•Analyzing the impacts of Blockchain technology’s integration in the supply chain.•The complexity of the system is evaluated by different cognitive map methods.•Different scenarios are formed for ...evaluation of the impact of Blockchain technology.•Observing the improved supply chain performance after Blockchain technology.•Increasing expected risks in the system is evaluated.
Blockchain technology have gained importance in the supply chain with its transparency, robustness, and elimination of intermediaries. Different impacts are expected with the integration of blockchain technology in supply chain processes. Comprehensive evaluation methods are required to take important strategic decisions about blockchain technology. To examine the impact of blockchain technology, factors of cost, risks, business, and customer related benefits should be considered comprehensively. The purpose of this paper is to investigate the causal relationships among the factors to evaluate blockchain technology impact on supply chain. Cognitive maps (CM) methods which are hesitant fuzzy cognitive map (HFCM), probabilistic linguistic fuzzy cognitive map (PL-FCM) and rough set cognitive map (RS-CM) are used in this study. Also, PL-FCM that is generated based on probabilistic linguistic term sets is used for the first time in this study as a novel cognitive model. This study provides a contribution by developing cognitive map methods to measure and examine the impact of blockchain technology on supply chain for the first time. Firstly, the impacts of blockchain technology on supply chain and their relations are formed according to expert views and literature review. Then, five scenarios are considered using cognitive mapping methods, future predictions in terms of supply chain management are determined. Sensitivity analysis is performed. In this way, firms can analyze various implications and under what conditions how blockchain technology will have an impact on the supply chain.
Fuzzy cognitive maps (FCMs) have demonstrated considerable success in time series forecasting and are adept at handling uncertainties and capturing the dynamics of complex systems. Nevertheless, ...challenges still remain in the handling of multivariate high-dimensional time series using a time-effective learning algorithm. This article introduces multiple-input multiple-output randomized high-order FCM (MRHFCM), a new methodology for predicting high-dimensional time series in multiple-input-multiple-output systems. MRHFCM represents a hybrid method that combines data embedding transformation, randomized high-order FCM (R-HFCM), and an echo state network. The core of MRHFCM involves a cascade of R-HFCMs termed the CR-HFCM model. Each CR-HFCM comprises three layers: 1) the input layer, 2) reservoir (internal layer), and 3) output layer. Notably, only the output layer is trainable, employing the least squares minimization algorithm. The weights within each subreservoir are randomly chosen and remain unchanged throughout the training procedure. Three real-world high-dimensional datasets are utilized to assess the performance of the proposed MRHFCM method. The results obtained reveal that our approach outperforms some existing baseline and state-of-the-art machine learning and deep learning forecasting techniques in terms of both accuracy and parsimony.
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.
A general framework for intelligent recommender systems Aguilar, Jose; Valdiviezo-Díaz, Priscila; Riofrio, Guido
Applied computing & informatics,
July 2017, 2017-07-00, 2017-07-01, Letnik:
13, Številka:
2
Journal Article
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In this paper, we propose a general framework for an intelligent recommender system that extends the concept of a knowledge-based recommender system. The intelligent recommender system exploits ...knowledge, learns, discovers new information, infers preferences and criticisms, among other things. For that, the framework of an intelligent recommender system is defined by the following components: knowledge representation paradigm, learning methods, and reasoning mechanisms. Additionally, it has five knowledge models about the different aspects that we can consider during a recommendation: users, items, domain, context and criticisms. The mix of the components exploits the knowledge, updates it and infers, among other things. In this work, we implement one intelligent recommender system based on this framework, using Fuzzy Cognitive Maps (FCMs). Next, we test the performance of the intelligent recommender system with specialized criteria linked to the utilization of the knowledge in order to test the versatility and performance of the framework.
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.
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.
Various automatic learning algorithms have been proposed to learn fuzzy cognitive maps (FCMs), but most of them were only applied to learn small-scale FCMs and the learned maps obtained by such ...methods are usually much denser than the real maps. Learning FCMs requires the learning methods to not only determine the existence of links between concepts but also optimize the edge weights, which is the difficulty for FCM learning methods. Therefore, we propose a mutual information (MI)-based two-phase memetic algorithm (MA) for learning large-scale FCMs, termed as MIMA-FCM. In MIMA-FCM, the first phase is oriented to determine the existence of links between concepts by MI, which can reduce the search space significantly for MA, and then MA is used to optimize the edge weights according to the multiple observed response sequences in the second phase. Experiments on both synthetic and real-life data and the application for the gene regulatory network reconstruction problem demonstrate that the proposed method can not only find the plausible existence of links between concepts, but also optimize the edge weights rapidly. The comparison with existing algorithms shows that MIMA-FCM can learn large-scale FCMs with higher accuracy without expert knowledge.
This paper presents a method to analyze the traffic flow pattern at a crowded junction in Chennai, one of the metropolitan cities in India, using waiting time in the signal in different time ...intervals with the help of Fuzzy Cognitive Map and Induced Fuzzy Cognitive Map.
Fuzzy cognitive maps (FCMs) keep growing in popularity within the scientific community. However, despite substantial advances in the theory and applications of FCMs, there is a lack of an up-to-date, ...comprehensive presentation of the state-of-the-art in this domain. In this review study we are filling that gap. First, we present basic FCM concepts and analyze their static and dynamic properties, and next we elaborate on existing algorithms used for learning the FCM structure. Second, we provide a goal-driven overview of numerous theoretical developments recently reported in this area. Moreover, we consider the application of FCMs to time series forecasting and classification. Finally, in order to support the readers in their own research, we provide an overview of the existing software tools enabling the implementation of both existing FCM schemes as well as prospective theoretical and/or practical contributions.
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.
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