Fuzzy Methodology Research Articles

An adaptive neuro fuzzy methodology for the diagnosis of prenatal hypoplastic left heart syndrome from ultrasound images

An adaptive neuro fuzzy methodology for the diagnosis of prenatal hypoplastic left heart syndrome from ultrasound images

Assessing of causes of accidents based on a novel integrated interval-valued Fermatean fuzzy methodology: towards a sustainable construction site

Assessing of causes of accidents based on a novel integrated interval-valued Fermatean fuzzy methodology: towards a sustainable construction site

Novel fuzzy deep learning approach for automated detection of useful COVID-19 tweets

Coronavirus (COVID-19) is a newly discovered viral disease from the SARS-CoV-2 family. This has caused a moral panic resulting in the spread of informative and uninformative information about COVID-19 and its effects. Twitter is a popular social media platform used extensively during the current outbreak. This paper aims to predict informative tweets related to COVID-19 on Twitter using a novel set of fuzzy rules involving deep learning techniques. This study focuses on identifying informative tweets during the pandemic to provide the public with trustworthy information and forecast how quickly diseases could spread. In this case, we have implemented RoBERTa and CT-BERT models using the fuzzy methodology to identify COVID-19 patient tweets. The proposed architecture combines deep learning transformer models RoBERTa and CT-BERT with the fuzzy technique to categorize posts as INFORMATIVE or UNINFORMATIVE. We performed a comparative analysis of our method with machine learning models and deep learning approaches. The results show that our proposed model can classify informative and uninformative tweets with an accuracy of 91.40% and an F1-score of 91.94% using the COVID-19 English tweet dataset. The proposed model is accurate and ready for real-world application.

Applying the Fuzzy PERT Method in Project Management: A Real-Life Case Study

Time is the most precious resource for any business simply because no one can bring time back. Therefore, there is a huge need to be able to control time accurately. Also, the competitiveness between companies increased the value of time. Many investments were allocated to build methodologies that help to use time effectively. Because it became a fact that the speed of work accomplishment is the main factor for being a considerable competitor. Planning for future projects usually relays on non-fixed but mostly expected data. It is more reliable to use fuzzy methods for this situation. In this study, Fuzzy PERT (FPERT) was used as a project management technique to plan the construction of a marble factory from the establishment phase to the plan of the machines that will be used later on. FPERT showed more realistic results than any classical method would. Out of 16 routes, the final result of the completion time was (90.9,122.1,151.9) days which was determined after triangular fuzzy numbers for the activity times in the entire project. This article aims to prove the efficiency of fuzzy methodologies especially FPERT to be conducted in future planned projects through this real-life case study.

Fuzzy Gaussian GARCH and Fuzzy Gaussian EGARCH Models: Foreign Exchange Market Forecast

This article discusses a comparison of the GARCH and EGARCH conditional variance methods, with respect to the Fuzzy Gaussian GARCH and Fuzzy Gaussian EGARCH. The returns of four exchange rates were forecasted at daily periodicity from January 2015 to November 2022 and out-of-sample, January 2019, and December 2022. The results indicate that the Fuzzy GARCH and Fuzzy EGARCH models better estimate the volatility behaviour of the exchange market series compared to traditional techniques. Therefore, the recommendation is to estimate other high volatility variables to verify the efficiency of the fuzzy techniques, however, the main limitation is that it is not possible to apply traditional econometric tests for fuzzy techniques because the parameters are not estimated with the logarithm of maximum likelihood. The estimation of the parameters of GARCH and EGARCH models with fuzzy theory is the originality proposal. In conclusion, fuzzy methodologies have less error in forecasting the volatility of in-sample and out-of-sample exchange rates.

Evaluation of series-parallel-cross-tied PV array configuration performance with maximum power point tracking techniques under partial shading conditions

Abstract Tracking the maximum power point is a critical issue with solar systems. The power output of the solar panel varies due to variations in irradiance and temperature. Nonuniform irradiation due to partial shading conditions has a direct impact on the characteristics of photovoltaic (PV) systems. To build a diversity of maximum power point tracking algorithms in solar PV systems, this work focuses on perturb and observe, incremental conductance, and fuzzy logic control methodologies. The suggested fuzzy logic control method outperformed the conventional incremental conductance and perturb and observe algorithms with a collection of 49 rules. This paper presents a novel series-parallel-cross-tied PV array configuration with a developed fuzzy methodology. To comment on the performance of a proposed system under various partial shading conditions, a series-parallel PV array configuration has been considered. The simulation result demonstrates that the fuzzy method has a percentage improvement in the global maximum power point tracking efficiency of 24.85% when compared to the perturb and observe method and a 65.5% improvement when compared to the incremental conductance method under long wide partial shading conditions. In the case of the middle partial shading condition, the fuzzy method has a percentage improvement in the global maximum power point tracking efficiency of 12.4% compared to the perturb and observe method and a 60.7% improvement compared to the incremental conductance method.

