Search

Machine Learning (ML) has attracted great interest in the modeling of systems using computational learning methods, being utilized in a wide range of advanced fields due to its ability and efficiency to process large amounts of data and to make predictions or decisions with a high degree of accuracy. However, with the increase in the complexity of the models, ML's methods have presented complex structures that are not always transparent to the users. In this sense, it is important to study how to counteract this trend and explore ways to increase the interpretability of these models, precisely where decision‐making plays a central role. This work addresses this challenge by assessing the interpretability and explainability of fuzzy‐based models. The structural and semantic factors that impact the interpretability of fuzzy systems are examined. Various metrics have been studied to address this topic, such as the Co‐firing Based Comprehensibility Index (COFCI), Nauck Index, Similarity Index, and Membership Function Center Index. These metrics were assessed across different datasets on three fuzzy‐based models: (i) a model designed with Fuzzy c‐Means and Least Squares Method, (ii) Adaptive‐Network‐based Fuzzy Inference System (ANFIS), and (iii) Generalized Additive Model Zero‐Order Takagi‐Sugeno (GAM‐ZOTS). The study conducted in this work culminates in a new comprehensive interpretability metric that covers different domains associated with interpretability in fuzzy‐based models. When addressing interpretability, one of the challenges lies in balancing high accuracy with interpretability, as these two goals often conflict. In this context, experimental evaluations were performed in many scenarios using 4 datasets varying the model parameters in order to find a compromise between interpretability and accuracy. [ABSTRACT FROM AUTHOR]

The information generated about crop harvesting can aid several purposes, including the maximization of crop yield, minimizing crop losses, assessing quality deterioration and crop health, and studying phenology. This study aims to detect the harvesting cycle of Sugarcane-plant and ratoon and analyze the underlying trends. The agriculture domain makes use of remote sensing data extensively for applications like crop yield forecast, crop type mapping, monitoring crop patterns, etc. Sugarcane is cultivated in abundance in the Muzzafarnagar district of Uttar Pradesh, India. The two variants of sugarcane (ratoon and plant) are commonly grown in this region along with other crops like wheat, paddy, and oil seeds (sesame). To monitor the harvesting of the sugarcane crop fields, the phenology of the crop type (from germination to maturity stage) was considered as base temporal data from the DOVE sensor. The temporal Planetscope DOVE sensor base data with particular harvesting dates were used to map harvested fields of sugarcane ratoon and plants on a particular date. Modified soil adjusted vegetation index 2 (MSAVI2) and its variant class-based sensor independent modified soil adjusted vegetation index 2 (CBSI-MSAVI2) were tested to reduce spectral dimensionality and map the harvested fields on approximately a weekly basis. The harvested sugarcane ratoon and plant fields were successfully mapped using the innovative machine-learning approach with a Mean Membership Difference (MMD) value of about 0.01 and 0.02 respectively. [ABSTRACT FROM AUTHOR]

Introduction: Nowadays, five-phase permanent magnet synchronous motors have been widely used in the industrial and transportation fields, and the existing sliding mode control methods for speed control systems can no longer meet the requirements such as fast response and good stability. Methods: In light of the aforementioned considerations, the study initially employs mathematical modeling to elucidate the five-phase permanent magnet synchronous motor. Secondly, on the basis of proportional-integralderivative sliding mode control, radial basis function and Takagi-Sugeno-Kang fuzzy model are introduced for parameter identification and optimization and regulation. Finally, a new neural network regulation algorithm and speed control strategy are proposed. Results and Discussion: The experimental results demonstrated that the expected parameter optimization rate of the regulation algorithm can reach 90%, and the overshooting amount under small inertia working condition is only 3%, and the adjustment time is 0.02 s. The new control algorithm can be used to control the motor speed with the lowest speed fluctuation and the fastest recovery time. In addition, when affected by the load torque, the motor speed controlled by the new strategy fluctuated the least, with a speed drop of only 1% and the fastest recovery time of 0.02 s. It exhibited the lowest control error of 3.7% and the lowest overshooting amount of 5.9%. Conclusion: In summary, the suggested approach has the potential to significantly enhance the speed control system’s control performance while maintaining strong resilience and anti-interference capabilities. The method has certain guiding significance for the practical application of five-phase permanent magnet synchronous motor speed control system. [ABSTRACT FROM AUTHOR]

