neuron while preserving the causal weights. This paper proposes a more realistic error function for this algorithm, which is based on 1) the dissimilarity between two consecutive responses, and 2) the dissimilarity between the current output and the expected one. As a second contribution, we introduce sufficient conditions to arrive at stability features. These conditions allow assessing the accuracy–convergence tradeoff attached to the proposed learning procedure. [ABSTRACT FROM AUTHOR]

Rule-based fuzzy cognitive maps (RBFCMs) have been developed for modeling nonmonotonic, uncertain, cause-effect systems. However, the standard reasoning and impact accumulation mechanisms developed for RBFCMs assume that the level of variation that a fuzzy set represents is directly linked with the shape of the fuzzy set. It poses a big restriction on how the corresponding fuzzy sets have to be constructed. In this paper, we propose a new reasoning and impact accumulation mechanisms which take into consideration standard semantics of fuzzy sets, where their uncertainty is measured by fuzziness. New type of complex fuzzy relationships and reasoning on them is introduced to model a joint impact of several causal nodes on one effect node. With these new mechanisms, RBFCMs become much more flexible, provide more means to capture complexity of real-world systems, and are less computational demanding than standard mechanisms. The advantages of the new RBFCMs are demonstrated using different examples and compared with standard mechanisms. [ABSTRACT FROM PUBLISHER]

Learning large-scale sparse fuzzy cognitive maps (FCMs) from observed data automatically without any prior knowledge remains an outstanding problem. Most existing methods are slow and have difficulty in dealing with large-scale FCMs, because of the large searching space. We develop a framework based on compressed sensing (CS), a convex optimization method, to learn large-scale sparse FCMs, called CS-FCM. Combining with the sparsity of FCMs, the task of learning FCMs is first decomposed into sparse signal reconstruction problems. The ability of CS to exactly recover the sparse signals provides CS-FCM the probability to exactly learn FCMs. In the experiments, CS-FCM is applied to learn both synthetic data with varying sizes and densities and real-life data. The results show that CS-FCM obtains good performance by just learning from a small amount of data. CS-FCM can effectively learn sparse FCMs with 1000 nodes and even more, which have one million weights to be determined. CS-FCM is also applied to reconstruct gene regulatory networks (GRNs), and the well-known benchmark datasets DREAM3 and DREAM4 are tested. The results show that CS-FCM also obtains high accuracy in reconstructing GRNs. CS-FCM establishes a paradigm for learning large-scale sparse FCMs with high accuracy. [ABSTRACT FROM AUTHOR]

Background and Objective A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems. This review study is conducted to identify different FCM structures used in MDSS designs. The best structure for each medical application can be introduced by studying the properties of FCM structures. Methods This paper surveys the most important decision- making methods and applications of FCMs in the medical field in recent years. To investigate the efficiency and capability of different FCM models in designing MDSSs, medical applications are categorized into four key areas: decision-making, diagnosis, prediction, and classification. Also, various diagnosis and decision support problems addressed by FCMs in recent years are reviewed with the goal of introducing different types of FCMs and determining their contribution to the improvements made in the fields of medical diagnosis and treatment. Results In this survey, a general trend for future studies in this field is provided by analyzing various FCM structures used for medical purposes, and the results from each category. Conclusions Due to the unique specifications of FCMs in integrating human knowledge and experience with computer-aided techniques, they are among practical instruments for MDSS design. In the not too distant future, they will have a significant role in medical sciences. [ABSTRACT FROM AUTHOR]

Learning Management Systems (LMSs) under blended (b-) learning modality can efficiently support Online Learning Environments (OLEs) at Higher Education Institutions (HEIs). Mining of LMS users’ data, involving artificial intelligence and incertitude modeling, e.g., via fuzzy logic, is a fundamental challenge. This study addresses the hypothesis that the structural characteristics of a Fuzzy Cognitive Map (FCM) can efficiently model the way LMS users interact with it, by estimating their Quality of Interaction (QoI) within a b-learning context. This study introduces the FCM-QoI model (combined with a model visualizer) consisting of 14 input-one output concepts, dependences and trends, considering one academic year of the LMS use from 75 professors and 1037 students at a HEI. The experimental findings have shown that the proposed FCM-QoI model can provide concepts interconnection and causal dependencies representation of Moodle LMS users’ QoI, revealing perspectives of its evolution both at a micro and macro analysis level. Moreover, the FCM-QoI model significantly adds to the evaluation and analysis of the QoI influential concepts’ contribution to self-sustained cycles (static analysis) and their alterations, when the time period of the LMS use is considered (dynamic analysis), showing potential to increase the flexibility and adaptivity of the QoI modeling and feedback approaches. Clearly, based on the FCM-QoI model, pedagogical instructors and decision-makers of HEIs could be assisted to holistically visualize, understand and assess OLEs stakeholders’ needs within the teaching and learning practices. [ABSTRACT FROM AUTHOR]

