Set Fuzzy Logic Research Articles

Integrating N‐person Intuitionistic Neutrosophic Soft Games with Neutrosophic Cognitive Maps for Cloud Storage with Accounting Information Systems

Neutrosophic logic is founded on non-standard evaluation. In neutrosophic set (NS), the computation of indefiniteness is explicit, while the membership of truth, falsity, and indeterminacy are non-reliable. Recently, various attractive game theory application is extended by entrenching the fuzzy set logic. The accounting data analysis has to contribute the innovativeness. This includes various characteristics. It applies billions of individuals in the business to develop and design novel products until the sale managing of the enormous sales staff. While cloud storage provides flexibility and scalability, it also comes with related costs, involving data transfer fees, subscription fees, and storage costs. Optimizing and managing cloud storage costs associated with business needs and budgetary constraints is crucial. Cloud-based AIS decreases the necessity for localized infrastructure and hardware, oscillating rate from capital to operational expenditure. This enables organizations to pay only for the used resource, resulting in economic efficiencies. Incorporating cloud technology into accounting information systems (AIS) provides several advantages. To increase the capability of accounting data statistics and analysis, this article introduces an N‐person intuitionistic neutrosophic soft game with Neutrosophic Cognitive Maps (NINSG-NCM) for cloud storage with AIS. The NINSG-NCM technique considered a contemporary accounting data analysis is built based on the block bit sequence evaluation technique and extracts the association rule representative amount of accounting data. Integrated with cloud computing (CC) framework, the NINSG-NCM method is proposed, and the NCM clustering technique is used for realizing the modern accounting data clustering, and the NINSG method could enhance the capability of statistical analysis and parallel computing of accounting data. Lastly, salp swarm algorithm (SSA) is applied for hyperparameter tuning method. The experimental outcomes illustrate that the designed intelligent data evaluation technique makes the parallel computing efficacy high and the statistical evaluation capability of accounting data better.

Theme with Variations as Mathematical Representation of Fuzzy Sets

Artistic representations follow, at a closer look, formal and structural archetypes, which often have a physical and mathematical basis, even if the inspiration frequently transcends the scientific dimension of the construction of the musical discourse. Aware or not, the composers personify in their art archetypal structures which are general and embody the ideals of their expression in one way or another. The organization of musical forms constitutes no exception from this phenomenon which has never been investigated in Romanian musicology: the compatibility between physical and mathematical concepts and significant musical realities. Among the great musical genres and forms, the theme and variations are the closest to one of the elements present in the mathematics of the 20th century: the logic of fuzzy sets (or systems).

Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy Logic

Developing models to understand disease dynamics and predict the risk of disease outbreaks to facilitate decision making is an integral component of plant disease management. However, these models have not yet been developed for one of the most damaging diseases in Mediterranean olive-growing areas (verticillium wilt (VW), caused by the fungus Verticillium dahliae Kleb.), although there are parameters (e.g., level of V. dahliae inoculum in the soil, level of susceptibility of the olive cultivar, isothermality, coefficient of variation of seasonal precipitation, etc.) that have previously been correlated with the severity of the disease. Using the data from previous VW studies conducted in the Guadalquivir Valley of Andalusia (one of the most damaged areas worldwide), in this work, a set of fuzzy logic (FL) models is developed with the aforementioned disease and climatic parameters, and the results are compared with machine learning (ML) models, of known effectiveness, to predict the risk levels of VW appearance in an olive orchard. Under these conditions, both groups of models were less effective than those previously studied with simpler models or models used under controlled conditions. However, the accuracy achieved with the most efficient FL model (60%; classification system based on fuzzy rules using the Ishibuchi method with a weighting factor) was somewhat greater than the efficiency achieved with the most efficient ML model (59.0%; decision tree classifier), in addition to being more appropriate (from a practical point of view) for the incorporation into a decision support system by allowing the risk of appearance of each observation to be known by providing rules for each of the combinations of the different parameters with similar precision. Therefore, in this study, we propose the FL methodology as suitable to act as an expert system for the future creation of a decision support system for VW in olives.

