Narrow Results

The assessment of a firm’s economic health is critical and is the cornerstone of sound corporate control and informed investment choices. This paper ambitions to cope with the restrictions of conventional monetary evaluation strategies, which frequently hostilities to seize the dynamic and complicated nature of economic statistics. Our modern approach combines the entropy weight approach with fuzzy common sense to provide a greater nuanced and complete assessment of corporate economic situations. The entropy weight method efficiently quantifies the significance of numerous monetary signs, accordingly decreasing the subjectivity usually encountered in conventional evaluations. however, its reliance on quantitative records can occasionally oversimplify complex monetary states. Fuzzy logic, on the other hand, lets in for a qualitative interpretation of economic indicators, supplying a greater rounded perspective. with the aid of integrating these two methodologies, our method leverages the entropy weight technique to envision the significance of every economic indicator, even as fuzzy logic gives a cultured assessment of these indicators. Experimental effects confirm the superiority of this hybrid method, showcasing its potential to yield greater accurate and robust evaluations of company monetary fitness as compared to traditional methods. This provides the field of economic evaluation with an advanced but practical analytical tool specifically designed to cope with the complexity of monetary facts and to provide insightful and actionable checks.

With the advancement of digital publishing, digital copyright trading is poised to become the predominant mode of copyright transactions in the future. However, the growth of the publishing industry is currently impeded by several challenges associated with digital copyright trading. These challenges include a complex evaluation process for determining copyright value, difficulties in protecting copyrights, obstacles in gathering evidence for infringement cases, and intricate management processes involved in trading. To address these issues within the existing traditional copyright valuation framework, this paper proposes and designs a digital copyright valuation system grounded in fuzzy logic. This system aims to evaluate digital copyright value comprehensively and intelligently by analyzing its key features through a fuzzy logic inference model. The objective is to provide valuable insights that can facilitate the development of digital copyright trading practices.

Sustainable supplier management literature is mainly on sustainable supplier selection but sustainable supplier performance monitoring & evaluation studies are scarce. Furthermore, the studies do not differentiate the sustainability evaluation criteria between the supplier selection and monitoring & evaluation stages. To bridge this gap, this study aimed to monitor & evaluate sustainability performance of the suppliers of a focal manufacturing company in automotive sector with the TBL approach. Toward that end, we questioned and tried to identify first the sustainable supplier selection and monitoring & evaluation differentiating criteria, and then, how we can rank and rate the sustainable suppliers for developmental purposes in the monitoring & evaluation phase. 21 criteria were determined for sustainable supplier performance in monitoring & evaluation by a detailed literature review and taking the opinions of the focal company. Then, decision makers assessed sustainability dimensions and determined criteria related to these dimensions. The weights of dimensions and criteria were calculated by Interval-Valued Intuitionistic Fuzzy AHP method. Then, sustainability performances of selected suppliers from the focal company’s portfolio were ranked by using Fuzzy EDAS, Fuzzy CODAS and Fuzzy MOORA methods. The results show that sustainable supplier monitoring & evaluation criteria involve a mix of external criteria (rules and regulations) and internal criteria (suppliers’ values). This finding helps us understand how we can have a more relaxed criteria set involving basic external criteria while selecting suppliers to have access to a more innovative and diverse supplier space and then have more challenging internal and external criteria to monitor & evaluate those suppliers toward true sustainability.

Since 2013, the Spanish fleet of EF-18 fighters has not received major updates nor any other jet has been proposed for their replacement. Thereby, a decision problem of interest to the Spanish Air Force is addressed in this study. In this regard, a collection of six potential alternatives (Eurofighter Typhoon, F-35 Lightning II, F/A-18 Super Hornet, Dassault Rafale, F-15 EX, and Saab 39 Gripen) and 14 criteria (3 of which being qualitative) are evaluated based on expert consensus by a combination of fuzzy logic and multi-criteria decision making (MCDM) approaches (AHP-VIKOR/AHP-TOPSIS). The weights of the criteria and the valuation of the qualitative ones (via questionnaires) were made possible by the participation of two independent groups of experts, all of them fighter pilots. Once the extraction of knowledge was carried out via a fuzzy version of a modified AHP, it was found that the two most important criteria (“air superiority over neighbor countries” and “tactical capability”) were qualitative. A comparison between the rankings of the alternatives provided by fuzzy VIKOR and fuzzy TOPSIS approaches highlights that the fuzzy VIKOR ranking appears highly influenced by the most important criteria. On the contrary, fuzzy TOPSIS presents a more compensatory behavior than fuzzy VIKOR. Given the differences observed when comparing such rankings of alternatives, a triple sensitivity analysis was conducted to ensure robustness. Our results highlight the Dassault Rafale as the most promising option for replacement. This finding suggests its potential as a benchmark for future fighter selection and next-generation fighter planning.