In this paper, a new automated Fuzzy Cognitive Maps (FCMs) learning algorithm is developed to generate FCMs from historical data. Automated FCM learning algorithms are used to model and analyze systems which are very complex and cannot be handled by experts' knowledge. The algorithm developed in this paper is based on the Imperialist Competitive Algorithm for global optimization and is called the Imperialist Competitive Learning Algorithm (ICLA). The ICLA divides the search space into several sections. It extracts the best knowledge from each section and follows a procedure to avoid local optima alongside rapid learning. Experiments have been conducted to compare the ICLA with other well-known FCM learning algorithms. The results show that in most cases, the ICLA performs better for learning FCMs in terms of solution accuracy and execution time. The testing results show clearly that the ICLA is a robust, fast and accurate FCM learning algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to web-based education that performs individualized instruction on the domain of programming languages is presented. This approach is fully implemented and evaluated in an educational application module, called fuzzy knowledge state definer (FuzKSD). In particular, FuzKSD performs user modeling by dynamically identifying and updating a student's knowledge level of all the concepts of the domain knowledge. The operation of FuzKSD is based on fuzzy cognitive maps (FCMs) that are used to represent the dependences among the domain concepts. FuzKSD uses fuzzy sets to represent a student's knowledge level as a subset of the domain knowledge. Thus, it combines fuzzy theory with the overlay model. Moreover, it employs a novel inference mechanism that dynamically updates user stereotypes using fuzzy sets. It should be noted that the overlay model and stereotypes constitute two widely used methods for user modeling. However, they have not been combined with fuzzy sets thus far in the literature. The gain from this novel combination is significant as a student level of knowledge is represented in a more realistic way by automatically modeling the learning or forgetting process of a student with respect to the FCMs and thus the system can provide individualized adaptive advice. The application of this approach is not limited to adaptive instruction. It can also be used in other systems with changeable user states, such as e-shops, where consumers’ preferences change over time and affect one another. Therefore, the particular module constitutes a novel generic fuzzy tool, which offers dynamic adaptation to users’ needs and preferences of adaptive systems. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Factors affecting the resilient level of a petrochemical plant are assessed. [•] It is achieved through a fuzzy cognitive map approach. [•] The results of FCM are combined with a set of questionnaire results. [•] The most important factors are awareness, preparedness and flexibility. [•] This is the first study for assessment of RE in a petrochemical plant by FCM. [Copyright &y& Elsevier]

Fuzzy cognitive maps (FCMs) form a class of graph-oriented fuzzy models describing causal relationships among concepts. In this study, we augment these models by introducing their generalization coming in the form of granular FCMs. In contrast with FCMs, in the granular FCMs, the connections between the nodes (states) are described in the form of information granules, especially intervals and fuzzy sets. Key scenarios in which granular models (and granular FCMs) arise are presented in order to offer a compelling rationale behind the formation of such models. In the context of system modeling, we show that information granularity emerges as an important design asset. We discuss detailed schemes of allocation of information granularity and quantify a performance of the resulting granular FCM in terms of a coverage criterion. For illustrative purposes, the detailed studies are completed for granular FCMs with interval-valued connections. [ABSTRACT FROM AUTHOR]

The prediction of time series has been widely applied to many fields such as enrollments, stocks, weather and so on. In this paper, a new prediction method based on fuzzy cognitive map with information granules is proposed, in which fuzzy c-means clustering algorithm is used to automatically abstract information granules and transform the original time series into granular time series, and subsequently fuzzy cognitive map is used to describe these granular time series and perform prediction, two benchmark time series are used to validate feasibility and effectiveness of proposed method. The experimental results show that the proposed prediction method can reach better prediction accuracy. Additionally, the proposed method is also able to precess the modeling and prediction of large-scale time series. [ABSTRACT FROM AUTHOR]