An Innovative MPPT Method Based on the Estimated Model of the Photovoltaic Cell considering the Environmental Conditions

In this paper, a novel adaptive photovoltaic maximum power point tracking (MPPT) circuit is proposed, called AT2MPPT. The AT2MPPT circuit is based on a type two fuzzy logic set and utilizes a detailed two-diode model of a solar cell, as well as an innovative hybrid algorithm for designing the MPPT circuit. The effects of environmental conditions such as azimuth, wind speed, wind direction, and irradiation, as well as the uncertainty of radiation intensity, are taken into account in the solar cell model. It is demonstrated that changes in environmental conditions can cause up to a 20% change in the PV parameters. To optimize the AT2MPPT parameters and fuzzy membership functions (MFs), a hybrid lightning search algorithm and whale optimization algorithm (hLSA-WOA) are proposed. The accuracy of the hLSA-WOA is evaluated by comparing its results with those of other optimization algorithms on 10 standard benchmarks. The simulation results indicate that the hLSA-WOA has high accuracy and is nontrapping in local optima. Finally, the proposed AT2MPPT circuit is used to track the MPPT of a PV array in partial shade, and its performance is compared with that of P&O and fuzzy MPPT circuits. The results confirm the superior performance of the AT2MPPT circuit, which produces 1.1% more power than the other two circuits. Additionally, the AT2MPPT circuit has lower power ripple and tracking time compared to perturb, observation, and fuzzy MPPT circuits.

Determination of the Most Appropriate Alternative Dispute Resolution Methods for Airport Projects: A Fuzzy VIKOR Approach

Being prone to dispute, airport projects involve diverse stakeholders including also national and international aviation authorities, and this complicates the dispute resolution process in these projects. Hence, decision-makers should not entirely rely on standard forms of contracts to settle disputes in airport projects. Instead, these projects necessitate a specific dispute resolution framework that meets their needs. Even though some effort has already been devoted to developing a framework for ADR selection, most of these studies neglect airport projects. Furthermore, existing studies fell short of adopting more advanced techniques such as fuzzy set logic and Fuzzy VlseKriterijumska Optimizacija | Kompromisno Resenje (VIKOR), posing a variety of drawbacks. Thus, this study aims to guide decision-makers by developing a selection framework of Alternative Dispute Resolution (ADR) methods to resolve disputes in airport projects. Firstly, the literature was reviewed comprehensively to reveal the criteria affecting the selection of ADR methods. Next, Focus Group Discussion (FGD) session was conducted with the participation of 17 experts who have diverse experiences in the dispute resolution process in airport projects to omit or validate the detected criteria obtained from the literature review. This was then followed by the Fuzzy VIKOR method to evaluate the importance of each ADR method. In the final step, sensitivity analysis was performed to confirm the reliability of the results. The results confirmed that a plethora of factors governs the ADR selection process, pinpointing the need for a systematic and holistic framework. Secondly, the “Expert Determination” was determined to be the most convenient ADR method for airport projects. The “Expert Determination” was followed by the “Dispute Adjudication Board (DAB)” and “Arbitration”, respectively. Revealing the very first holistic framework concerning ADR selection for airport projects, this study significantly contributes to the dispute resolution domain. Furthermore, this proposed framework is believed to have promising benefits for the decision-makers while drafting the contract clauses in terms of ADR methods in airport projects.

Does Corporate Hypocrisy Predict When Firms Will Shirk Remedy Responses For Human Rights Abuses?

Following an overview of literature on corporate human rights abuse and remedy, we employ a qualitative comparative analysis approach based on the logic of fuzzy sets (fsQCA) (Crilly et al., 2012; Fiss, 2007; Ragin, 2008), to explore varying configurations of talk, decisions, activist NGO initiatives, and state involvement that may lead to additional action steps toward remedy for corporate human rights abuses. Our findings show that aspirational talk alone is not sufficient to lead to action, contrary to recent theorizing within organized hypocrisy scholarship (Christensen, et. al., 2020). Without NGO pressure, corporate hypocrisy prevails when it comes to human rights remedy: Firms that engage in human rights talk are no more likely to take action toward remedy than indifferent firms who do not engage with human rights. But with NGO involvement, firms’ human rights talk seems to provide leverage to pressure firms to take consistent action. When it comes to human rights abuse, firms that make empty promises should be prepared to match their talk with action because organized hypocrisy provides a trigger for NGOs to effectively apply pressure for remedy.

Mathematical Methods in Biomedical Studies: The Need for New Approaches to Study of Follicular Thyrocytes

The current stage of medical science development requires updating the methodological and procedural base of research, which leads to the expansion of the mathematical methods' scope for medical purposes, including medical diagnosis. Most frequently, its problems are solved by Bayesian, correlation and regression analysis, phase interval method and the methodology of the logical conclusion (logical programming), which operate on quantitative information and are not designed to use qualitative and binary data. The methodology of the fuzzy-set logic, which permits to transform qualitative information into mathematical dependencies, is not widely used yet in the study of biological objects, as it assumes a rigid dependence of some phenomena on others, which is not typical of living biological systems. This limits the use of mathematical technologies to study the characteristics of changes that occur in cells of the body under the influence of various factors. To effectively solve a number of multidisciplinary medical, medico-social and social problems, namely polyetiologically caused thyroid pathology, it is necessary to develop modern informative approaches to study the activity of the thyroid gland in normal and pathology based on mathematical methods.