This research reports the development of anti-reflective films for solar cell application by employing the hot embossing technique with laser-patterned microstructures. The goal is to increase the light-trapping ability of crystalline silicon (c-Si) wafers by employing micro-textured polycarbonate films to decrease surface reflectance. A series of micron-sized rhombus patterns were first created on the titanium-grade-5 mold using a fiber laser, and then, polycarbonate sheets were hot embossed under the optimized conditions. In order to investigate the influence of the embossing temperature, pressure, and time on the average reflectance and surface roughness of the films, a parametric analysis was carried out through the Taguchi method. The most effective embossing parameters were the embossing temperature of $220^{\circ }$C, pressure of 50 kg/cm², and an 8 min embossing duration, which resulted in a significant decrease of 41.53% reflectivity. The findings in the existing study and a fuzzy logic-based multi-objective optimization approach also supported these findings, suggesting the scalability and efficiency of the process. It is evident that the proposed method could provide a more significant cost reduction in fabricating anti-reflective films with large-area applications to optoelectronics devices such as solar cells, LEDs, and optical sensors. This study opens the door to further studies about using micro-patterned films to enhance light management for other energy-efficient devices.

In this work, our interest is restricted to study the links among $L$-fuzzy pre-proximity spaces and $L$-fuzzy ideals. We also show that there is a Galois correspondence between the categories of stratified $L$-fuzzy ideals and $L$-fuzzy pre-proximity spaces. Finally, we establish the relationship between $L$-fuzzy prime ideal spaces and $L$-fuzzy co-topological spaces.

The development index (DI) of a region is defined by various elements such as geography, population density, gross domestic product (GDP), GDP per capita, natural resources, markets, economic development, and other relevant factors. The traditional approach to classification usually depends on the country’s total land area measured in square kilometers. Nevertheless, the transition points between different levels of development size (small, medium, and giant) are highly subjective, resulting in frequent inconsistencies and conflicts. Fuzzy logic, a superset of conventional (Boolean) logic, extends the framework to include partial truth values, ranging from “fully true” to “totally false”. This development is especially essential since human reasoning, particularly commonsense reasoning, is approximate rather than precise. This study offers a model that uses fuzzy logic and the Mamdani fuzzy inference system (MFIS) to evaluate nine main cities in Pakistan based on their populations (POPs), gross domestic products (GDPs), and literacy rates (LR). The model includes variables for the antecedents (inputs) and the consequences (outputs). The input variables are the population (POP), GDP, and LR, while the output variable is the DI. The MFIS is used with Python programming tools, including the scikit-fuzzy library for inference and aggregation and matplotlib for graphics.

This work reviews a number of algorithms to solve the location management problem in mobile networks for both static and dynamic scenarios. In the static mode, results of five algorithms are used to highlight the pros and cons for each algorithm. These results provide new insight into the mobility management problem that can influence the design of future wireless networks. In the dynamic mode in which mobile users' past movement patterns are used in making future paging decisions by the network, the performance of an online location management algorithm is examined under different deployment setups. Performance results of this algorithm show its advantages over the currently implemented and/or proposed static location management systems (including GSM).

We introduce and investigate a weighted propositional configuration logic over De Morgan algebras. This logic is able to describe software architectures with quantitative features especially the uncertainty of the interactions that occur in the architecture. We deal with the equivalence problem of formulas in our logic by showing that every formula can be written in a specific form. Surprisingly, there are formulas which are equivalent only over specific De Morgan algebras. We provide examples of formulas in our logic which describe well-known software architectures equipped with quantitative features such as the uncertainty and reliability of their interactions.

This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved GA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved GA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA.

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study — namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52–54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.

In this paper we analyze the identification problem which consists of choosing an appropriate identification model and adjusting its parameters according to some adaptive law, such that the response of the model to an input signal (or a class of input signals), approximates the response of the real system for the same input. For identification models we use fuzzy-recurrent high order neural networks. High order networks are expansions of the first-order Hopfield and Cohen-Grossberg models that allow higher order interactions between neurons. The underlying fuzzy model is of Mamdani type assuming a standard defuzzification procedure such as the weighted average. Learning laws are proposed which ensure that the identification error converges to zero exponentially fast or to a residual set when a modeling error is applied. There are two core ideas in the proposed method: (1) Several high order neural networks are specialized to work around fuzzy centers, separating in this way the system into neuro-fuzzy subsystems, and (2) the use of a novel method called switching parameter hopping against the commonly used projection in order to restrict the weights and avoid drifting to infinity.

Fuzzy logic represents an extension of classical logic, giving modes of approximate reasoning in an environment of uncertainty and imprecision. Fuzzy inference systems incorporates human knowledge into their knowledge base on the conclusions of the fuzzy rules, which are affected by subjective decisions. In this paper we show how the reinforcement learning technique can be used to tune the conclusion part of a fuzzy inference system. The fuzzy reinforcement learning technique is illustrated using two examples: the cart centering problem and the autonomous navigation problem.

This paper developed the dynamics of opinion network where a node interacts with only one node in each step and these nodes will not exchange their opinions until the difference of their opinions is below a tolerance threshold. Every node is a Gaussian fuzzy set with the center representing an opinion itself and a standard deviation characterizing an uncertainty about the opinion. The fuzzy opinion network with different uncertainties’ levels of nodes was investigated to show how opinions and their uncertainties propagate and evolve for reaching a consensus in the network. The theoretical and numerical analyses were used to assess the conditions where a consensus can be reached in the fuzzy opinion network.

