Recent Developments and the New Direction in Soft-Computing Foundations and Applications

Data becomes overwhelming present in almost all aspects of manufacturing, finance, commerce and entertainment. Today’s world seems to generate tons of data related to all aspect of human activities every minute. A lot of hope and expectations are linked to benefits that analysis of such data could bring. Among many sources of data, social networks start to play a very important role. Indications what individuals think about almost anything related to their lives, what they like and dislike are embedded in posts and notes they leave on the social media platforms. Therefore, discovering the users’ opinions and needs is very critical for industries as well as governments. Analysis of such data—recognized as a big data due to its tremendous size—is of critical importance. The theory of fuzzy sets and systems, introduced in 1965, provides the researchers with techniques that are able to cope with imprecise information expressed linguistically. This theory constitutes a basis for designing and developing methodologies of processing data that are able to identify and understand views and judgments expressed in a unique, human way—the core of information generated by the users of social networks. The paper tries to recognize a few important example of extracting value from social network data. Attention is put on application of fuzzy set and systems based methodologies in processing such data.

This work describes the idea of an adaptive semantic layer for large-scale databases, allowing to effectively handling a large amount of information. This effect is reached by providing an opportunity to search information on the basis of generalized concepts, or in other words, linguistic descriptions. These concepts are formulated by the user in natural language, and modelled by fuzzy sets, defined on the universum of the significances of the attributes of the database. After adjustment of user’s concepts based on search results, we have “personalized semantics” for all terms which particular person uses for communications with database (for example, “young person” will be different for teenager and for old person; “good restaurant” will be different for people with different income, age, etc.).

Modern industries and business firms are widely using data mining applications in which the problem of Frequent Itemset Mining (FIM) has a major role. FIM problem can be solved by standard traditional algorithms like Apriori in certain transactional database and can also be solved by different exact (UApriori, UFP Growth) and approximate (Poisson Distribution based UApriori, Normal Distribution based UApriori) probabilistic frequent itemset mining algorithm in uncertain transactional database (database in which each item has its existential probability). In our algorithm it is considered that database is distributed among different locations of globe in which one location has certain transactional database, we call this location as main site and all other locations have uncertain transactional databases, we call these locations as remote sites. To the best of our knowledge no algorithm is developed yet which can calculate frequent itemsets on the combination of certain and uncertain transactional database. We introduced a novel approach for finding itemsets which are globally frequent among the combination of all uncertain transactional databases on remote site with certain database at main site.

The presence of missing value in a dataset can affect the performance of an analysis system such as classifier. To solve this problem many methods have been proposed in different studies using different theorems, analysis systems and methods such as Neural Network (NN), k-Nearest Neighbor (k-NN), closest fit etc. In this paper, we propose novel method based on RST for solving the problem of missing value that was lost (e.g., was erased). After dataset filling with proposed method, it has been observed improvement the performance of used analysis systems.

We advance our work on a special text categorization problem, the multiaspect text categorization, introduced in our previous works. In general case, it assumes a hierarchy of categories, and documents are assigned to leaves of a category but within categories documents are further structured into sequences of documents, referred to as cases. This is much more complex than the classic text categorization. Previously, we proposed a number of approaches to deal the above problem but we took into account to a limited extent hierarchies occurring in the definition of the problem. Here, we we start with one of our best approaches proposed so far and extend it by assuming that categories are arranged into a hierarchy, and that there is a hierarchical relation between a category and its offspring cases.

In this work, we applied Adaptive Neuro-Fuzzy Inference System to three different classification problems: (1) sentence-level subjectivity detection, (2) sentiment analysis of texts, and (3) detecting user intention in natural language call routing system. We used English dataset for the first and second problems, but Azerbaijani dataset for the third problem based on same features. Our feature extraction algorithm calculates a feature vector based on the statistical occurrences of words in a corpus without any lexical knowledge.

