The theory and application of advanced computational intelligence and intelligent informatics has been one of the most active fields of research over the last decades. A significant number of methods have been proposed to effectively solve problems of clustering, image processing, optimization, bio-information understanding, and modeling and control. They have been applied to many areas, including complex industrial processes and economics. This paper presents a review of the articles and papers addressing advanced computational intelligence and intelligent informatics from the Journal of Advanced Computational Intelligence and Intelligent Informatics in the past two years.

When a non-luxury apparel brand attempts to raise its brand value by employing the luxury strategy, rarity management is a brand manager’s concern. This study focuses on apparel brand’s rarity as perceived by consumers and its influence on consumers’ brand attitudes depending on the extent of the brand’s luxuriousness. In this paper, a consumer survey is conducted in Japan to analyze consumers’ perceptions and attitudes toward 10 leading apparel brands. The results of principal factor analysis and regression analysis suggest that the higher a brand’s luxuriousness is, the more strongly its perceived rarity positively impacts on consumers’ brand attitudes. On the contrary, it is suggested that the lower a brand’s luxuriousness is, the more strongly its perceived rarity negatively impacts on consumers’ attitudes. This result implies the existence of the snob effect for luxury brands. Conversely, investments to raise consumers’ perceived rarity potentially might harm consumers’ attitudes towards the brand when the extent of brand’s luxuriousness is low.

Interval models based on fuzzy regression and fuzzy time-series can illustrate the possibilities of a system using the intervals in the model. Thus, the aim is to minimize the vagueness of the model in order to describe the possible states of the system. In the present study, we consider on an interval fuzzy time-series model based on a Box–Jenkins model, a fuzzy autocorrelation model proposed by Yabuuchi, and a fuzzy regressive model proposed by Ozawa. We examine two models by analyzing the Japanese national consumer price index and demonstrate that our approach improves the accuracy of predictions. The utility and predictive accuracy of fuzzy time-series models are validated using two concepts of fuzzy theory and statistics. Finally, we demonstrate the applicability of the fuzzy autocorrelation model with fuzzy confidence intervals.

With the assumption that the vertices have numerical values. The aim of this paper is to construct regression models to estimate the values from their relationship on the graph by defining the vertex and the numerical value as an independent variable and a dependent variable, respectively. Given the condition that near vertices have close values, k-Nearest Neighbor regression models (KNN) has been proposed. However, the condition is not satisfied when some near vertices have different values. To overcome such difficulty, c-regression which classify data points int o some clusters has been proposed to improve performance of regression analysis. We moreover propose new c-regression models on a graph with fuzzy numbers on vertices and show some numerical examples.

This paper proposes an objective approach to the construction of an appropriate membership function that extends to our previous studies. It is important to set a membership function with subjectivity and objectivity to obtain a reasonable optimal solution that complies with the decision maker’s feelings in real-world decision making. To ensure objectivity and subjectivity of the obtained membership function, an entropy-based approach based on mathematical programming is integrated into the interval estimation considered by the decision maker. Fuzzy Harvda-Charvat entropy, which is a natural extension of fuzzy Shannon entropy, is introduced as general entropy with fuzziness. The main steps of our proposed approach are to set intervals with membership values 0 and 1 to enable a decision maker to judge confidently, and to solve the proposed mathematical programming problem strictly using nonlinear programming. In this paper, the given membership function is assumed to be a piecewise linear membership function as an approximation of nonlinear functions, and each intermediate value of partial linear function is optimally obtained.

This paper aims to establish a framework for evaluating technological innovation and to emphasize the important influence of path dependence on technological innovation. The fuzzy cognitive map (FCM) method is used to identify causal relationships among factors that influence technological innovation, and a FCM structural diagram for evaluating enterprise technological innovation is described. Meanwhile, a fuzzy feedback system for the evaluation of technological innovation, integrated with a nonlinear Hebbian learning algorithm, is established; dependence on expert opinions may be avoided through learning and practice using the cognitive map. Finally, using a computer software platform, a dynamic simulation of any complex index system can be realized. From this simulation, stable conditions can provide path references by which an enterprise engaging in technological innovation can improve the integrative efficiency and the overall effect of any realistic technological innovation activity.

Diffusion kurtosis imaging is a newly developed diffusion magnetic resonance imaging technique, which is becoming increasingly valuable in clinical practice. Although low-resolution sampling is commonly used to compensate the unsteadiness of kurtosis estimation, the influence of the sampling shape has not been investigated. In this study, by using two different acquisition protocols, isotropic and anisotropic sampling voxels were acquired and their influence on various white matter structures was observed. Fiber tracking, T-tests, and correlation analysis were used to quantify the difference between the anisotropic and isotropic sampling. A significant difference (p<0.01) was found in the fractional anisotropic level but not in kurtosis. The results presented here can provide a basis for higher resolution as well as higher quality kurtosis mapping, which may be of great significance in clinical examinations.

