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Abstract: The underlying objective of this study is to show how fuzzy sets (and information granules in general) and grammatical inference play an interdependent role in information granularization and knowledge-based problem characterization. The bottom-up organization of the material starts with a concept and selected techniques of data compactification which involves information granulation and gives rise to higher-order constructs (type-2 fuzzy sets). The detailed algorithmic investigations are provided. In the sequel, we focus on Computing with Words (CW), which in this context is treated as a general paradigm of processing information granules. We elaborate on a role of randomization and offer a detailed example illustrating the essence of the granular constructs along with the grammatical aspects of the processing. [Copyright &y& Elsevier]

This paper investigates decision making with normalized probabilistic linguistic preference relations (NPLPRs). Consistency analysis is indispensable for deriving the reasonable ranking. After recalling previous research, we find that all previous concepts cannot fully define consistent NPLPRs. As a fundamental topic of decision making with preference relations, it is necessary to further study the consistency of NPLPRs. For this purpose, an interactive algorithm for deriving disjunctive probabilistic additive linguistic preference relations (DP-ALPRs) is provided, by which an additive consistency concept for NPLPRs is defined. When NPLPRs are unacceptably consistent, models for obtaining acceptably additively consistent NPLPRs are built. Considering the situation where only incomplete NPLPRs are obtained, a disjunctive probability and additive consistency based interactive algorithm for ascertaining missing judgments is provided. Meanwhile, we discuss group decision making (GDM) with NPLPRs and offer a distance measure based formula to determine the weights of the decision makers. In addition, the method defines a consensus index and builds models for improving the consensus level. Under the additive consistency and consensus discussions, an interactive algorithm for GDM with NPLPRs is proposed. Finally, the new method is applied to select green raw material suppliers to illustrate the application and compared with several previous ones. [ABSTRACT FROM AUTHOR]

In this paper, a new approach to estimate scaling factors of the fuzzy PID controller is presented. The performance of the fuzzy PID controller is sensitive to the variety of scaling factors. The design procedure dwells on the use of evolutionary computing (more specifically, a genetic algorithm) and estimation algorithm. The tuning of the scaling factors of the fuzzy PID controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy PID controller by means of three types of estimation algorithms such as HCM (Hard C-Means) clustering-based regression polynomial, neuro-fuzzy networks, and regression polynomials. Numerical studies are presented in detail along with a detailed comparative analysis. [Copyright &y& Elsevier]

In this paper, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The development of the ‘conventional’ FPNNs uses an extended Group Method of Data Handling. The network exploits a fixed fuzzy inference type in each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. Here, the proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least-square method-based learning. The performance of the networks is quantified through experimentation involving two time series dataset already used in fuzzy modeling. The results demonstrate their superiority over the existing fuzzy and neural models. [Copyright &y& Elsevier]

In today's world of connectivity, various domains use different multi-sensor Internet of Things (IoT) and Intelligent Systems (IS) applications. These applications involve extensive interactions between thousands or millions of components in open environments, which challenges verifying their reliability and efficiency. This paper introduces the first investigation in verifying IoT applications and intelligent systems in multi-source data settings with multi-agent commitment protocols in uncertain or inconsistent environments. Specifically, we present original and efficient solutions for modeling and verifying these systems with conditional and unconditional commitment protocols under uncertain or inconsistent settings. We introduce 4v-CTL c and 4v-CTL c c , multi-valued logics of commitment for reasoning about inconsistency over these systems that expand 3v-CTL c and 3v-CTL c c for reasoning about uncertainty. Moreover, we introduce new reduction algorithms for reducing our multi-valued model checking problems to the two-valued ones. To implement these algorithms, we develop two new automatic verification tools. The first tool translates the multi-valued logics to CTL and automatically interacts with the NuSMV model checker. The second tool translates these logics to the two-valued versions, CTL c and CTL c c , and automatically interacts with the MCMAS+ model checker. We apply our verification approaches to a Smart Home, a Smart Hospital and a Smart Mortgage system with multi-source data as case studies using sets of properties, including safety, liveness and reachability. The experimental results obtained by the proposed multi-valued model checking techniques proved these techniques' high efficiency and applicability to modeling and verifying intelligent and autonomous multi-source data systems. • New multi-valued logics to reason about inconsistency in multi-source data settings. • Modeling IoT and IS applications using different multi-valued commitment logics. • New reduction-based model checking algorithms for multi-source data applications. • Implementing two new model checking tools: MCMAS-interactor and NuSMV-interactor. [ABSTRACT FROM AUTHOR]