The game approaches are rather popular in many applications, where a collective of automata is used. In the present paper such a collective consists in a group of learning automata characterized with simple number parameters. The fuzzy measuring implemented as a game which is played sequentially with one automaton at a time, the result of the game defines next automaton to be played with. This game provides some measuring system that is very close to the procedure of collecting statistics in Probability Theory. For measuring of an unknown membership function a new concept has been introduced called Cognitive Generator which transforms a fuzzy singleton to ordinary crisp logic value. Considerations on various types of axiomatic approaches shows that the Cognitive Generator, as well as the Evidence Combination Axiomatic, belongs to one class of axiomatic theories, which may be used in application directly. The present paper contains also some programming examples aimed to illustrate our general approach.

Łukasiewicz fuzzy systems are fuzzy systems based on Łukasiewicz implication and Łukasiewicz t-norm and t-conorm as fuzzy operations. They are deeply rooted in classical logic while being fuzzy systems, so they establish a connection between classical logic and fuzzy logic. Łukasiewicz fuzzy systems with Center of Gravity defuzzification have been shown to have good approximation properties, however Center of Gravity defuzzification makes them to be computationally not very efficient. In the present paper we develop a real-time Łukasiewicz fuzzy system, using the Mean of Maxima defuzzification. This defuzzification will be directly computable for Łukasiewicz systems with certain properties. We investigate approximation properties of such systems and we obtain a generalization of a previous universal approximation result.

Ranking and ordering generalized fuzzy numbers are hot topics in decision making under uncertainty. By extension principle, addition and multiplication of generalized fuzzy numbers are presented for establishing goodness criteria. This paper initially proposes four criteria for judging the goodness of a given ranking or total ordering defined on a set of generalized fuzzy numbers and then discusses the methods of rankings and total orderings which satisfy these goodness criteria. Besides, the cardinality of the set of all generalized fuzzy numbers is, for the first time, determined.

In this work a new method of bio-inspired optimization based on the self-defense mechanism of plants applied to mathematical functions is presented. Habitats on the planet have gone through changes, so plants have had to adapt to these changes and adopt new techniques to defend from natural predators (herbivores). There are many works in the literature have shown that plants have mechanisms of self-defense to protect themselves from predators. When the plants detect the presence of invading organisms this triggers a series of chemical reactions that are released to air and attract natural predators of the invading organism (Bennett and Wallsgrove New Phytol 127(4):617–63, 1994 [1]; Melin et al Expert Syst Appl 40(8):3196–3206, 2013 [10]; Neyoy et al Recent Advances on Hybrid Intelligent Systems, 2013 [11]). For the development of this algorithm we consider as a main idea the predator prey model of Lotka and Volterra.

In this paper the experimental results of a new evolutionary algorithm are presented. The proposed method was inspired by the growth and reproduction of fungi. Experiments were executed and evaluated on discretized versions of common functions, which are used in benchmark tests of optimization techniques. The results were compared with other optimization algorithms and the directions of future research with many possible modifications/extension of the presented method are discussed.

Minimum Weight Dominating Set (MWDS) belongs to the class of NP-hard graph problem which has several real life applications especially in wireless networks. In this paper, we present a new hybrid genetic algorithm. Also, we propose a new heuristic algorithm for MWDS to create initial population. We test our hybrid genetic algorithm on (Jovanovic et al., Proceedings of the 12th WSEAS international conference on automatic control, modeling and simulation, 2010) [3] data set. Then the results are compared with existing algorithms in the literature. The experimental results show that our hybrid genetic algorithm can yield better solutions than these algorithms and faster than these algorithms.

This paper describes an optimization method based on genetic algorithms and particle swarm optimization for ensemble neural networks with type-1 and type-2 fuzzy aggregation for forecasting complex time series. The time series that was considered in this paper to compare the hybrid approach with traditional methods is the Taiwan Stock Exchange (TAIEX), and the results shown are for the optimization of the structure of the ensemble neural network with type-1 and type-2 fuzzy integration. Simulation results show that the ensemble approach produces good prediction of the Taiwan Stock Exchange.

According to the literature of Particle Swarm Optimization (PSO), there are problems of getting stuck at local minima and premature convergence with this algorithm. A new algorithm is presented in this paper called the Improved Particle Swarm Optimization using the gradient descent method as an operator incorporated into the Algorithm, as a function to achieve a significant improvement. The gradient descent method (BP Algorithm) helps not only to increase the global optimization ability, but also to avoid the premature convergence problem. The Improved PSO Algorithm (IPSO) is applied to the design of Neural Networks to optimize their architecture. The results show that there is an improvement with respect to using the conventional PSO Algorithm.