Vehicle gasoline engines are fueled by ethanol as a low-cost environmentally renewable fuel in several areas. Ethanol possesses high antidetonation properties, which is further appropriate for gasoline engines. Therefore, the current research proposes a new conceptual methodology to examine the perspective merits of motor gasoline-ethanol (GE) mixtures and their formulation using Fuzzy model. The effect of these blends on antidetonation combustion properties, in respect to motor and research octane ratings (MON and RON) of automotive gasoline-ethanol (GE) mixtures at several proportions are also studied. Industrial-grade ethanol was mixed in fractions of 0%, 5%, 10%, 20%, 30%, 40%, 50%, 70%, and 85% by weight with lower octane hydrocarbons, comprising heavy hydrocracked naphtha (HHN) and straight run naphtha (SRN) as baseline fuels. The experimental results proved that mid-ethanol fuels (E40) based on lower-octane fractions, including SRN and HHN were produced. The optimal proportion for these blends was E40 with SRN-60 and HHN-60 by weight percent. The results demonstrated that the addition of ethyl alcohol into motor gasoline has maximized the entire petrol engine performance. Additionally, a great gasoline-ethanol fuel mixture (E40) is appropriate for small engine speeds. The obtained results declared that the created Fuzzy models are good, corresponding with the actual tested results to a good level, exhibiting the trustworthiness of the Fuzzy models. Furthermore, this is a perspective to employing ethanol at low mixing ratios, excluding any alterations in the mechanical sections of the gasoline engine and automotive gasoline system as well. Finally, mid-ethanol fuels based on low-octane hydrocarbon fractions, including SRN and HHN were produced. Even though the utilization of gasoline-free ethanol needs several alterations in the gasoline system in internal combustion engines and the engine design, GE blends at small percentages as mid-ethanol fuel can be employed without any alteration to the engines. [Display omitted] • Blending ON with ethanol was done linearly. • The created models corresponded with the actual tested results. • The optimal blend was E40 with SRN-60 and HHN-60. [ABSTRACT FROM AUTHOR]

The paper studies the problem of robust tracking of the fuzzy systems in the presence of uncertainties. A robust feedback controller based on the linear matrix inequality (LMI) is proposed using a Lyapunov function combined with the supervisory controller. Hence, the sufficient stability condition for the general type of robust fuzzy tracking controls has been formulated and the performance of the controller has been tested for different classes of the fuzzy models. It has been demonstrated that the smooth fuzzy compositions have better performance rather than the standard fuzzy compositions, (i.e., min–max compositions and product–sum compositions). Considering that the smooth fuzzy compositions are efficient in face of the uncertainties, it is believed that the present work opens up new doors for wider applications of the smooth fuzzy compositions in the industry. [ABSTRACT FROM AUTHOR]

A method and system for diagnosing subsurface scars of two-component biological structures has been developed based on the measurement and analysis of orientation and phase images of the Mueller matrices of optically thin samples. The diagnostic method is complemented by a classification analysis of informative features of Mueller matrix images. The diagnostic system has expanded functionality due to the introduction of a decision support subsystem. Fuzzy models of decision-making support were derived and the reliability of diagnosis of cervical oncology using the developed system was evaluated. [ABSTRACT FROM AUTHOR]

