Fuzzy Framework Research Articles

Cost Effective Indoor HVAC Energy Efficiency Monitoring based on Intelligent Decision Support System under Fermatean Fuzzy Framework

The heating, ventilation, and air conditioning (HVAC) control system is responsible for the efficient building energy system. Indoor energy consumption patterns can be monitored and reduced intelligently. Occupancy information plays a vital role to save a reasonable amount of energy. Traditional energy monitoring and control systems can be improved with the installation of the occupancy monitoring system which will consist of a network of sensors and cameras. In this research work, we propose a new and revolutionary convolutional neural network (CNN) based on real-time camera occupancy detection and recognition techniques across different sorts of sensors that provide realistic low-cost energy-saving solutions with robust graphical processing units (GPUs). This occupancy information will decide the energy behaviour inside buildings. Decision-making tools can be used to select the appropriate occupancy detection and recognition alternative for indoor environment and energy monitoring and management. In this research work, we introduce and develop the "Fermatean fuzzy prioritized weighted average and geometric operator". These aggregation operators (AOs) are a modern approach to modelling complexities in decision-making. In the end, we give an algorithm for an intelligent decision support system (IDSS) using proposed AOs to compare our CNN based method with other existing sensors techniques.

Graph Ontology Based IAFCM Model for Itemset Mining in Transactional Database: An Optimized Fuzzy Framework

Graph Ontology Based IAFCM Model for Itemset Mining in Transactional Database: An Optimized Fuzzy Framework

An intuitionistic fuzzy bireduct model and its application to cancer treatment

Due to technological advancement, data size has seen a significant increase both in terms of features and instances. An efficient way to handle large sized datasets is to apply data reduction technique to ease the computational intractability of learning algorithms. Intuitionistic fuzzy rough and fuzzy rough sets based approaches provide a handful of solution, but the applicability is mostly restricted to reduction in terms of feature size. However, reduction in terms of instance size has not been handled by any of the existing intuionistic fuzzy set approaches. The present paper introduces the notion of bireducts in intuitionistic fuzzy framework that can be used for simultaneous reduction of instances and features. A robust lower approximation formulation is employed along with laying the foundation for the variants of instance selection technique. The proposed model is therefore robust to noise and can very effectively handle uncertainities due to judgement as well as identification. Further, an efficient instance selection technique in bireduct formulation enhances the performance. The experimental evaluation on benchmark datasets demonstrates the applicability and robustness of the proposed bireducts. It significantly reduces data size both in terms of instances and features whilst maintaining high performances. Further, the model is applied in the challenging domain of cancer treatment by enhancing the prediction performance of anti-angiogenic peptides. A comparative analysis demonstrates the superiority of the proposed methodology.

Fuzzy control decision-making framework adapted to the uncertainty environment of complex software system

As the system becomes more intelligent and embedded, the operating environment is gradually changed from closed, static, and controllable to more open, dynamic, and difficult to control. As a result, the design of complex software systems is faced with many challenges arising from the uncertainty of the environment (UoE). On the one hand, ignoring the UoE to manually describe requirements is not only a tough job, but it can also hinder the discovery of potential requirements; on the other hand, it is a challenge to integrate the representation of and reasoning of UoE into the process of modeling complex systems. Based on the analysis of the characteristics of complex systems engineering, this paper takes solving the UoE caused by stakeholders preferences and complex environment context as the entry point and designs a fuzzy control decision-making framework. Specifically, the framework contributes to the spiral of complex systems while solving the UoE by constructing a closed-loop intelligent system based on automatic data flow between information space and physical space for environment sensing, uncertainty analysis, requirements mining, fuzzy reasoning, decision execution as well as feedback optimization. Finally, the framework is validated with a concrete example of an adaptive treadmill system based on the support tools developed.

Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm

The significance and purpose of this multi-objective Combined Heat and Power Economic Emission Dispatch (MO-CHPEED) problem aims to determine the optimal generator output of the co-generation systems, in which two conflicting objectives of the fuel cost and mass of emissions are to be simultaneously minimized. The nonlinear and nonconvex nature of the objective functions needs a good optimization technique to handle it. This paper proposes a Dynamically Controlled Whale Optimization Algorithm (DCWOA) to solve the multi-objective non-convex MO-CHPEED problem in fuzzy environment. The proposed DCWOA is an improved variant of the traditional WOA method by adding dynamically controlled constriction function. Both the conflicting objectives of fuel cost and mass of emissions are handled using Fuzzy Framework. To highlight the performance of the proposed technique, it is tested on the latest CEC test functions and three different MO-CHPEED case studies. The results obtained by proposed DCWOA after 100 independent trails on latest CEC test functions and compared with latest different published methods show the effectiveness and robustness of the proposed method for getting better average and STD values. Moreover, proposed DCWOA is also tested on different dimensioned MO-CHPEED test functions after 100 independent trails and compared with latest techniques. Again the most compromise results given by proposed DCWOA highlights the supremacy of the proposed method in terms of the getting better fitness and best compromise solution obtained and the convergence traits of the MO-CHPEED problem.

Prioritizing Barriers and Strategies Mapping in Business Intelligence Projects Using Fuzzy AHP TOPSIS Framework in Developing Country

Business Intelligence (BI) is an essential technology in an increasingly competitive landscape since it helps make decisions more accurately. To achieve an effective BI implementation, the organization must formulate the right strategy to overcome its challenges. This research aimed to develop a framework to map barriers into strategies using qualitative and quantitative methods. The qualitative approach is driven by interviewing BI experts to validate the barriers and strategies previously obtained. Based on the interview, there are 19 barriers and 9 strategies that could be used. The quantitative approach compiles a priority list of the most significant barriers and the most effective strategies to overcome these barriers using fuzzy AHP TOPSIS, an MCDM method to eliminate inconsistencies during ranking. The results indicate that the lack of collaboration between the IT and BI departments, the BI implementation demands to be done quickly, and low data quality are the main barriers that hinder BI's success. This research also found that business people's involvement in a BI project is the best strategy to overcome the obstacles. The chances of a successful BI implementation will increase by having good cooperation between IT and business units within the company. Doi: 10.28991/ESJ-2022-06-02-010 Full Text: PDF

Multicriteria decision-making based on distance measures and knowledge measures of Fermatean fuzzy sets.

Fermatean fuzzy sets are more powerful than fuzzy sets, intuitionistic fuzzy sets, and Pythagorean fuzzy sets in handling various problems involving uncertainty. The distance measures in the fuzzy and non-standard fuzzy frameworks have got their applicability in various areas such as pattern analysis, clustering, medical diagnosis, etc. Also, the fuzzy and non-standard fuzzy knowledge measures have played a vital role in computing the criteria weights in the multicriteria decision-making problems. As there is no study concerning the distance and knowledge measures of Fermatean fuzzy sets, so in this paper, we propose some novel distance measures for Fermatean fuzzy sets using t-conorms. We also discuss their various desirable properties. With the help of suggested distance measures, we introduce some knowledge measures for Fermatean fuzzy sets. Through numerical comparison and linguistic hedges, we establish the effectiveness of the suggested distance measures and knowledge measures, respectively, over the existing measures in the Pythagorean/Fermatean fuzzy setting. At last, we demonstrate the application of the suggested measures in pattern analyis and multicriteria decision-making.

A new outranking method for multicriteria decision making with complex Pythagorean fuzzy information

This article contributes to the advancement and evolution of outranking decision-making methodologies, with a novel essay on the ELimination and Choice Translating REality (ELECTRE) family of methods. Its primary target is to unfold the constituents and expound the implementation of the ELECTRE II method for group decision making in complex Pythagorean fuzzy framework. This results in the complex Pythagorean fuzzy ELECTRE II method. By inception, it is intrinsically superior to models using one-dimensional data. It is designed to perform the pairwise comparisons of the alternatives using the core notions of concordance, discordance and indifferent sets, which is then followed by the construction of complex Pythagorean fuzzy concordance and discordance matrices. Further, the strong and weak outranking relations are developed by the comparison of concordance and discordance indices with the concordance and discordance levels. Later, the forward, reverse and average rankings of the alternatives are computed by the dint of strong and weak outranking graphs. This methodology is supported by a case study for the selection of wastewater treatment process, and by a numerical example for the selection of the best cloud solution for a big data project. Its consistency is confirmed by an effectiveness test and comparison analysis with the Pythagorean fuzzy ELECTRE II and complex Pythagorean fuzzy ELECTRE I methods.