Flux Estimator for Salient Pole Synchronous Machines Driven by the Cycloconverter Based on Enhanced Current and Voltage Model of the Machine with Fuzzy Logic Transition

Flux estimation is a key feature of the field-oriented control for the electrically excited synchronous machine which enables the high-performance, high-dynamic drive behavior. In this work, an electrically excited synchronous machine flux estimator based on a current and voltage model is proposed. In this case, the transition between the estimators is done with a fuzzy logic set of rules. The flux estimator based on the current model of the machine in this paper considers the saturation and cross-coupling effect in both axis and it is suitable for applications where a limited amount of the machine data is available. The flux estimator based on the voltage model is specially designed for the drives where high voltage and current ripple is present under normal operating conditions, e.g., like in cycloconverter applications. To exploit all the advantages of both models, a fuzzy logic transition is proposed based on multiple choices which manages the transition between the models based on a speed and torque reference. The proposed flux estimator is experimentally verified on a cycloconverter fed salient-pole electrically excited synchronous machine. The experimental results clearly show that the proposed flux estimator enables the accurate and stable operating conditions for different operating points of the cycloconverter-fed salient-pole electrically excited synchronous machine.

Decision-making support for transportation and logistics combining rough set fuzzy logic algorithm

To lower the operating cost of an enterprise, improving the efficiency of logistics, or reducing the cost of circulation is one of the crucial means for the enterprise to cut the costs. In this paper, combined with the rough set fuzzy logic algorithm, in response to the features of the large-scale vehicle scheduling problem, the data collected by monitoring in real time provided by the tracking technology, in conjunction with the data mining technology, are used to support the decision making based on the reverse logistics mode and establish a vehicle scheduling optimization model with the purpose to support the decision making in transportation logistics.

Improving the ship's power plant automatic control system by using a model-oriented decision support system in order to reduce accident rate under the transitional and dynamic modes of operation

This paper proposes a method to improve the performance of a ship's power plant by reducing accidents within it under transitional operating modes. The method is based on decreasing the number of service personnel errors by using a model-oriented decision support system. In order to implement the proposed method, the structure of the system of automatic control of the ship's power plant has been improved. Such an improvement of the control system implied the integration of a modeling unit and a decision support unit into its structure. The modeling unit makes it possible to predict values of the controlled parameters under a transition mode of operation before they actually appear in the system as a result of the operator's actions. A mathematical model of the automatic control system under transitional operating modes has been built for this unit. In order to implement the decision support unit, a method has been devised to formalize the task of managing the power plant under transitional operating modes. The method essentially involves modeling a transitional operating regime, followed by an evaluation of the results based on regulatory requirements and an empirical criterion for assessing the quality of enabling the diesel generators to work in parallel. In addition, a method has been developed for the decision support unit to reduce the accident rate and improve performance with the help of a mathematical apparatus of fuzzy inference, fuzzy logic, and fuzzy sets. Transitional operating regimes resulting from actual erroneous operator actions during ship flights were investigated. As a result of using the proposed system, the power plant performance increases

Fuzzy Analysis of the Development of Regional Labour Markets of Ukraine

Purpose: We propose to apply the theory of fuzzy logic and fuzzy sets, as well as modern information technologies, as neural networks, to assess the state of the labor market. Design/Methodology/Approach: The article considers the procedure of fuzzy assessment of the state of the regional labor on the basis of the construction of a generalized indicator, the value of which increases with the improvement of the state. In the process of calculations, 16 primary indicators were used, the values of some of which were obtained statistically, and others - by experts. In this paper, the values of the result generalized indicator Level of development of the regional labor market for the regions of Ukraine for 2007-2017 are obtained. The Fuzzy Logic Toolbox module of the MatLab program was used to automate the calculations. Findings: The results show that the global financial and economic crisis of 2008 and the Russian aggression in eastern Ukraine in 2014 affected the state of its regional labor markets. However, the regulatory measures used by the state in these markets were able to significantly offset this impact. Practical implications: The research uses advanced mathematical tools to analyze the labor market. In particular, the logic of fuzzy sets and fuzzy logic were used. Originality/Value: The research can be used for a comparative analysis of selected labor markets. The applied research metrology as a universal method enables the assessment and forecast of local markets.