In order to achieve a more efficient allocation of healthcare resources in the near future, it is crucial to understand the patterns and causes of excess mortality and hospitalizations. Neonatal mortality still poses a significant challenge, particularly in developed nations where the mortality rates are already low and healthcare resources are generally available to most of the population. Furthermore, the low mortality rates mean that the data available for modeling are often very limited, restricting the modeling methods that can be used. It is also important that the chosen methods allow for explainable, non-black-box models that can be interpreted by healthcare professionals. Considering these challenges, the work hereby presented thoroughly analyzed the time series of the neonatal mortality rates in Portugal between 2014 and 2019 in terms of trend and seasonal patterns. The applicability and performance of different data-based methods were also analyzed. Furthermore, the mortality rates were studied in terms of their relation to environmental variables, such as temperature and air pollution indicators, with the goal of establishing causal relations between such variables and excess mortality. The preliminary results show that ARMA, neural and fuzzzy models are able to forecast the studied mortality rates with good performance. In particular, neural models have the best predictive performance, while fuzzy models are well suited to obtain interpretable models with acceptable performance. [ABSTRACT FROM AUTHOR]

A system of two-wave polarization reproduction of distributions of phase and orientation parameters of anisotropy and coefficients of linear and circular dichroism of bile films in the diagnosis of pathologies has been developed. Fuzzy decision support models were derived and the reliability of the diagnosis of type 2 diabetes using the developed system was evaluated. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of fuzzy modeling methods in the field of environmental engineering. Since predicting meteorological data is considered to be a challenging task, the current work aimed to assess the performance of various fuzzy models on temperature, solar radiation, and wind speed forecasting. The models studied were taken from the fuzzy systems literature, varying from well-established to the most recent methods. Four cases were considered: a Wang–Mendel (WM)-based fuzzy predictive model, an adaptive network fuzzy inference system (ANFIS), a fuzzy system ensemble, and patch learning (PL). The prediction systems were built from input/output data without any prior information, in a model-free approach. The ability of the models to display high performance on complex real datasets, provided by the National Observatory of Athens, was demonstrated through numerical studies. Patch learning managed to not only display a similar approximation ability to that of strong machine learning models, such as support vector machines and Gaussian processes, but also outperform them on the highly demanding problem of wind speed prediction. More accurately, as far as wind speed prediction is concerned, patch learning produced a 0.9211 root mean squared error for the training data and a value of 0.9841 for the testing data. The support vector machine provided a 0.9306 training root mean squared error and a 0.9891 testing value. The Gaussian process model resulted in a 0.9343 root mean squared error for the training data and a value of 0.9861 for the testing data. Finally, as shown by the numerical experiments, the fuzzy system ensemble exhibited the highest generalisation performance among all the intelligent models. [ABSTRACT FROM AUTHOR]

Models of decision rules have been developed to support decision-making based on the principles of fuzzy logic for a system of azimuthally independent polarization mapping of azimuths and ellipticities of polarization images of biological layers. The reliability of differentiation of chronic and acute myocardial ischemia in laser polarization-invariant diagnosis was evaluated. [ABSTRACT FROM AUTHOR]

The use of semi-hypertemporal (SH) satellite data can play an important role in species differentiation based on the physiological and phenological characteristics of different plant species in a forest. Mapping of plant species is essential for addressing problems related to their management and conservation. This study has been conducted to map Acacia catechu from a heterogeneous forest area using SH images in fuzzy-based classifiers. This study has claimed that an individual sample as mean (ISM) training approach outperforms the conventional mean-based approach for selecting the training samples for fuzzy-based possibilistic $c$ means (PCM), noise clustering (NC), and modified PCM (MPCM) algorithms. Furthermore, the SH dataset is found to be better for a particular species extraction in contrast to the single date image, using both the conventional and ISM approaches. Moreover, an increasing number of images in the SH dataset produces significantly better classification outputs. This study can be used to map the endangered species with improved classification accuracy and for better management practices. [ABSTRACT FROM AUTHOR]