Sustainable supplier evaluation and selection in developing countries: an integrated fuzzy framework

Evaluation and selection of suppliers play a vital role in improving their sustainability performance. In this regard, evaluating the sustainability performance of suppliers is much more critical in developing countries. These countries are among the best destinations for supplying raw materials and workforce utilisation because of lower costs. In recent years, various multi-criteria decision aiding (MCDA) frameworks utilised to evaluate suppliers based on sustainable aspects. However, few studies analysed the evaluation and selection of suppliers' sustainability in an integrated fuzzy MCDA model regarding the supplier selection process and developing nations. This research presents a comprehensive, integrated fuzzy decision model for sustainable supplier evaluation and selection using an integrated FDelphi-FDEMATEL-FANP-FVIKOR by considering sustainability (social, economic and environmental). Also, this research provides a comprehensive analysis of MCDA techniques' literature in terms of the main steps of sustainable supplier selection (e.g., formulation of criteria, handling of criterion's interaction, weight calculation and final selection). The previous research rarely considered the evolution of suppliers' sustainability according to its steps in the literature. This framework assists supply chain managers and practitioners better understand the extraordinary role of these steps on businesses. This research's proposed framework is validated by a numerical example taken from Iran's automotive industry.

Sustainable Supplier Evaluation and Selection in Developing Countries; An Integrated Fuzzy Framework

Sustainable Supplier Evaluation and Selection in Developing Countries; An Integrated Fuzzy Framework

An efficient algorithm of fuzzy reinstatement labelling

<abstract><p>The fuzzy reinstatement labelling ($ FRL $) puts forward a reasonable method to rewind the acceptable degrees of arguments in fuzzy argumentation frameworks. The fuzzy labelling algorithm ($ FLAlg $) computes the $ FRL $ by infinitely approximating the limits of an iteration sequence. However, the $ FLAlg $ is unable to provide an exact $ FRL $, and its computation complexity depends on not only the number of arguments but also the accuracy. This brings a quick increase in complexity when higher accuracy is acquired. In this paper, through the in-depth study of the $ FLAlg $, we introduce an effective algorithm for decomposing $ FRL $ by strongly connected components. For simple fuzzy frameworks in the form of trees, odd cycles, and even cycles, the new algorithm provides an exact value of the limit. Therefore, by avoiding the infinite approximation process, it is independent of accuracy. And for complex frames, the new algorithm outputs an approximate value to the $ FLAlg $. It is more efficient because the number of arguments in the approximation process is usually reduced.</p></abstract>

Fuzzy Machine Learning Model in Real-World Physical Domains; A State-of-the-Art Approach

Day after day, individuals all across the world come up with fresh ideas for improving the future. Several intriguing discoveries and ideas paved the way for a new age of electronics, telecommunications, business, and medicinal innovation. Using less resources, greater changes in these domains can be achieved. As improving efficiency and productivity allow exponential development in some areas of the global economy, artificial intelligence (AI) and machine learning (ML) is being adopted by a growing number of individuals, corporations, and governments. Since real-world scenarios influence imprecise and unpredictable situations, fuzzy systems have become an inescapable machine learning aspect. Thus, this research presents a qualitative analysis of the significance of fuzzy machine learning systems like fuzzy support vector machines (FSVM) in various physical domains. Based on this analysis, this research extends with the proposal of a fuzzy machine learning-based framework for two different physical domains: (1) intelligent transportation and (2) ecological risk handling. Thereby, this state-of-the-art approach presents fault detection using FSVM (FD-FSVM) model in the intelligent transportation domain. In ecological risk handling, this study proposes an improved FSVM for risk level classification (FSVM-RLC) approach, which uses the persistent organic pollutants data for training and validation. These two domains are chosen randomly to evaluate the classification performance of the fuzzy machine learning algorithms based on their mean absolute error, accuracy, precision, recall, and F1 score. Apart from this, the mean square error and mean absolute error are measured. Compared to existing machine learning models, the individual results of these two approaches show the highest performance. Furthermore, this fuzzy integrated machine learning technique kept consistency in both domains by giving 98.2% and 97.89% accuracy levels for FD-FSVM and FSVM-RLC, respectively.