Maturity and level of knowledge management in the company: an application of Nonaka and Takeuchi model and Fuzzy Logic

Abstract: This paper aims to understand how the interconnection between the tacit and explicit dimensions of knowledge impacts knowledge management and its degree of maturity. Knowledge management maturity is understood as the ability firms are expected to have in order to exercise (to some degree) the skills required to manage knowledge internally. Nonaka and Takeuchi (N&T) model of organizational knowledge management was applied to a sample of companies (segmented by size) from the states of São Paulo, Minas Gerais, and Rio de Janeiro, by using the logic of fuzzy sets. The results have shown that small and medium companies have lower levels of knowledge management maturity when compared to large companies. However, in large companies, knowledge management is at medium levels. In this context, this paper suggests, as management guidelines, that companies measure their professionals' perception regarding the company level of knowledge management, pursuing to identify which of their components should be developed.

ОЦІНЮВАННЯ ТЕПЛОІЗОЛЯЦІЙНИХ ПОКАЗНИКІВ ОГОРОДЖУВАЛЬНИХ КОНСТРУКЦІЙ БУДІВЕЛЬ ЛІНГВІСТИЧНИМИ ЗМІННИМИ

The classification of the factors influencing decision-making at an estimation of thermal conductivity of a heat-insulating cover of the building is offered. The relationship between the parameters of the thermal state of the thermal insulation of buildings and its energy efficiency is presented in the form of a logical conclusion tree. The logical inference tree at the system level characterizes the ratio of parameters that affect thermal conductivity. To create a mathematical model for expert assessment of the technical condition of the thermal insulation of the building, it is advisable to use the apparatus of fuzzy logic and fuzzy set theory. The formalization of quantitative and qualitative factors influencing the thermal efficiency of the building insulation shell has been carried out. When assessing the technical condition of the thermal insulation of the building (energy efficiency), quantitative and qualitative factors of influence are expressed through linguistic variables. Estimation of levels of linguistic variables is represented by term sets. Thermal conductivity as a factor in the thermal insulation of a building is related to the normative classification of energy efficiency of buildings in force in Ukraine.

A Methodological Approach to Identifying the Optimal Market Behavior Strategy Based on Fuzzy Game Modeling

The presented study models market behavior strategies for firms in real (“impure”) markets.Aim. The presented study aims to develop tools for optimizing (according to the criterion of profit maximization) the market behavior of firms based on the achievements of modern mathematics.Tasks. The authors describe theoretical approaches to modeling the behavior of firms; define the problem of modeling the market behavior of firms in the language of game theory; modify the game model by implementing elements of the theory of fuzzy logic and fuzzy sets; develop and test a methodological approach to identifying the optimal behavior of firms based on fuzzy game modeling.Methods. This study uses general methods of economic and mathematical modeling, provisions of neoclassical and institutional firm theory, tools of game theory and the theory of fuzzy logic and fuzzy sets.Results. The optimal market behavior strategy for firms selling the same product is determined. The implementation of this strategy focuses on maximizing profits with allowance for the imperfections of real markets. A rigorous solution to this problem is proposed, based on the provisions of game theory, theory of fuzzy sets and fuzzy logic. The developed methodological approach to identifying the optimal market behavior strategy based on fuzzy game modeling is illustrated by a meaningful example.Conclusions. The developed and tested methodological approach to identifying the optimal market behavior strategy based on fuzzy game modeling described in the article allows firms to search for optimal strategies with allowance for the imperfections of real markets. It can be used for theoretical modeling of the behavior of firms in an “impure” market, including in a mixed economy, where the government has a certain degree of planning and administrative influence on economic processes. The proposed approach can be recommended for use by the management of firms in the development and implementation of competitive strategies.

The role of universities in the realization of opportunities and curbing threats to innovative economic development – expert opinions