Road networks are instrumental in resource allocation and preevacuation, and profoundly affect disaster response and recovery, particularly emergent-disaster logistics and island rescues. Disruptions to road networks impair daily operations, irrespective of whether they are damaged by external forces or failures in interacting elements. However, functional interdependency is absent from transportation vulnerability assessments. This study thus constructed a framework to assess the interdependent vulnerability of road network failures. Based on eleven fragile factors developed in the literature and an empirical case study in the Taipei metropolitan area, road network vulnerability is determined by fuzzy cognitive maps and geographic information systems for functional and spatial interactions, respectively. The analytical results indicate that road network vulnerability is underestimated if the interdependency is neglected. Delay time on the shortest substitution, level of service on adjacent links, and inaccessibility to hospital emergency centers significantly affect vulnerability. Whereas certain socioeconomic resilience is performed in the short term, spatial-functional interdependency dilutes those effects in the long term. The framework developed facilitates decision makers in understanding interdependent vulnerabilities and adopting appropriate strategies to improve vulnerability. [ABSTRACT FROM AUTHOR]

Existing studies on the propagation of infectious diseases have not sufficiently considered the uncertainties that are related to individual behavior and its influence on individual decision making to prevent infections, even though it is well known that changes in behavior can lead to variations in the macrodynamics of the spread of infectious diseases. These influencing factors can be categorized into emotion-related and cognition-related components. We present a fuzzy cognitive map (FCM) denotative model to describe how the factors of individual emotions and cognition influence each other. We adjust the weight matrix of causal relationships between these factors by using a so-called nonlinear Hebbian learning method. Based on this FCM model, we can implement individual decision rules against possible infections for disease propagation studies. We take the simulation of influenza A [H1N1] spreading on a campus as an example. We find that individual decision making against infections (frequent washing, respirator usage, and crowd contact avoidance) can significantly decrease the at-peak number of infected patients, even when common policies, such as isolation and vaccination, are not deployed. [ABSTRACT FROM PUBLISHER]

Highlights: [•] This study proposed an indicator system for local governments. [•] FCMs were used to construct causal relationships of the key indicators. [•] Expanding MRT lines significantly improves the transport sustainability. [•] The focuses of future strategy scenarios were suggested. [ABSTRACT FROM AUTHOR]

This survey makes a review of the most recent applications and trends on fuzzy cognitive maps (FCMs) over the past decade. FCMs are inference networks, using cyclic digraphs, for knowledge representation and reasoning. Over the past decade, FCMs have gained considerable research interest and are widely used to analyze causal complex systems, which have originated from the combination of fuzzy logic and neural networks. FCMs have been applied in diverse application domains, such as computer science, engineering, environmental sciences, behavioral sciences, medicine, business, information systems, and information technology. Their dynamic characteristics and learning capabilities make them essential for a number of tasks such as modeling, analysis, decision making, forecast, etc. Overall, this paper summarizes the current state of knowledge of the topic of FCMs. It creates an understanding of the topic for the reader by discussing the findings presented in recent research papers. A survey on FCM studies concentrated on FCM applications on diverse scientific areas, where the FCMs emerged with a high degree of applicability, has also been done during the past ten years. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive maps (FCMs) are convenient and widely used architectures for modeling dynamic systems, which are characterized by a great deal of flexibility and adaptability. Several recent works in this area concern strategies for the development of FCMs. Although a few fully automated algorithms to learn these models from data have been introduced, the resulting FCMs are structurally considerably different than those developed by human experts. In particular, maps that were learned from data are much denser (with the density over 90% versus about 40% density of maps developed by humans). The sparseness of the maps is associated with their interpretability: the smaller the number of connections is, the higher is the transparency of the map. To this end, a novel learning approach, sparse real-coded genetic algorithms (SRCGAs), to learn FCMs is proposed. The method utilizes a density parameter to guide the learning toward a formation of maps of a certain predefined density. Comparative tests carried out for both synthetic and real-world data demonstrate that, given a suitable density estimate, the SRCGA method significantly outperforms other state-of-the-art learning methods. When the density estimate is unknown, the new method can be used in an automated fashion using a default value, and it is still able to produce models whose performance exceeds or is equal to the performance of the models generated by other methods. [ABSTRACT FROM AUTHOR]