The present study proposes two types of power-regularized fuzzy c-means (pFCM) clustering algorithms with a fuzzification parameter less than one, which supplements previous work on pFCM with a fuzzification parameter greater than one. Both the proposed methods are essentially identical to each other, but not when fuzzification parameter values are specified. Theoretical discussion reveals the property of the proposed methods, and some numerical results substantiate the property of the proposed methods and show that the proposed methods outperform two conventional methods from an accuracy point of view.

The fuzzy non-metric model (FNM) is a representative non-hierarchical clustering method, which is very useful because the belongingness or the membership degree of each datum to each cluster can be calculated directly from the dissimilarities between data and the cluster centers are not used. However, the original FNM cannot handle data with uncertainty. In this study, we refer to the data with uncertainty as “uncertain data,” e.g., incomplete data or data that have errors. Previously, a methods was proposed based on the concept of a tolerance vector for handling uncertain data and some clustering methods were constructed according to this concept, e.g. fuzzy c-means for data with tolerance. These methods can handle uncertain data in the framework of optimization. Thus, in the present study, we apply the concept to FNM. First, we propose a new clustering algorithm based on FNM using the concept of tolerance, which we refer to as the fuzzy non-metric model for data with tolerance. Second, we show that the proposed algorithm can handle incomplete data sets. Third, we verify the effectiveness of the proposed algorithm based on comparisons with conventional methods for incomplete data sets in some numerical examples.

Rough set approaches provide useful tools to induce minimal decision rules from given data. Acquired minimal rules are typically used to build a classifier. However, minimal rules are sometimes used for design knowledge. Specifically, if a new object is designed to satisfy the condition of a minimal rule, it can be classified into a class suggested by the rule. Although we are interested in the goodness of the set of obtained minimal decision rules for the purpose of building a classifier, we are more interested in the goodness of an individual minimal decision rule for design knowledge. In this study, we propose robustness measures as a new type of evaluation index for decision rules. The measure evaluates the extent to which interestingness is preserved after the some conditions are removed. Four numerical experiments are conducted to examine the usefulness of robusetness measures. Decision rules selected by robustness scores are compared with those selected by recall, which is the well-known measure to select good rules. Our results reveal that a different aspect of the goodness of a rule is evaluated by the robustness measure and thus, the robustness measure acts as an independent and complementary index of recall.

The conditional autoregressive Value-at-Risk (CAViaR) model, as a conditional autoregressive specification for calculating the Value-at-Risk (VaR) of the security market, has been receiving more and more attentions in recent years. As asymmetry may have a significant influence on the markets and the returns may have an autoregressive mean, this study proposes some extended CAViaR models, including asymmetric indirect threshold autoregressive conditional heteroskedasticity (TARCH) model and indirect generalized autoregressive conditional heteroskedasticity (GARCH) model with an autoregressive mean. We also present two types of CAViaR-Volatility models by adding the volatility term as an exogenous explanatory variable. Our empirical results indicate that extended models perform more effectively on out-of-sample predictions, as both forecasting effect and model stability have been improved. In addition, we find that the forecasting effect is better at the lower quantile (1%) than at the higher quantile (5%); a possible explanation is that extreme market information has more impact on VaR. In addition, there is negative correlation between volatility and VaR; VaR decreases as volatility increases.

In recent 30 years, countries from the world have attached great attention to the influence on inflation posed by asset price. Real estate market is a very important economic market for any country. Therefore, housing price has become a hot topic for discussion and research in China. Whether housing price will affect a country’s inflation or not and to what extent the effect will be are social focuses. Hence, it is feasible to theoretically apply Tobin’s Q Theory in this paper, combine the real estate market price with the assets replacement and study the inflationary impact posed by the housing price, through the application of the wealth effect theory. By using monthly statistics of China’s real estate market and inflation from the year 2005 to 2014, this paper will conduct a theoretical and empirical research on the influence that housing price has on inflation with the adoption of dynamic analysis methods including Granger Causality Test, impulse response and variance decomposition. Furthermore, this paper is featured with systematic and complete empirical thinking and methodology, comprehensive data selection and distinctive research results associated with the relationship between housing prices and inflations. According to the study result, housing price is the Granger Cause of inflation and will not drive inflation in short time. But as time passes, this effect will be gradually enhanced. This paper suggests that housing price and other price factors should be taken into consideration so as to establish a broad-sense inflation index in China.