The economic importance of SMEs in the world is transcendental, since the union of their local profits generates global benefits; however, managers do not know how to visualize their problems to implement automated processes. There are solutions on the market, but they are expensive and with unnecessary modules that are not useful for SME leaders. Systems based on fuzzy models provide technology that allows, through Fuzzy Cognitive Maps (FCMs synthesizing problems and show solution strategies; In this case, the problems became Items in an FCM to simulate the entry and exit of causes and effects in the company, converted into proposals for automated processes. [ABSTRACT FROM AUTHOR]

Wind turbine power curve (WTPC) modeling from measured data is essential to predict the power generation from wind farms. Polynomial regression is commonly the first choice for this purpose, but there are other more powerful alternatives based on neural networks and fuzzy algorithms, for instance. Despite the existence of previous applications of such learning algorithms to WTPC modeling, a critical analysis of their performances has not yet been carried out while taking into into account both quantitative and quantitative aspects. Quantitative figures of merit include the root-mean-square error (RMSE) and R-squared ( R 2 ), whereas qualitative approaches are often based on simple visual inspection. In this context, this work reports the results of a comprehensive performance comparison involving the estimation of WTPC. The study comprises three neural-network-based models, that is, multilayer perceptron (MLP), radial basis function (RBF), and extreme learning machine (ELM); as well two fuzzy-logic-based models, that is, Takagi-Sugeno-Kang (TSK) and adaptive network fuzzy inference system (ANFIS). Using two real-world challenging data sets, it is possible to evaluate how the models perform concerning the accuracy of the curve fitting, sensitivity to parameter initialization, and occurrence of pathological solutions. Relevant issues, such as hyperparameter settings and data normalization are also addressed. The obtained results confirm the fact that the model selection should not rely only on quantitative performance indices. Thus, it is reasonable to state that the design of general-purpose modeling tools such as the ones evaluated in this work should incorporate domain-specific knowledge to provide good accuracy associated with reliable results. [ABSTRACT FROM AUTHOR]

In order to achieve a more efficient allocation of healthcare resources in the near future, it is crucial to understand the patterns and causes of excess mortality and hospitalizations. Neonatal mortality still poses a significant challenge, particularly in developed nations where the mortality rates are already low and healthcare resources are generally available to most of the population. Furthermore, the low mortality rates mean that the data available for modeling are often very limited, restricting the modeling methods that can be used. It is also important that the chosen methods allow for explainable, non-black-box models that can be interpreted by healthcare professionals. Considering these challenges, the work hereby presented thoroughly analyzed the time series of the neonatal mortality rates in Portugal between 2014 and 2019 in terms of trend and seasonal patterns. The applicability and performance of different data-based methods were also analyzed. Furthermore, the mortality rates were studied in terms of their relation to environmental variables, such as temperature and air pollution indicators, with the goal of establishing causal relations between such variables and excess mortality. The preliminary results show that ARMA, neural and fuzzzy models are able to forecast the studied mortality rates with good performance. In particular, neural models have the best predictive performance, while fuzzy models are well suited to obtain interpretable models with acceptable performance.

Copyright of Eastern-European Journal of Enterprise Technologies is the property of PC TECHNOLOGY CENTER and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the bounded variation property of fuzzy models with smooth compositions have been studied, and they have been compared with the standard fuzzy composition (e.g., min–max). Moreover, the contribution of the bounded variation of the smooth fuzzy model for the noise removal and edge preservation of the digital images has been investigated. Different simulations on the test images have been employed to verify the results. The performance index related to the detected edges of the smooth fuzzy models in the presence of both Gaussian and Impulse (also known as salt-and-pepper noise) noises of different densities has been found to be higher than the standard well-known fuzzy models (e.g., min–max composition), which demonstrates the efficiency of smooth compositions in comparison to the standard composition. [ABSTRACT FROM AUTHOR]