Extended Fully Fuzzy Linear Regression to Analyze a Solid Cantilever Beam Moment

There are several procedures such as possibilistic and least-square methods to estimate regression models. In this study, first, a fully fuzzy regression equation is converted into a fully fuzzy linear framework. By considering a least-square approach, a model is suggested based on matrix equations for solving fully fuzzy regression models. The main advantage of this method over existing ones is that this method considered values based on their specification, and all linear problems can be easily solved. Moreover, a case study for solid mechanics about the quantity of beam momentum is considered. In this example, the inner data are force values, and the output is momentum values.

Picture Fuzzy Maclaurin Symmetric Mean Operators and Their Applications in Solving Multiattribute Decision-Making Problems

To evaluate objects under uncertainty, many fuzzy frameworks have been designed and investigated so far. Among them, the frame of picture fuzzy set (PFS) is of considerable significance which can describe the four possible aspects of expert’s opinion using a degree of membership (DM), degree of nonmembership (DNM), degree of abstinence (DA), and degree of refusal (DR) in a certain range. Aggregation of information is always challenging especially when the input arguments are interrelated. To deal with such cases, the goal of this study is to develop the notion of the Maclaurin symmetric mean (MSM) operator as it aggregates information under uncertain environments and considers the relationship of the input arguments, which make it unique. In this paper, we studied the theory of MSM operators in the layout of PFSs and discussed their applications in the selection of the most suitable enterprise resource management (ERP) scheme for engineering purposes. We developed picture fuzzy MSM (PFMSM) operators and investigated their validity. We developed the multiattribute decision-making (MADM) algorithm based on the PFMSM operators to examine the performance of the ERP systems using picture fuzzy information. A numerical example to evaluate the performance of ERP systems is studied, and the effects of the associated parameters are discussed. The proposed aggregated results using PFMSM operators are found to be reliable as it takes into account the interrelationship of the input information, unlike traditional aggregation operators. A comparative study of the proposed PFMSM operators is also studied.

Application of meteorological drought for assessing watershed health using fuzzy-based reliability, resilience, and vulnerability

Extreme events, such as droughts, lead to considerable detrimental impacts on watersheds, especially in arid and semi-arid regions. To develop measures for mitigation of drought and formulate management plans, efficient methods are needed for assessing watershed health and risk. This study developed a Reliability, Resilience, and Vulnerability-based fuzzy framework (DRRV-Fuzzy) compared to the traditional method (DRRV-Fixed) for assessing watershed health and risk under drought. To this aim, the Standardized Precipitation-Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI) of meteorological drought were employed to evaluating the proposed framework in the Zayandehrud River basin of Iran from 1983 to 2017. Also, to examine the role of both traditional and fuzzy methods for extreme droughts, the 2000 to 2017 period has been chosen to evaluate Rel, Res, and Vul indicators in the study area. The proposed framework yielded more efficient and different results from the traditional method. Results revealed that: (1) because of the evapotranspiration effects in SPEI drought index, vulnerability is expected to be larger than over SPI index, which fuzzy approach can clarify it better; (2) the fuzzy method can quantify the categories of failure types of drought in the watershed and detected drought failures relative to the threshold value; (3) the fuzzy framework was more efficient than the fixed approach for quantifying watershed health and drought risk, which was developed by DRRV metrics; (4) according to both fixed and fuzzy techniques, the health of Zayandehrud River basin was affected by drought, especially at a higher time scale (12-month time).

Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients

IntroductionThe vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues. ObjectivesThis research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods. MethodsThe methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the ‘recipients list’ and ‘COVID-19 distribution criteria’. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM. Results(1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values. ConclusionThe findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.

Real R&D options under fuzzy uncertainty in market share and revealed information

In this paper, we study Nash equilibria for real R&D options in a context of non-cooperative oligopoly under fuzzy uncertainty in market shares and revealed information. In particular, we consider a duopoly fuzzy game in which each firm has two available strategies, that is to invest or to defer its research investment. The deferring option gives the benefit of the information revelation about market reaction but, on the other hand, the disadvantage of a lower market share. Since these two effects are not easily to quantify, we describe the revealed information and the market shares as fuzzy numbers. As shown by numerical examples, the proposed fuzzy framework allows to reach different firms' strategic behaviours and new Nash equilibria with respect to the crisp case.