Purpose. Defining the role of universities in realizing opportunities and containing threats to the development of an innovative economy related to training and scientific and practical cooperation. Identification of peculiarities of views of different institutional groups of society on the characteristics of innovation economy.Methodology. The study was based on an expert method. In total, 106 experts representing four institutional groups took part in the assessment of opportunities and threats: representatives of banks, business of the real sector of the economy, authorities, faculty of universities. The evaluation is carried out on 18 possibilities formulated by experts and 19 threats from which positions are selected, which are influenced by the state of personnel provision and interaction of universities with organizations of the real sector of the economy. A fuzzy logic set was used to process the results.Results. As part of the development opportunities of the innovative economy, two are directly related to the training of qualified personnel and seven – to the scientific and practical cooperation of universities, research institutes, enterprises of the real sector of the economy. In the composition of 19 threats – four are caused by insufficient qualification, weak state support of specialists. The positions of experts within the groups are in most cases sufficiently harmonized. There are significant differences between the estimates of different groups. All expert groups assessed threats higher than opportunities, which couldbe considered a pessimistic attitude to the prospects for the development of an innovative economy. Among opportunities as the most significant are recognized: the state support of talented scientists and experts in the field of scientific, scientific and technological and innovative activity; State support for small innovative enterprises. Threats include low budget funding for academic science and higher education; Lack of the necessary legal framework to protect intellectual property owners; Lagging behind in the training of personnel with competencies adequate to the requirements of an innovative economy; «Brain drain» of current and potential developers of technological and product innovations.

Sustainability: Analysis Using Mathematics of Uncertainty

All member states of the United Nations adopted Agenda 2030 and the Sustainable Development Goals (SDGs) in 2015. The 17 SDGs describe a universal agenda that applies to and must be implemented by all countries. We take the metrics and data provided in the SDG Index and Dashboards Reports and the Report of a Study by Stakeholder Forum and transform them into a fuzzy logic setting. This allows for the analysis of the results in these reports by using techniques of mathematics of uncertainty. We focus on countries making up the Organization for Economic Cooperation and Development (OECD). We provide a ranking of the countries in the OECD as to their achievement of the SDGs.

A fuzzy inference system for predicting pavement surface damage due to combined action of traffic loading and water

ABSTRACT This paper presents a fuzzy logic-based deterioration prediction models for gap and open-graded asphalt surfaces when both dynamic loading and shallow flooding coincide. The impact of aggregate size, load frequency, compaction levels, and environmental conditions was evaluated in a controlled laboratory testing to measure cracking and rutting performance of each mixture. A set of fuzzy logic was developed using the experimental data and then tested against randomly selected samples. The predicted cracking and rutting showed excellent agreements (95% correlation) with the experimentally measured values. The validation and sensitivity analysis showed that irrespective of aggregate gradation, mixture parameters (aggregate size, void contents), traffic parameters (loading frequency) and environmental factors (wet and dry condition) have a significant impact on model performance. Overall, the Fuzzy-based prediction model showed the potential to differentiate the performance of different asphalt surfaces and can be further developed to use in practical applications.

Fuzzy Logic Applied to Sustainable Development Goals and Human Trafficking

In 2015, the leaders of all the UN’s Member States agreed to the 2030 Agenda for Sustainable Development. The 17 Sustainable Development Goals and their 169 associated targets address five areas of critical importance: people, planet, prosperity, peace, and partnership. The purpose of this paper is to take the metrics and data provided and transform them into a fuzzy logic setting. This allows for the analysis of the results in SDG Index and Dashboards Report 2019 by using the techniques of fuzzy logic. Many of these 17 Sustainable Development Goals are related to the terrible crime of human trafficking. We also examine these goals in a fuzzy logic setting.

Aggregation of the fuzzy logic sets in terms of the functions of the triangular norm and triangular co-norm

The purpose of this article is to explore the group of the operators, which can be used for aggregation of the fuzzy sets. There were scrutinized operators to be used for the intersection and union such as the triangular norm and triangular co-norm in the article. All of those operators are defined as the binary operations, where there was indicated that the norms and co-norms are two-valued functions of [0, 1]. Furthermore, there were discussed properties of norm and co-norm functions such as symmetry, associativity, neutral entry and null entry properties, monotonicity properties. Moreover, the consideration of the properties of norm and co-norm functions have routed investigation of the norm and co-norm operators to go further to prove theorem, which converts the union into intersection and vice versa in terms to all possible norm and co-norm functions based on the fuzzy logic sets.

Fashion designer selection with the method of GRA-based intuitionistic fuzzy multi-criteria decision making

Personnel selection is a decision making problem based on the determination of the most suitable individual in line with the determined criteria, as known. Particularly in the labor-intensive sectors, such as apparel, the choice of the right personnel is a crucial problem and plays a key role in continuing the life cycle of companies successfully. In this study, the fashion designer selection process is examined for the design departments which are one of the most important organs for the apparel companies. For this purpose, the most suitable designer was chosen for an apparel company operating in Izmir province. In order to fulfill the aim, criteria weights which play an active role in designer selection are obtained through AHP method, and intuitive fuzzy logic sets are utilized to enable the evaluations of the candidates by the experts according to the criteria. In the light of these data, the final choice was conducted with the method of grey relational analysis.