The luxury goods market has been expanding worldwide since the early 1990s. In the marketplace, there are new entrants (emerging luxury brands) reputed to be leading luxury brands, especially designer brands, which neither provide new luxury like Coach, nor are similar to traditional luxury brands, such as Louis Vuitton. In this research, an emerging luxury brand also refers to a brand that provides luxury goods in or above the intermediate level of the luxury goods products hierarchy and was established in or after the 1970s. The purpose of this paper is to propose a conceptual framework for emerging luxury brand construction. It (1) defines luxury and emerging luxury brand, (2) reviews the theoretical basis of luxury goods and the brand strategy of luxury goods, (3) frames the nexus between luxury brand attributes and brand image, (4) conducts consumer survey and data mining, and (5) discusses and concludes the research. This research includes qualitative research (a semi-structured interview) and quantitative research (exploratory factor analysis and regression analysis). The results show that the location and atmosphere of luxury stores, E-commerce, online ads and newsletters, origin, iconic products, symbols, and PR events have positive effects on consumers’ impression of emerging luxury brands.

The use of intelligent wheelchair and rehabilitation robots has increased rapidly in the recent years owing to a growing number of patients experiencing paralysis, quadriplegia, amputation, and geriatric conditions. In this paper, the design and development of a powerful voice control system is proposed. It includes three modes of operational voice-recognition algorithms. Two sophisticated voice-recognition modules are used to achieve this goal. The system supports speaker dependent (SD) and speaker independent (SI) voice processing. Two voice-recognition algorithms are used, dynamic time warping (DTW) and Hidden Markov Model (HMM), to ensure the maximum voice-recognition accuracy and reduce voice-recognition errors. The system is validated in different noise environments to verify the performance of the system with low and high noise and to evaluate the feasibility of using the system in these environments. Three popular languages (English, German, and Chinese) were used by the system to verify performance with different pronunciations.

The series of subsidy policies launched by the Chinese government has affected supply chain members’ profits distribution. To explore this influence, an agent-based model was designed, and experiments were conducted under different subsidy levels. Our model focused on the ordinary business entities and their activities in the supply chain. By investigating the real world and other researchers’ studies, agent simulation class library (e.g., control agents, cooperation/collaboration agents, and fractal simulation agents) and their decision knowledge bases were designed to simulate the supply chain members’ behaviors, decision processes, and operation and production activities and behaviors. Price model, demand model and profit model under the subsidy were built to evaluate the supply chain members’ profits under different subsidy scenarios. Finally, a multi-echelon appliance supply chain model was constructed, and experiments were performed with different levels of subsidy limit. Results showed that the supply chain members’ profits increased under the government subsidy policy. The agent-based modeling and simulation method provides a novel approach to explore the impact on profit distribution.

This paper deals with a discrete-time uncertain linear quadratic (LQ) optimal control, where the control weight costs are indefinite . Based on Bellman’s principle of optimality, the recurrence equation of the uncertain LQ optimal control is proposed. Then, by using the recurrence equation, a necessary condition of the optimal state feedback control for the LQ problem is obtained. Moreover, a sufficient condition of well-posedness for the LQ problem is presented. Furthermore, an algorithm to compute the optimal control and optimal value is provided. Finally, a numerical example to illustrate that the LQ problem is still well-posedness with indefinite control weight costs.

The notion of lifespan of an electronic device (or system) is formulated in terms of a reinterpretation of the concept of electronic systems reliability (ESR) so that the impact of ‘physical’ geographic environmental stresses, notably: psychrometry and aridity, which are known to vary from one location to another could be effectively accounted for. The proposed formulation is based on a conscientious analysis of climatic data and its relationship with the longevity of electronic devices. To validate our proposal, we employed a veridical approach, wherein we compared the failure rate of a widely used electronic biomedical electrocardiogram (ECG) device based on standard environmental ‘conditions’ and reference values and then computed the lifespan of the same device based on our proposed configuration using the average climatic conditions prevalent in five countries that are geographically spread across the length of the Earth. Our proposed approach estimates a lifespan of only 2 years when the device is used in the Kingdom of Saudi Arabia (KSA) as opposed to an average lifespan of 40 years when the same device is deployed for use under average environmental conditions prevalent in (the capital cities of) China, Japan, the USA, and Britain. Results from both aridity-based and psychrometry-based interpretations of ESR suggest that the ECG device has a lower lifespan when used in harsher arid environments which also infers a greater influence of physical geographic proximity on the smooth, reliable, and prolonged operation of electronic devices.

A new surveillance video enhancement method is proposed to improve the visual effect of videos captured in low-light conditions. The proposed technique, called temporal-spatial (TS) filtering, uses adaptive temporal filtering and nonlocal mean filtering to smooth the transmission map in the temporal and spatial dimensions and thus yields restored video sequences with significantly reduced noise, improved details and good spatial and temporal coherence. The main advantage of this work is that the performance of contrast enhancement, noise reduction and temporal-spatial coherence can be significantly improved using the proposed framework, which adopts a strategy that applies the same transmission map to a series of video frames. Comparative study and quantitative evaluation demonstrate that the proposed method is better than previous techniques in terms of reducing noise and improving contrast.