Fintech has not changed the nature and risk attributes of financial business. Its openness, interoperability, and other characteristics trigger the concealment, infectivity, universality and sudden characteristics of financial risk more obviously, and the potential systemic risk more complexly. This paper adopts fuzzy set analysis to conduct a comprehensive review of Fintech risk. It is found that the technology risk, moral hazard and legal risk, with a weight of up to 80%, are the dominant factors affecting the Fintech risk, while other prominent credit risk, market risk and operational risk in the traditional financial field account for a small proportion, but they still cannot be ignored. The research of this paper enriches the relevant research on the quantification of Fintech risk, and helps to strengthen risk prevention from the aspect of enhancing the safety of Fintech infrastructure, improving the legal system, sound the regulatory framework and strengthening industry self-discipline. [ABSTRACT FROM AUTHOR]

Abstract Nowadays, a huge amount of data are generated, often in very short time intervals and in various formats, by a number of different heterogeneous sources such as social networks and media, mobile devices, internet transactions, networked devices and sensors. These data, identified as Big Data in the literature, are characterized by the popular Vs features, such as Value, Veracity, Variety, Velocity and Volume. In particular, Value focuses on the useful knowledge that may be mined from data. Thus, in the last years, a number of data mining and machine learning algorithms have been proposed to extract knowledge from Big Data. These algorithms have been generally implemented by using ad-hoc programming paradigms, such as MapReduce, on specific distributed computing frameworks, such as Apache Hadoop and Apache Spark. In the context of Big Data, fuzzy models are currently playing a significant role, thanks to their capability of handling vague and imprecise data and their innate characteristic to be interpretable. In this work, we give an overview of the most recent distributed learning algorithms for generating fuzzy classification models for Big Data. In particular, we first show some design and implementation details of these learning algorithms. Thereafter, we compare them in terms of accuracy and interpretability. Finally, we argue about their scalability.

The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fuzzy numbers (TrFN) are being taken as the parameters in the fuzzy model to deal with vague and incomplete data and α -cut set is used to get a deterministic model from the fuzzy model. Concept of fuzzy goals is implemented to set aspiration level as unity for all objective functions. An attractive flowchart, algorithm and a computational problem are also illustrated to show the validity of the designed approach. [ABSTRACT FROM AUTHOR]

Methods for the development of fuzzy and linguistic models of technological objects, which are characterized by the fuzzy output parameters and linguistic values of the input and output parameters of the object are proposed. The hydrotreating unit of the catalytic reforming unit was investigated and described. On the basis of experimental and statistical data and fuzzy information from experts and using the proposed methods, mathematical models of a hydrotreating reactor and a hydrotreating furnace were developed. To determine the volume of production from the outlet of the reactor and furnace, nonlinear regression models were built, and fuzzy models were developed in the form of fuzzy regression equations to determine the quality indicators of the hydrotreating unit—the hydrogenated product. To identify the structure of the models, the ideas of sequential inclusion regressors are used, and for parametric identification, a modified method of least squares is used, adapted to work in a fuzzy environment. To determine the optimal temperature of the hydrotreating process on the basis of expert information and logical rules of conditional conclusions, rule bases are built. The constructed rule bases for determining the optimal temperature of the hydrotreating process depending on the thermal stability of the feedstock and the pressure in the hydrotreating furnace are implemented using the Fuzzy Logic Toolbox application of the MatLab package. Comparison results of data obtained with the known models, developed models and real, experimental data from the hydrotreating unit of the reforming unit are presented and the effectiveness of the proposed approach to modeling is shown. [ABSTRACT FROM AUTHOR]