In this paper an ensemble of three neural networks with type-2 fuzzy weights is proposed. One neural network uses type-2 fuzzy inference systems with Gaussian membership functions for obtain the fuzzy weights; the second neural network uses type-2 fuzzy inference systems with triangular membership functions; and the third neural network uses type-2 fuzzy inference systems with triangular membership functions with uncertainty in the standard deviation. Average integration and type-2 fuzzy integrator are used for the results of the ensemble neural network. The proposed approach is applied to a case of time series prediction, specifically in the Mackey-Glass time series.

In principle, distributed heterogeneous commodity clusters can be deployed as a computing platform for parallel execution of user application, however, in practice, the tasks of first discovering and then configuring resources to meet application requirements are difficult problems. This paper presents a general-purpose resource selection framework that addresses the problems of resources discovery and configuration by defining a resource selection scheme for locating distributed resources that match application requirements. The proposed resource selection method is based on the frequencies of weighted condition attribute values of resources and the outstanding overall searching ability of genetic algorithm. The concept of soft set condition attributes reducts, which is dependent on the weighted conditions’ attribute value of resource parameters is used to achieve the required goals. Empirical results are reported to demonstrate the potential of soft set condition attribute reducts in the implementation of resource selection decision models with relatively higher level of accuracy.

The objective of this research is to develop a methodological approach to the making managerial decisions in HRM tasks, which have such specific features as multi-objectivity and heterogeneity of data, the hierarchal, quantitative, and qualitative nature of criteria, their ambiguity, the need for considering the expert evaluation of their weight, and the influence of the experts’ competence on the made decision. To ensure the adaptability of multi-criteria decision-making in HRM a modified TOPSIS method is proposed. Introducing additional components into the decision-making algorithm, this modification excludes the hierarchal structure of criteria and takes into account the competence of experts. The method is tested on the employment case study.

The levels of modeling the processes of interaction between supply and demand on the labor market of IT specialists identified. Different types of imbalance between supply and demand for IT specialists and identified, the main areas, models and methods of their coordination are defined. The management method by a supply and demand on IT specialists at the micro-level, based on fuzzy situation analysis and fuzzy pattern recognition is proposed. The method and algorithm of an estimation of the imbalance degree between supply and demand on IT-specialists labor market at the macro-level, based on fuzzy mismatch scale is offered.

Authors measured electroencephalograms (EEGs) as participants recognized and recalled 13 playing card images (from ace to king of club) presented on a CRT monitor. During the experiment, electrodes were fixed on the scalps of the participants. Four EEG channels located over the right frontal and temporal cortices (Fp₂, F₄, C₄ and F₈ according to the international 10–20 system) were used in the discrimination. Sampling data were taken at latencies between 400 and 900 ms at 25 ms intervals for each trial. Thus, data were 84 dimensional vectors (21 time point X 4 channels). The number of objective variables was 13 (the number of different cards), and the number of explanatory variates was thus 84. Canonical discriminant analysis was applied to these single trial EEGs. Results of the canonical discriminant analysis were obtained using the jack knife method and were 100% of nine participants. We could perform playing card estimation magic without a trick. This fact is sub production based on our series of precedent research.

Measurement and expert estimates are never absolutely accurate. Thus, when we know the result M(u) of measurement or expert estimate, the actual value A(u) of the corresponding quantity may be somewhat different from M(u). In practical applications, it is desirable to know how different it can be, i.e., what are the bounds $$f(M(u))\le A(u)\le g(M(u))$$f(M(u))≤A(u)≤g(M(u)). Ideally, we would like to know the tightest bounds, i.e., the largest possible values f(x) and the smallest possible values g(x). In this paper, we analyze for which (partially ordered) sets of values such tightest bounds always exist: it turns out that they always exist only for complete lattices.