Implementation of Fuzzy Logic in Managing Common Anesthesia

Using fuzzy sets for reasoning is what we mean when we talk about fuzzy logic. Contradictory natures are prevalent phenomena in the medical field. Guidelines are used by anesthesiologists when taking care of patients. After gauging the patient’s vitals, he may make adjustments to the flow of medication and fluids, or even the ventilator settings. Knowledge about the real world is typically sketchy, inaccurate, and inconsistent. Due to the inherent representation of subjective human conceptions used in much medical decision making, fuzzy logic seems well-suited for use in anaesthesia. We have developed a fuzzy expert system using fuzzy methodology for the aim of fluid management during general anaesthesia. The desired intravenous fluid rate (IFR) is the defuzzified value that is output by the fuzzy expert system. Fuzzy inputs serve as antecedent parts of rules for a fuzzy expert system, and some examples of such inputs are mean arterial pressure (HUO), hourly urine output (HUO), and central venous pressure (CVP). It would only cost a little sum to have a human operator sometimes enter MAP, HUO, and CVP values into a personal computer for this purpose. The study’s overarching goal was to devise a method for approximating IFR by making use of a linguistic description of MAP and HUO. Fuzzy sets, including decreasing, constant, and growing MAP and HUO rates of change, would assist to illustrate the trend in a patient’s fluid state. To regulate fluid levels more precisely would be possible. Expert guidance in addition to the calculated use of fuzzy methods are essential for achievement of the desired outcome. Patients must be in generally good health before this mode may be used on them, and they must be undergoing minimally invasive surgery. Moderate to severe blood loss after surgery need more complex modalities involving more factors.

Centroid for Solving Bi-Level Linear Fractional Programming Problems with Fuzzy Interval Coefficients by Utilizing MO-Technique

This paper presents an efficient and straightforward methodology with less computational complexities to title the bi-level objective linear fractional programming problem with fuzzy interval coefficients (BILOLFPP with FIC). To construct the methodology, the concept of mean technique is utilized to tackle the fuzzy numbers in addition to adding to α = [mean (ai) , mean (bi)] , i = 1, …, n, then. Accordingly, the fuzzy programming issue is converted into a single objective linear fractional programming problem (SOLFPP with FIC) by the utilize of weight function. The fuzzy technique has significant structural transform metamorphosis during the recent decades. Numerous to mention introduced have been undertaken to explanation fuzzy methodology for linear, non-linear programming issues. While, the previous finding that introduced have been conflicting, recent studies of competitive situations indicate that LFPP with fuzzy interval coefficients (LFPP with FIC) has an advantageous effect mostly on comparison situation. One of the suggestions which we found is interval approximations, closed interval approximation of sequential fuzzy number for resolving fuzzy number LFPP without changing it to a crisp issue. A new variant of modified simplex methodology is studied here just for resolving fuzzy number LFPP utilizing fuzzy arithmetic. Consequently, fuzzy representation of some important theories of fuzzy LFPP has been reproved. A fuzzy LFPP with FIC is worked out as numerical examples illustrate to the suggested methodology. On iterative processes, it decreases the overall processing time to explain, the modified simplex methodology for solving BILLFPP with FIC with out to crisp by taking numerical examples and compare with Nasseri, Verdegay and Mahmoudi methodology changing it to a crisp issue [9].

A model for green-resilient supplier selection: fuzzy best-worst multi-criteria decision-making method and its applications.

Supplier selection is regarded as the primary goal of supply chain management (SCM) because it affects its performance, productivity, pleasure, flexibility, and system speed in lockdown. A new method is proposed based on a multi-stage fuzzy sustainable supplier index (FSSI). Experts can use the triple bottom line (TBL) criteria to select the best supplier. In addition, the worst method is proposed based on trapezoidal membership and fuzzy membership functions, which can cover uncertainties and ambiguous environments. Because it collects the related criteria and sub-criteria and uses a direct fuzzy methodology, this research has impacted the SCM literature because it helps solve previous expert methods' computational difficulties. In addition, an ordered mean integration representation method has been implemented to prioritize the selection of the best supplier (SS) based on the sustainability performance of the best supplier, which improves the selection accuracy compared to the previous ranking method. This study can be used as a benchmark to determine which supplier is the best in sustainability. To provide the superiority and broad applicability of the proposed model, a practical case study was completed. On the other hand, the COVID-19 pandemic harms productivity, company performance, and selecting the best suppliers based on sustainability performance. The lockdown situation caused by the COVID-19 pandemic hurts company performance and management.

Genetic Fuzzy Methodology for Decentralized Cooperative UAVs to Transport a Shared Payload

In this work, we train controllers (models) using Genetic Fuzzy Methodology (GFM) for learning cooperative behavior in a team of decentralized UAVs to transport a shared slung payload. The training is done in a reinforcement learning fashion where the models learn strategies based on feedback received from the environment. The controllers in the UAVs are modeled as fuzzy systems. Genetic Algorithm is used to evolve the models to achieve the overall goal of bringing the payload to the desired locations while satisfying the physical and operational constraints. The UAVs do not explicitly communicate with one another, and each UAV makes its own decisions, thus making it a decentralized system. However, during the training, the cost function is defined such that it is a representation of the team’s effectiveness in achieving the overall goal of bringing the shared payload to the target. By including a penalization term for any constraint violation during the training, the UAVs learn strategies that do not require explicit communication to achieve efficient transportation of payload while satisfying all constraints. We also present the performance metrics by testing the trained UAVs on new scenarios with different target locations and with different number of UAVs in the team.