The article analyzes foreign experience and concludes that one of the ways to improve the efficiency of aviation security in the Russian Federation is to use modern network training complexes. A new approach to the assessment of the competence of the aviation security screeners was proposed and tested, that allows to take into account the parameters of the oculomotor activity and heart rate variability of the aviation security screeners being tested, different from the existing approaches using fuzzy classification models. The eye-tracking technology and the device of psychophysiological testing UPFT-1/30 "Psychophysiologist" were used as instruments of psychophysiological monitoring. The basics of automatic generation of fuzzy models such as Sugeno and Mamdani from experimental data are presented. Experimental studies were conducted on the basis of the Ulyanovsk Civil Aviation Institute. The results of the comparison of the generated models showed that the Sugeno model trained with the use of ANFIS-algorithm is more accurate than the Mamdani model and the linear regression model identifies the dependence being studied, according to the competence of aviation security screeners. As a criterion of quality of models on training and test data the average square error is used. The actual problem of choosing an effective concept of noise-resistant coding in the telecommunication component of advanced training complexes is substantiated. The ways of solving the important problem of increasing the reliability of actual digital data in network training complexes based on the use of noise-resistant coding are described. A model of permutation decoder of non-binary redundant code based on lexicographic cognitive map is presented. This model of redundant code decoder uses methods of cognitive data processing in the implementation of the procedure of permutation decoding to effectively protect remote control commands from the influence of destructive factors on the control process.

In this paper, the bounded variation property of fuzzy models with smooth compositions have been studied, and they have been compared with the standard fuzzy composition (e.g., min–max). Moreover, the contribution of the bounded variation of the smooth fuzzy model for the noise removal and edge preservation of the digital images has been investigated. Different simulations on the test images have been employed to verify the results. The performance index related to the detected edges of the smooth fuzzy models in the presence of both Gaussian and Impulse (also known as salt-and-pepper noise) noises of different densities has been found to be higher than the standard well-known fuzzy models (e.g., min–max composition), which demonstrates the efficiency of smooth compositions in comparison to the standard composition.

Both academic and nonacademic literature is evolving following the oscillating development of artificial intelligence (AI) and computing power's evolution in their application to finance and financial markets. The limits to economic growth encountered after the financial crisis and, successively, the recent pandemic bursts have posed new challenges to AI‐related technologies. The surveyed publications have given hope in harvesting improved efficiency, new data, information, advisory and management services, risk mitigation, and some unanswered questions regarding negative impacts on sustainable growth and increasing economic welfare. [ABSTRACT FROM AUTHOR]

Fintech has not changed the nature and risk attributes of financial business. Its openness, interoperability, and other characteristics trigger the concealment, infectivity, universality and sudden characteristics of financial risk more obviously, and the potential systemic risk more complexly. This paper adopts fuzzy set analysis to conduct a comprehensive review of Fintech risk. It is found that the technology risk, moral hazard and legal risk, with a weight of up to 80%, are the dominant factors affecting the Fintech risk, while other prominent credit risk, market risk and operational risk in the traditional financial field account for a small proportion, but they still cannot be ignored. The research of this paper enriches the relevant research on the quantification of Fintech risk, and helps to strengthen risk prevention from the aspect of enhancing the safety of Fintech infrastructure, improving the legal system, sound the regulatory framework and strengthening industry self-discipline.

The assertion that uncertainty characterizes the economic processes is already a truism and it is one of their inseparable features. The determinism can only have a coordinate of the didactic process in the field of economic sciences. The deterministic path has been replaced, under the assault of the sciences of complexity, the chaos theory and the fuzzy sets of new approaches, with new openings that make their way and find an increasingly important place in economy. [ABSTRACT FROM AUTHOR]

This study explores the unified stability problem for a class of continuous‐time switched non‐linear systems, in which the non‐linearities are described by the Takagi‐Sugeno (T‐S) fuzzy models. The 'unified' means that the switched system can be arbitrarily composed of either stable or unstable subsystems. Firstly, mode‐dependent average dwell time switching strategies are introduced, and the fast switching strategy is improved by a mode‐dependent bounded maximum average dwell time. Then, the subsystems are reclassified into 'switching‐stable' and 'switching‐unstable' subsystems. The state divergence will be absorbed by the 'switching‐stable' subsystems. Moreover, by constructing a time‐scheduled multiple Lyapunov function, the unified stability conditions are established. Finally, the effectiveness of the achieved results is validated by numerical examples. [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]