Abstract: In this study, polynomial-based radial basis function neural networks are proposed as one of the functional components of the overall face recognition system. The system consists of the preprocessing and recognition module. The design methodology and resulting procedure of the proposed P-RBF NNs are presented. The structure helps construct a solution to high-dimensional pattern recognition problems. In data preprocessing part, principal component analysis (PCA) is generally used in face recognition. It is useful in reducing the dimensionality of the feature space. However, because it is concerned with the overall face image, it cannot guarantee the same classification rate when changing viewpoints. To compensate for these limitations, linear discriminant analysis (LDA) is used to enhance the separation between different classes. In this paper, we elaborate on the PCA-LDA algorithm and design an optimal P-RBF NNs for the recognition module. The proposed P-RBF NNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part realized in terms of fuzzy “if–then” rules. In the condition part of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. In the conclusion part of rules, the connection weight is realized through three types of polynomials such as constant, linear, and quadratic. The coefficients of the P-RBF NNs model are obtained by fuzzy inference method forming the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum, fuzzification coefficient, and the feature selection mechanism) of the networks are optimized by means of differential evolution (DE). The experimental results completed on benchmark face datasets – the AT&T, and Yale datasets demonstrate the effectiveness and efficiency of PCA-LDA combined algorithm compared with other algorithms such as PCA, LPP, 2D-PCA and 2D-LPP. A real time face recognition system realized in this way is also presented. [Copyright &y& Elsevier]

• Take morphology and shape features into account for granularity representation. • Using interval information granules to achieve system modelling. • A granular model is proposed and used in the field of anomaly detection. • Evaluation indexes are established to quantify the performance. • Greatly reduce the data volume and improve the detection performance. Since time series are characterized by a substantial volume of data, high levels of noise and the correlation between data in the time series attributes, it becomes challenging to mine crucial information from the series and apply it to anomaly detection. In this study, inspired by the concept of information granularity being applied to the process of system modelling, a granular Markov model is proposed for time series anomaly detection. Anomalies are generally caused by the changes in amplitude and shape; in this study we take both the original time series data and their amplitude change data into consideration. First, we utilize an interval information granularity representation based on the principle of justifiable granularity to represent the original time series data in an abstract manner to arrive at the corresponding representation results-- that is, interval information granules. Then, based on the results of the interval information granularity representation and the Fuzzy C-Means (FCM) clustering algorithm, a granular Markov model is developed to produce anomaly scores to quantify possible anomalies. Compared with state-of-the-art methods, experimental studies completed for a large number of datasets demonstrate that the proposed method can significantly improve the anomaly detection process with higher data anomaly resolution. The obtained results are consistent across all datasets. [ABSTRACT FROM AUTHOR]

• Apply dynamic utility calculation framework to mine closed high utility itemsets. • Dramatically reduce the cost of database scans by adopting the P-set structure. • Propose a new CHUIM algorithm, named iEFIM-Closed. • Conduct experiments to measure the performance of the proposed algorithm. The problem of discovering high-utility itemsets (HUIs) in transaction databases, which is an extension of Frequent Itemset Mining, is a commonly encountered mining task. Researchers have proposed algorithms to efficiently mine highly profitable itemsets in customer transaction databases, in which the unit profits of items are fixed. However, this assumption does not reflect the true nature of the utility measure of items in real-life transaction databases, which might vary over time. Moreover, since this important characteristic is ignored by all the current HUI mining algorithms, they are either not applicable to this type of database, or they generate inaccurate results. In addition, the HUI mining algorithms' traditional limitation is that they produce a huge number of HUIs for users. In this paper, we define the problem of mining a lossless, concise and compact representation of HUIs, called closed HUIs (CHUIs), in dynamic unit profit databases. Based on newly defined of utility measure, a novel algorithm, called iEFIM-Closed, is introduced. This relies on this new utility measure, a novel compact database format to reduce the cost of database scans and increase the efficiency of the mining process. Experimental evaluations show that iEFIM-Closed significantly outperforms state-of-the-art algorithms for mining CHUIs on sparse databases with dynamic profit, and has competitive performance in dense databases in terms of runtime, the cost of database scans and scalability. [ABSTRACT FROM AUTHOR]

• Our method for mining contrast sets (CSs) is 10–77 times faster than the baselines. • Our method eliminates up to thousands of redundant CSs. • Our method achieves 1–16% improvements in classification accuracy over the baselines. • Our method is an efficient way to analyze the length of stay in an emergency department. Many applications of intelligent systems involve understanding a group of contrastively different outcome (e.g., all survivors of a deadly cancer, a top performing team in a large corporation). The intelligent system needs to identify attributes (features) which best describe or explain the group versus its alternatives. In data mining, this problem is studied under the framework of contrast set mining (CSM). Although CSM is not new, the era of big data has produced new computational and statistical challenges. In particular, existing algorithms fail (1) to perform efficiently in terms of runtime on large-scale datasets and (2) to accommodate simultaneous inference on an overwhelming array of features which are often repetitive and collinear. In this paper, we develop a CSM algorithm which addresses both challenges. The computational challenge is addressed with a tree structure and two theorems while the statistical challenge is addressed with the application of false discovery rate for multiple testing. The computational and statistical advantages of the proposed algorithm over three state-of-the-art algorithms are demonstrated with comprehensive experiments. In addition, we also show the effectiveness of our proposed method in an intelligence-system application involving hospital process redesign. The proposed method not only improves the performance of machine learning systems, but also generates succinct and insightful patterns directly relevant to clinical decision-making. [ABSTRACT FROM AUTHOR]