Li (IEEE Trans Cybern 43:610-621, 2013) [1] recently proposed a method for solving matrix games with fuzzy payoffs and claimed that the obtained minimum expected gain of Player I and maximum expected loss of Player II, will be identical. Chandra and Aggarwal (Eur J Oper Res 2015. https://doi.org/10.1016/j.ejor.2015.05.011) [2], in their recent paper, pointed out the shortcomings of Li’s approach and overcome the shortcomings of Li’s approach. Chandra and Aggarwal, transformed the fuzzy mathematical programming problem into a multiobjective programming problem and obtained its result by using GAMS software. In this paper, it is pointed out that Chandra and Aggarwal have not considered some necessary constraints for the value of game to be a fuzzy number. Further, a new method (named as Jagdambika method) is proposed to overcome the limitations of existing method and to obtain the solution of matrix games with fuzzy payoffs. To illustrate the proposed Jagdambika method, an existing numerical problem of matrix games with fuzzy payoffs is solved by the proposed Jagdambika method.

This paper analyzes various methods of structural and parametric optimization for fuzzy control and decision-making systems. Special attention is paid to hierarchical structure selection, rule base reduction, and reconfiguration in the presence of incomplete data sets. In addition fuzzy system parameter optimization based on gradient descent, Kalman filters, H-infinity filters, and maximization of envelope curve values, are considered for unconstrained and constrained cases. Simulation results show the validity of the proposed methods.

This paper focuses on a statistical comparison with a proposed Fuzzy BCO based on an Interval Type-2 Fuzzy System and the Original BCO algorithm using Trapezoidal Membership Functions. The Fuzzy Bee Colony Optimization method applied for tuning the parameters of the Fuzzy Logic Controller is presented. The objective of the work is based on the main reasons for the statistical comparison of BCO and Fuzzy BCO algorithm that is to find the optimal design in the fuzzy logic controller for two problems in fuzzy control. We added perturbations in the model with band-limited noise so that the Interval Type-2 Fuzzy Logic System is better analyzed under uncertainty and to verify that the Fuzzy BCO shows better results than the Original BCO.

The current work handles Automatic Generation Control (AGC) of an interconnected two area hydro-hydro system. The proposed system is integrated with conventional Proportional Integral (PI) as well as Fuzzy Logic Controller (FLC). Since, the conventional PI controller does not offer sufficient control performance. Thus, non-linearities such as the Generation Rate Constraint (GRC) and Governor Dead Band (GDB) are included in the system in order to overcome this drawback with employing Fuzzy Logic Controller (FLC) in the system. The results reported the time domain simulation that used to study the performance, when 1% step load disturbance is given in either area of the system. Furthermore, the conventional PI controller simulation results are compared to fuzzy logic controller. The simulation results depicted that the FLC achieved superior control performance.

Heating, ventilation and air-conditioning (HVAC) system is an important component of Smart Home. The HVAC system is connected to network for the transfer of measurement data and control action packets from sensors to controller and controller to actuators respectively. The HVAC system can therefore be categorized as a Cyber-Physical system (CPS). Such systems are prone to communication uncertainties like packet losses and delays. Such systems require integrated architecture of communication and control. An evolutionary algorithm tuned fuzzy PI controller design coupled in a communication framework is presented in this paper for performance improvements of HVAC system. The entire architecture considers relevant system objectives based on system states and actuator actions. The formulated problem has been solved through real time optimization approach using the designed controller following the communication protocol. The developed algorithm helps in obtaining optimal actuator actions and shows a fast convergence to the different desired temperature sets. The results also show that the system can recover from sudden burst packet losses.

There exist different methods of malware identification, while the most common is signature-based used by anti-virus vendors that includes one-way cryptographic hash sums to characterize each particular malware sample. In most cases such detection results in a simple classification into malware and goodware. In a modern Information Security society it is not enough to separate only between goodware and malware. The reason for this is increasingly complex functionality used by various malware families, in which there has been several thousand of new ones created during the last decade. In addition to this, a number of new malware types have emerged. We believe that Soft Computing (SC) may help to understand such complicated multinomial problems better. To study this we ensambled a novel large-scale dataset based on 400 k malware samples. Furthermore, we investigated the limitation of community-accepted Soft Computing methods and can clearly observe that the optimization is required for such non-trivial task. The contribution of this paper is a thorough investigation of large-scale multinomial malware classification by Soft Computing using static characteristics.