This article studies the application of fuzzy logic to the risk analysis of a new software product development and marketing in specific case of a small size IT company. Identification and analysis of external and internal risk factors show that this type of business activity could be evaluated as high-risk enterprise. So, the purpose of the paper is to develop robust method to evaluate probability of occurrence of major risk events and their impact on the company financial health. The fuzzy logic is used to estimate degrees of threat of each relevant risk factor due to lack of reliable statistical data. The novelty of proposed approach is the inclusion into the model the risk event time. [ABSTRACT FROM AUTHOR]

The shipbuilding industry has evolved in recent years. Some quantitative and qualitative factors influence competitiveness of shipyards. The development of shipbuilding processes depends on evolution of manufacturing techniques, planning, organization and external environment. In general, regarding performance, quantitative variables are analyzed through mathematical and linear models. However, the qualitative variables, due to its subjectivity because it deals with human perceptions, it is not common for organizations to work on the performance of these factors. Then, this is the objective of the research, to analyze qualitative factors in shipbuilding in context with the Brazilian industry. The chosen that compose the proposed model are technology and industrial location. This research presents a prioritizing evaluation model of qualitative performance variables. So, presents as original the treatment by the fuzzy sets of qualitative critical factors expressed in linguistic terms by specialists working in the Brazilian sector, resulting in the prioritization of these factors. After result, the model was applied at specific shipyard. The results converge Brazil's real scenario and show that the proposed fuzzy model might be a good qualitative performance factor evaluation tool. Finally, to identify real the distance of a specific shipyard for to this research's model prioritization pattern by brazilian specialists. [ABSTRACT FROM AUTHOR]

The usage of indicators as constituent parts of composite indices is an extended practice in many fields of knowledge. Even if rigorous statistical analyses are implemented, many of the methodologies follow simple arithmetic assumptions to aggregate indicators to build an index. One of the consequences of such assumptions can be the concealment of the influence of some of the composite index's components. We developed a fuzzy method that aggregates indicators using non-linear methods and, in this paper, compare it to a well-known example in the field of risk assessment, called Moncho's equation, which combines physical and social components and uses a linear aggregation method to estimate a level of seismic risk. By comparing the spatial pattern of the risk level obtained from these two methodologies, we were able to evaluate to what extent a fuzzy approach allows a more realistic representation of how social vulnerability levels might shape the seismic risk panorama in an urban environment. We found that, in some cases, this approach can lead to risk level values that are up to 80% greater than those obtained using a linear aggregation method for the same areas. [ABSTRACT FROM AUTHOR]

Nowadays, a huge amount of data are generated, often in very short time intervals and in various formats, by a number of different heterogeneous sources such as social networks and media, mobile devices, internet transactions, networked devices and sensors. These data, identified as Big Data in the literature, are characterized by the popular Vs features, such as Value, Veracity, Variety, Velocity and Volume. In particular, Value focuses on the useful knowledge that may be mined from data. Thus, in the last years, a number of data mining and machine learning algorithms have been proposed to extract knowledge from Big Data. These algorithms have been generally implemented by using ad-hoc programming paradigms, such as MapReduce, on specific distributed computing frameworks, such as Apache Hadoop and Apache Spark. In the context of Big Data, fuzzy models are currently playing a significant role, thanks to their capability of handling vague and imprecise data and their innate characteristic to be interpretable. In this work, we give an overview of the most recent distributed learning algorithms for generating fuzzy classification models for Big Data. In particular, we first show some design and implementation details of these learning algorithms. Thereafter, we compare them in terms of accuracy and interpretability. Finally, we argue about their scalability. [ABSTRACT FROM AUTHOR]