In general, fuzzy modeling requires two stages: structure identification (generating the fuzzy rule base) and parameter learning (optimizing parameters in fuzzy rules). Here, we present an on-line algorithm for competitive learning and optimization of fuzzy models. Differing from existing methods, in this approach the structure identification and parameter optimization of the fuzzy model can be carried out automatically, using on-line acquisition of data. We demonstrate this approach by applying it to different types of nonlinear system modeling.

In this paper, we describe our system for writer independent, off-line unconstrained handwritten word recognition. We have developed a new method to automatically determine the parameters of Gabor filters to extract features from slant and tilt corrected images. An algorithm is also developed to translate 2D images to 1D domain. Finally, we propose a modified dynamic programming method with fuzzy theory to recognize words. Our initial experiments have shown promising results.

This paper proposes a general formalism for representation, inference and learning with general hybrid Bayesian networks in which continuous and discrete variables may appear anywhere in a directed acyclic graph. The formalism fuzzifies a hybrid Bayesian network into two alternative forms: the first form replaces each continuous variable in the given directed acyclic graph (DAG) by a partner discrete variable and adds a directed link from the partner discrete variable to the continuous one. The mapping between two variables is not crisp quantization but is approximated (fuzzified) by a conditional Gaussian (CG) distribution. The CG model is equivalent to a fuzzy set but no fuzzy logic formalism is employed. The conditional distribution of a discrete variable given its discrete parents is still assumed to be multinomial as in discrete Bayesian networks. The second form only replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adds a directed link from that partner discrete variable to the continuous one. The dependence between the partner discrete variable and the original continuous variable is approximated by a CG distribution, but the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution. Obviously, the second form is a finer approximation, but restricted to CGR models, and requires more complicated inference and learning algorithms. This results in two general approximate representations of a general hybrid Bayesian networks, which are called here the fuzzy Bayesian network (FBN) form-I and form-II. For the two forms of FBN, general exact inference algorithms exists, which are extensions of the junction tree inference algorithm for discrete Bayesian networks. Learning fuzzy Bayesian networks from data is different from learning purely discrete Bayesian networks because not only all the newly converted discrete variables are latent in the data, but also the number of discrete states for each of these variables and the CG or CGR distribution of each continuous variable given its partner discrete parents or both continuous and discrete parents have to be determined.

In this paper a novel Fuzzy Rule Based Dissimilarity Function is presented, to determine the hierarchical merging sequence in a region based segmentation scheme. The proposed technique, based on distinct region features and fuzzy logic principles, is designed to cope with the problems inherent in the segmentation task that the traditional merging cost functions cannot overcome. It combines the global (color) and local (spatial) information of the image to compare two adjacent regions in the rgb space. The validity of the approach has been subjectively and objectively verified for several types of color images such as head and shoulders, natural and texture images.

This paper proposes a new neuro-fuzzy technique suitable for binarization and gray-scale reduction of digital documents. The proposed approach uses both the image gray-scales and additional local spatial features. Both, gray-scales and local feature values feed a Kohonen Self-Organized Feature Map (SOFM) neural network classifier. After training, the neurons of the output competition layer of the SOFM define two bilevel classes. Using the content of these classes, fuzzy membership functions are obtained that are next used by the fuzzy C-means (FCM) algorithm in order to reduce the character-blurring problem. The method is suitable for improving blurring and badly illuminated documents and can be easily modified to accommodate any type of spatial characteristics.

A fuzzy-logic approach to the classification of multitemporal, multisensor remote-sensing images is proposed. The approach is based on a fuzzy fusion of three basic sources of information: spectral, spatial and temporal contextual information sources. It aims at improving the accuracy over that of single-time noncontextual classification. Single-time class posterior probabilities, which are used to represent spectral information, are estimated by Multilayer Perceptron neural networks trained for each single-time image, thus making the approach applicable to multisensor data. Both the spatial and temporal kinds of contextual information are derived from the single-time classification maps obtained by the neural networks. The expert's knowledge of possible transitions between classes at two different times is exploited to extract temporal contextual information. The three kinds of information are then fuzzified in order to apply a fuzzy reasoning rule for their fusion. Fuzzy reasoning is based on the "MAX" fuzzy operator and on information about class prior probabilities. Finally, the class with the largest fuzzy output value is selected for each pixel in order to provide the final classification map. Experimental results on a multitemporal data set consisting of two multisensor (Landsat TM and ERS-1 SAR) images are reported. The accuracy of the proposed fuzzy spatio-temporal contextual classifier is compared with those obtained by the Multilayer Perceptron neural networks and a reference classification approach based on Markov Random Fields (MRFs). Results show the benefit of adding spatio-temporal contextual information to the classification scheme, and suggest that the proposed approach represents an interesting alternative to the MRF-based approach, in particular, in terms of simplicity.