The paper presents experimental evaluation of image classification in the field of web content filtering using bag of visual features and convolutional neural networks approach. A more difficult data set than traditionally used ones was built from very similar types of images in order to make conditions closer to real world practice. F1-measure of classifiers that are based on bags of visual features was significantly lower than that reported in previously published papers. Convolutional neural networks performed much better. Also, we measured and compared training and prediction time of various algorithms.

Hierarchical fuzzy systems are one of the most popular solutions for the curse of dimensionality problem occurred in complex fuzzy rule based systems with a large number of input parameters. Nevertheless these systems have a hidden inaccuracy and instability problem. In detail, the outputs of hierarchical systems, based on Mamdani style inference, differ from the outputs of equivalent single system. Moreover they are not stable in any variation of system modeling even if the rules and membership functions do not expose any differentiation. This paper revisits inaccuracy and instability problems of hierarchical fuzzy inference systems. It investigates the differentiation in systems’ behaviors against the variations in system modeling, and provides a pattern to identify the magnitude of this differentiation.

This chapter presents an easily implementable method of fuzzy shape extraction for shape recognition. The method uses Fuzzy Hypermatrix-based classifiers in order to find the potential location of the target objects based on their colors, then determines the areas where the most densely occurring positive findings in order to restrict the area of operation thus speeding the process up. In these areas the edges are detected, the edges are mapped to tree structures, which are trimmed down to simple outline sequences using heuristics from the Fuzzy Hypermatrix. Finally, fuzzy information is extracted from the outlines that can be used to classify the shape with a fuzzy inference machine.

Edge detection is a fundamental task for any image processing system. It serves as an entry point for a lot of major algorithms such as image identification, segmentation, and feature extraction. Consequently, a lot of different techniques have evolved to accomplish this task. The most commonly known methods include Sobel, Laplacian, Prewitt, and fuzzy logic based methodology. In this paper, a novel, pipelined architecture for a type-1 fuzzy edge detector system is discussed. It has been implemented on different Xilinx devices. The fuzzy system consists of modules as follows: preprocessing, fuzzification which creates four fuzzy input variables, inference and defuzzification resulting in a single crisp output. The hardware accelerator utilizes a pipeline of seven stages. Each stage requires just one clock cycle. The system can operate at a frequency range of 83–100 MHz depending on the speed grade of the FPGA device it is compiled to. Using the 1080P HD standard, the proposed architecture is capable of processing up to 45 fps which makes it feasible for real time applications. The system was developed using Xilinx Vivado and 7000-series FPGA devices. Simulations were carried out using ModelSim by Mentor Graphics.

Edge detection algorithms have traditionally utilized the Gaussian Linear Filter (GLF) for image smoothing. Although GLF has very good properties in removing noise and unwanted artifacts from an image, it is also known to remove many valid edges. To cope with this problem, edge preserving smoothing filters have been proposed and they have recently attracted increased attention. In this paper, we quantitatively compare three prominent edge preserving smoothing filters; namely, Bilateral Filter (BLF), Anisotropic Diffusion (AD) and Weighted Least Squares (WLS) with each other and with GLF in terms of their effects on the final detected edges using the precision/recall framework of the famous Berkeley Segmentation Dataset (BSDS 300). We conclude that edge preserving smoothing filters indeed improve the performance of the edge detectors, and of the filters compared, WLS yields the best performance with AD also outperforming the GLF.

In many practical applications, it turned out to be efficient to assume that the signal or an image is sparse, i.e., that when we decompose it into appropriate basic functions (e.g., sinusoids or wavelets), most of the coefficients in this decomposition will be zeros. At present, the empirical efficiency of sparsity-based techniques remains somewhat a mystery. In this paper, we show that fuzzy-related techniques can explain this empirical efficiency. A similar explanation can be obtained by using probabilistic techniques; this fact increases our confidence that our explanation is correct.