This study presents a survey on the most recent learning approaches and algorithms that are related to fuzzy cognitive maps (FCMs). FCMs are cognition fuzzy influence graphs, which are based on fuzzy logic and neural network aspects that inherit their main advantages. They gained momentum due to their dynamic characteristics and learning capabilities. These capabilities make them essential for modeling and decision-making tasks as they improve the performance of these tasks. An efficient number of learning algorithms for FCMs, by modifying the FCM weight matrix, have been developed in order to update the initial knowledge of human experts and/or include any knowledge from historical data in order to produce learned weights. The proposed learning techniques have mainly been concentrated on three directions: on the production of weight matrices on the basis of historical data, on adaptation of the cause–effect relationships of the FCM on the basis of experts’ intervention, and on the production of weight matrices by combining experts’ knowledge and data. The learning techniques could be categorized into three groups on the basis of the learning paradigm: Hebbian-based, population-based, and hybrid, which subsequently combine the main aspects of Hebbian-based- and population-based-type learning algorithms. These types of learning algorithms are the most efficient and widely used to train the FCMs, according to the existing literature. A survey on recent advances on learning methodologies and algorithms for FCMs that present their dynamic capabilities and application characteristics in diverse scientific fields is established here. [ABSTRACT FROM PUBLISHER]

In this paper, a new evolutionary-based fuzzy cognitive map (FCM) methodology is proposed to cope with the forecasting of the patient states in the case of pulmonary infections. The goal of the research was to improve the efficiency of the prediction. This was succeeded with a new data fuzzification procedure for observables and optimization of gain of transformation function using the evolutionary learning for the construction of FCM model. The approach proposed in this paper was validated using real patient data from internal care unit. The results emerged had less prediction errors for the examined data records than those produced by the conventional genetic-based algorithmic approaches. [ABSTRACT FROM PUBLISHER]

The theory of fuzzy cognitive maps (FCMs) is a powerful approach to modeling human knowledge that is based on causal reasoning. Taking advantage of fuzzy logic and cognitive map theories, FCMs enable system designers to model complex frameworks by defining degrees of causality between causal objects. They can be used to model and represent the behavior of simple and complex systems by capturing and emulating the human being to describe and present systems in terms of tolerance, imprecision, and granulation of information. However, FCMs lack the temporal concept that is crucial in many real-world applications, and they do not offer formal mechanisms to verify the behavior of systems being represented, which limit conventional FCMs in knowledge representation. In this paper, we present an extension to FCMs by exploiting a theory from formal languages, namely, the timed automata, which bridges the aforementioned inadequacies. Indeed, the theory of timed automata enables FCMs to effectively deal with a double-layered temporal granularity, extending the standard idea of B-time that characterizes the iterative nature of a cognitive inference engine and offering model checking techniques to test the cognitive and dynamic comportment of the framework being designed. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive maps (FCMs), as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the dynamic behavior of the investigated systems. However, due to defects in expression and architecture, the traditional FCMs and most of their relevant extensions are not applicable to classification problems. To solve this problem, this paper presents an approach that directly extends the model by translating the reasoning mechanism of traditional FCMs to a set of fuzzy if– then rules. Moreover, the proposed approach fully considers the contribution of the inputs to the activation of the fuzzy rules and quantifies the causalities using mutual subsethood, which works in conjunction with volume defuzzification in a gradient descent-learning framework. In this manner, our approach enhances the capability of the conventional FCMs to automatically identify membership functions and quantify causalities. Despite the increase in the number of tunable parameters, experimental results show that the proposed approach efficiently extends the application of the traditional FCMs into classification problems, while keeping the ability for prediction and approximation. [ABSTRACT FROM AUTHOR]

The fuzzy cognitive map (FCM) has gradually emerged as a powerful paradigm for knowledge representation and a simulation mechanism that is applicable to numerous research and application fields. However, since efficient methods to determine the states of the investigated system and to quantify causalities that are the very foundations of FCM theory are lacking, constructing FCMs for complex causal systems greatly depends on expert knowledge. The manually developed models have a substantial shortcoming due to the model subjectivity and difficulties with assessing its reliability. In this paper, we proposed a fuzzy neural network to enhance the learning ability of FCMs. Our approach incorporates the inference mechanism of conventional FCMs with the determination of membership functions, as well as the quantification of causalities. In this manner, FCM models of the investigated systems can automatically be constructed from data and, therefore, operate with less human intervention. In the employed fuzzy neural network, the concept of mutual subsethood is used to describe the causalities, which provides more transparent interpretation for causalities in FCMs. The effectiveness of the proposed approach in handling the prediction of time series is demonstrated through many numerical simulations. [ABSTRACT FROM AUTHOR]