An extension of TOPSIS based on linguistic terms in triangular intuitionistic fuzzy structure

Chen [24] introduced the extension of TOPSIS in the fuzzy structure, while this article stretches the modern approach of TOPSIS to the intuitionistic fuzzy framework. Linguistic terms are used in this study to evaluate the weight of each criterion and the rating of alternatives in the context of a triangular intuitionistic fuzzy number. A new intuitionistic fuzzy positive ideal solution (IFPIS) and intuitionistic fuzzy negative ideal solution (IFNIS) are proposed in this model of extended TOPSIS. Euclidean distance is introduced between two triangular intuitionistic fuzzy numbers to calculate separation between each alternative to both (IFPIS) and (IFNIS). The proposed model’s mechanism is presented with the help of an algorithm, and then it is applied to the personal selection problem. Finally, a comparative study is given between this model and other TOPSIS techniques.

Power Flow Control of Interconnected AC-DC Microgrids in Grid-Connected Hybrid Microgrids Using Modified UIPC

A typical grid-connected hybrid microgrid has one AC microgrid and one DC microgrid, which is considered for this system. Alternate of deploying the parallel-connected power converters, these microgrids are having all constituent parts linked deploying to make partial changes UIPC. The traditional construction of UIPC, which conveys three power converters in each stage, is rolled out incomplete improvements so the check of power converters is executed for power trade control between AC-DC microgrids. The substitute situating involves one power converter in each stage, named as line power converter, and transport power converter. The AC microgrid is united to the main grid into the LPCs which their DC transports are related and can set off in capacitance mode. A fuzzy regulator is conveyed in the lead representative development of the LPCs. The fuzzy framework is registered dependent on H∞ separating strategy to diminish the mistakes in enrollment capacities design. From start to the completion the BPC, the DC voltage of LPCs is contributed by the DC microgrid. Nonetheless, since the DC microgrid voltage is on condition that here by a PV framework, the DC interface voltage of the LPCs is rising and falling sporadically in number. Subsequently, as the beneficiation to hold consistent the DC connect dissimilarities, another nonlinear unsettling influence eyewitness based powerful different surface sliding mode control game plan is administered for DC side control of the BPC. The results build up the adequacy of the set forward power stream control approach of the improved UIPC for mixture microgrids.

Sparse machine learning assisted deep computational insights on the mechanical properties of graphene with intrinsic defects and doping

Despite the tremendous capabilities of Molecular dynamics (MD) simulations, they suffer from the limitation of computationally intensive and time-consuming nature. This hinders the seamless discovery of nanomaterials with adequate computational insights. Over the last decade, graphene has received widespread attention from the scientific community due to its extra-ordinary multi-physical properties, primarily focusing on the fundamental physics and chemistry along with the notion of scalable synthesis. However, the recent advances in machine learning have opened new frontiers in the research of such exceptional two-dimensional materials where the boundaries of different multi-physical properties could be pushed further efficiently with the assistance of deep computational intelligence. Here we propose a coupled machine learning (ML) based approach to investigate the critical mechanical responses of graphene by capturing the underlying physics of the system through an (D-)optimally minimum number of MD simulations. We have investigated five different internal and external controlling input features like temperature, strain rate, nanostructural defects, doping, and chirality, which influence the critical mechanical properties of graphene such as the constitutive behavior including fracture strength, failure strain and Young's modulus. Even though the aspect of computational intensiveness in molecular dynamics simulations is addressed through the coupled ML based approach, in a data-driven comprehensive analysis, it is often difficult to get complete information about the concerning input parameters including their statistical distributions and occurrence bounds. Thus, it becomes difficult to carry out computational investigations based on powerful techniques like Monte Carlo simulation. To mitigate this lacuna, here we have exploited the ML-assisted approach for developing a level-based fuzzy framework at nano-scale under sparse input descriptions. In this article, we first demonstrate the computational efficiency achieved through the proposed ML based framework without compromising quality of the analysis, followed by data-intensive correlation analysis, sensitivity and uncertainty quantification considering various levels of the influencing system parameters, revealing detailed computational insights on mechanical properties of graphene including the coupled interactive influence of intrinsic defects and doping.