When using an individualized learning system ALEKS to study mathematics, bilingual students can use both English-language and Spanish-language modules. When we started our study, we expected that those Spanish-language students whose knowledge of English is still not perfect would first use mostly Spanish-language modules, and that their use of English-language modules will increase as their English skills increase. Instead, what we found is that even students who are not very skilled in English use both Spanish-language and English-language modules. This raises a natural question: why, in spite of the presence of well-designed well-tested easy to Spanish-language models, the students benefit from also using English-language modules—which for them are not so easy to access (they use Google translator). In this paper, we use fuzzy logic to provide a possible theoretical explanation for this surprising behavior.

Industrial applications often face elaborated problems. In order to solve them properly a great deal of complexity and data diversity has to be managed. In this paper we present a planning system that is used globally by the Volkswagen Group. We introduce the specific challenges that this industrial application faces, namely a high complexity paired with diverse heterogeneous data sources, and describe how the problem has been modelled and solved. We further introduce the core technology we used, the revision of Markov networks. We further motivate the need to handle planning inconsistencies and present our framework consisting of six main components: Prevention, Detection, Analysis, Explanation, Manual Resolution, and Automatic Elimination.

Traditionally, academic ranking of students’ performance is based on test score which can be interpreted in linguistic terms such as ‘very good’, ‘good’, ‘poor’, ‘very poor’ with varying degree of certainty attached to each description. There could be several students in a school having ‘very poor’ performance with varying degree of certainty. The authorities would certainly like to improve students’ academic performance based on their ranking. The case study relates to the combination of Zadeh-Deshpande formalism with Bellman-Zadeh method to arrive at an optimal ranking of especially ‘very poor’ students based on well-defined performance shaping factors.

Fuzzy logic techniques were originally designed to translate expert knowledge—which is often formulated by using imprecise (“fuzzy”) from natural language (like “small”)—into precise computer-understandable models and control strategies. Such a translation is still the main use of fuzzy techniques. Lately, it turned out that fuzzy methods can help in another class of applied problems: namely, in situations when there are semi-heuristic techniques for solving the corresponding problems, i.e., techniques for which there is no convincing theoretical justification. Because of the lack of a theoretical justification, users are reluctant to use these techniques, since their previous empirical success does not guarantee that these techniques will work well on new problems. In this paper, we show that in many such situations, the desired theoretical justification can be obtained if, in addition to known (crisp) requirements on the desired solution, we also take into account requirements formulated by experts in natural-language terms. Naturally, we use fuzzy techniques to translate these imprecise requirements into precise terms.

In the past half century of fuzzy systems they were used to solve a wide range of complex problems, and the field of recommendation is no exception. The mathematical properties and the ability to efficiently process uncertain data enable fuzzy systems to face the common challenges in recommender systems. The main contribution of this paper is to give a comprehensive literature overview of various fuzzy based approaches to the solving of common problems and tasks in recommendation systems. As a conclusion possible new areas of research are discussed.

Chloroform is one of the most prevalent disinfection byproducts (DBPs) formed in swimming pools through reactions between disinfectants and organic contaminants. Chloroform and related DBPs have been a subject of research in exposure and human health risk assessments over the last several decades. Physiologically based pharmacokinetic (PBPK) models are one tool that is being used increasingly by researchers to evaluate the health impacts of swimming pool exposures. These models simulate the absorption, distribution, metabolism and excretion of chemicals in the human body to assess doses to sensitive organs. As with any model, uncertainties arise from variability and imprecision in inputs. Among the most uncertain model parameters are the partition coefficients which describe uptake and distribution of chemical to different tissues of the body. In this paper, a fuzzy based model is presented for improving the description and incorporation of uncertain parameters into the model. The fuzzy PBPK model compares well with the deterministic model and measured concentrations while providing more information about uncertainty.

In the paper, the application of Mamdani-type fuzzy inference method to the expert evaluation of the impact of tax administration reforms on the tax potential is investigated. As input data of the system are taken reforms in tax administration and fuzzified by the triangle, trapezoid, Gaussian and Bell membership functions. It has been shown that the suggested fuzzy approach is one of the effective methods for evaluation of tax potential.

In the traditional chemical kinetics, the rate of each reaction $$\mathrm{A} + \ldots + \mathrm{B} \rightarrow \ldots $$A+…+B→…is proportional to the product $$c_A\cdot \ldots \cdot c_B$$cA·…·cB of the concentrations of all the input substances A, ..., B. For high concentrations $$c_A,\ldots ,c_B$$cA,…,cB, the reaction rate is known to be proportional to the minimum $$\min (c_A,\ldots ,c_B)$$min(cA,…,cB). In this paper, we use fuzzy-related ideas to derive the formula of the reaction rate for situations intermediate between usual and high concentrations.

Determining how to trade off individual criteria is often not obvious, especially when attributes of very different nature are juxtaposed, e.g. health and money. The difficulty stems both from the lack of adequate market experience and strong ethical component when valuing some goods, resulting in inherently imprecise preferences. Fuzzy sets can be used to model willingness-to-pay/accept (WTP/WTA), so as to quantify this imprecision and support the decision making process. The preferences need then to be estimated based on available data. In the paper, I show how to estimate the membership function of fuzzy WTP/WTA, when decision makers’ preferences are collected via survey with Likert-based questions. I apply the proposed methodology to a data set on WTP/WTA for health. The mathematical model contains two elements: the parametric representation of the membership function and the mathematical model how it is translated into Likert options. The model parameters are estimated in a Bayesian approach using Markov-chain Monte Carlo. The results suggest a slight WTP-WTA disparity and WTA being more fuzzy as WTP. The model is fragile to single respondents with lexicographic preferences, i.e. not willing to accept any trade-offs between health and money.

The first step in a knowledge base expert system could be to mathematically evaluate perceptions of the domain experts which are invariably expressed in linguistic terms based on their tactic knowledge followed by the defined steps in differential diagnostic process. We have simulated the process in three stages, especially in gynaecological diseases. Stage I, refers to Type1 Fuzzy Relational Calculus used to arrive at the initial diagnostic labels for gynaecological diseases in patients and to estimate similarity between the domain experts. The case study focused only on the identified gynaecological diseases arrives at comparatively low diagnostic percentage, and therefore termed as Initial Screening Process. The output of the algorithm for patient diagnostic records, considering the variability among the experts, was tested for diagnosing a single disease. After application of ‘History’ fuzzy rule base in Stage 2, using Type 1 Fuzzy Inference System, the accuracy was increased to some extent which was further enhanced to high level by Stage III for the prototype of 226 patients diagnosed by the model. The need based research presented will ultimately assist physicians and upcoming gynaecologists.

Over the last decade a number of technologies have been developed that support individuals in keeping themselves active. This can be done via e-coaching mechanisms and by installing more advanced technologies in their homes. The objective of the Active Healthy Ageing (AHA) Platform is to integrate existing tools, hardware, and software that assist individuals in improving and/or maintaining a healthy lifestyle. This architecture is realized by integrating several hardware/software components that generate various types of data. Some examples include heart-rate data, coaching information, in-home activity patterns, mobility patterns, and so on. Various subsystems in the AHA platform can share their data in a semantic and interoperable way, through the use of a AHA data-store and a wearable devices ontology. This paper presents such an ontology for wearable data interoperability in Ambient Assisted Living environments. The ontology includes concepts such as height, weight, locations, activities, activity levels, activity energy expenditure, heart rate, or stress levels, among others. The purpose is serving application development in Ambient Intelligence scenarios ranging from activity monitoring and smart homes to active healthy ageing or lifestyle profiling.

To ensure the quality of pavement, it is important to make sure that the resilient moduli—that describe the stiffness of all the pavement layers—exceed a certain threshold. From the mechanical viewpoint, pavement is a non-linear medium. Several empirical formulas have been proposed to describe this non-linearity. In this paper, we describe a theoretical explanation for the most accurate of these empirical formulas.

Accurate prediction of pollutant concentration is very important part in any air quality management program (AQMP). The conventional time series modelling techniques like ARIMA has showing poor prediction and forecasting, as air quality is non-linear and complex phenomenon. Although neural networks have been applied for prediction of air quality data in the previous studies, the model performance was very poor as they don’t consider the data as time series in their algorithms. Combining the aspects of both neural networks and time series analysis, Nonlinear Autoregressive models with exogenous input (NARX) based neural networks were found to predict chaotic time series better because of better learning and faster convergence than the conventional neural network algorithms. In the present work, meteorological and traffic parameters near busy urban corridors were used to train NARX based neural network model for the prediction of ambient air quality. Diagnostic analysis between different model variables was done to understand the relationship between one other. The developed model predicted NO_x and SO₂ concentrations with a very good performance over the entire dataset.

In this paper, we show how fuzzy and probabilistic techniques can be used in environment-related data processing. Specifically, we will show that these methods help in solving two environment-related problems: how to predict the birds’ nesting sites and how to measure shoreline erosion.

Urban air quality has degraded at an alarming rate due to rapid urbanisation and industrialization in megacities. Therefore, there is an urgent need to assess air quality and suggest risk mitigation measures. In this paper, air quality of Chennai city was evaluated using different Fuzzy Synthetic Evaluation (FSE) techniques i.e. Fuzzy similarity method (FSM) and Simple fuzzy classification (SFC) and the results are compared with the National air quality index (NAQI). In the case of SFC weights for different pollutants were computed using Shannon’s information entropy. Seasonal analysis of the criteria pollutants shows highest concentration during the winter season followed by pre-monsoon and summer season. The lowest concentration was observed during Monsoon in most cases. The FSE results are optimistic as compared to the NAQI due to aggregation of pollutant concentration as opposed to maximising function in NAQI which reconfirms the findings of earlier researchers. FSE can be used as a decision making tool to communicate the overall air quality to policy makers/end users (Public) in a simplified qualitative form.

Green urban areas have natural attributes that provide important environmental services. However, their impacts depend on the local characteristics, so it is necessary to evaluate the environmental potentials of such areas individually. The lack of methodologies to assess the quality of green urban areas led to the implementation of the present work, which proposes a calculation model for assessing the environmental quality of these spaces. The construction of the model used fuzzy systems capable of handling the subjectivity of the variables, with the creation of fuzzy rule-based systems that permitted working with parameters of different natures. The variables employed were the percentage of vegetation in the area in question and its quality, the latter being determined by analysis of the degree of maturation. Forest formations were considered potentially more advantageous for the provision of environmental services, compared to savannah and grasslands. The model was constructed considering the physical and biological characteristics of the city of Sorocaba, as well as Brazilian standard classifications of vegetation types and their successional stages.

The Environmental Impact Assessment (EIA) is a mechanism used by governments and private institutions to predict environmental damage and optimize positive impacts. One of the greatest challenges of the EIA process is to achieve an effective integration of the various tools and the analytical procedures. This paper proposes a new methodology for the application of impact’s identification and characterization matrices by the use of Fuzzy Logic, using specifically Fuzzy Relations Theory. Three Weight Matrices of State and Association, for each type of expertise (physical, biotic, and anthropic) were created. Fuzzy Relations to aggregate all these matrices were used, and a Weighted Response Matrix was obtained. The developed fuzzy methodology was applied to real cases of EIA, aiming to compare the results. The EIA Mario Covas Road Program—Modified Southern Section was used, and the fuzzy interaction matrix proves to be a successful tool. Because of the results, it was possible to infer that the new methodology, using fuzzy approach is an effective and practical tool in the Environmental Impact Assessment.

Changing the behavior and habits of people and promoting sustainable production and consumption, has caused investment and action by Governments in promoting sustainability as a development model. Although many studies have been developed to evaluate the conditions for sustainability, few take into account the economic, environmental and social aspects altogether. Thus, the aim of this study is the development of an index to measure the degree of sustainability of municipalities using indicators for the social, demographic, economic and environmental dimensions. The methodology employed here is based on the concepts of fuzzy logic which models is subjectivity, uncertainty and imprecision. The index is applied to all 5,565 municipalities in Brazil generating a national sustainability study. After an assessment of the data using our index, a comparison is made with the Municipal Human Development index. Similar results are obtained by both methodologies. However, we argue that a fuzzy logic approach is useful since it uses linguistic variables and is intuitive to implement. Thus, the sustainability index is suitable to aggregate the various indicators and it is an important tool for decision makers. Since it provides information that is useful for the formulation, monitoring and evaluation of public policies.