Forgotten Factors in Knowledge Conversion and Routines: A Fuzzy Analysis of Employee Knowledge Management in Exporting Companies in Boyacá

The department of Boyacá accounts for only 0.93% of national exports, which means that the participation of exporting companies in the region is low. One of the most important factors within these organizations is the knowledge of the collaborators, since it is an asset that contributes to the daily activities carried out within an organization. Hence, the objective of this research was to analyze the incidence of the forgotten factors in knowledge management through the conversion of knowledge and the routines of the personnel in Boyacá’s exporting companies, by means of causal analysis using fuzzy methodologies. The participants are exporting activity collaborators in the companies, who were consulted as sources of information for the Boyacá chamber of commerce. For the treatment, the forgotten effects theory, the experton method, and the adequacy coefficient are used. The information collected is processed using FuzzyLog software. The findings highlight that there are forgotten factors between the knowledge conversion and routines related to informal communication and social interactions. It is worth noting that it is important to carry out a more in-depth analysis of each of the individual knowledge spiral pillars in exporting companies in different regions of the country, focusing on social interactions (linguistic expression) and informal communication (electronic meetings).

Fuzzy Layered Convolution Neutral Network for Feature Level Fusion Based On Multimodal Sentiment Classification

Multimodal sentiment analysis (MSA) is one of the core research topics of natural language processing (NLP). MSA has become a challenge for scholars and is equally complicated for an appliance to comprehend. One study that supports MSdifficulties is the MSA, which is learning opinions, emotions, and attitudes in an audio-visual format. In order words, using such diverse modalities to obtain opinions and identify emotions is necessary. Such utilization can be achieved via modality datafusion;such as feature fusion. In handling the data fusion of such diverse modalities while obtaining high performance, a typical machine learning algorithm is Deep Learning (DL), particularly the Convolutional Neutral Network (CNN), which has the capacity to handle tasks of great intricacy and difficulty. In this paper, we present a CNN architecture with an integrated layer via fuzzy methodologies for MSA, a task yet to be explored in improving the accuracy performance of CNN for diverse inputs. Experiments conducted on a benchmark multimodal dataset, MOSI, obtaining 37.5% and 81% on seven (7) class and binary classification respectively, reveals an improved accuracy performance compared with the typical CNN, which acquired 28.9% and 78%, respectively.

Innovation in the strategic planning through fuzzy methodologies. A study of the industrial context of Bogota, Colombia during COVID-19

Innovation in the strategic planning through fuzzy methodologies. A study of the industrial context of Bogota, Colombia during COVID-19

Greenness, leanness and agility assessment of hospitals: a fuzzy methodology

Similar to manufacturing industry, performance measurement is essential in service industries. This study develops an integrated fuzzy methodology to assess greenness, leanness and agility (GLA) of hospitals. Firstly, essential measures of GLA are determined using a literature review. Secondly, a standard fuzzy approach is applied to assess a hospital by the developed measures. Thirdly, the results are integrated to provide a single unit-less GLA index. Hence, different outputs of performance metrics are integrated in this phase. Finally, an interval-value fuzzy approach is applied to confirm the results. The investigated case study is moderately compatible to GLA measures and needs to improve its performance. A discussion and some research directions are provided at the end of this study. As the main benefit of this research, managers can simultaneously investigate hospitals by GLA viewpoints.

Greenness, Leanness and Agility (GLA) Assessment of Hospitals: A Fuzzy Methodology

Greenness, Leanness and Agility (GLA) Assessment of Hospitals: A Fuzzy Methodology

A redistribution-based multi-stage humanitarian logistic design model considering the spherical fuzzy methodology

A redistribution-based multi-stage humanitarian logistic design model considering the spherical fuzzy methodology

Fuzzy risk-based technique for the design of an ogee spillway in a diversion dam based on hydrological return period discharge and the resistance-load theory

This paper provides a Fuzzy methodology based on calculating the hydrological return period discharge and the resistance-load theory for designing an ogee spillway in a diversion dam. Hence, the uncertainties of rainfall intensity, catchment runoff coefficient, and spillway discharge coefficient are endowed with triangular Fuzzy numbers (TFNs), describing possible values the uncertain parameters may have. To do so, the rational formula and, later, the spillway discharge equation are analyzed here to obtain the Fuzzy products of the peak runoff and spillway discharge capacity. On the basis that both must match, the risk is calculated using the difference between the obtained Fuzzy product quantities with three Fuzzy arithmetic operations. It is observed that the standard approximation closely tracks the result of the alpha-cut (exact) method, providing a time-saving tool for Fuzzy calculations. Since the return period (T) and risk of failure are related quantities, the spillway dimensions are calculated for several recurrence intervals, assuming risk equal to 1/T. Then, the Fuzzy approach is compared with the conventional return-period approach for the design of an ogee crest in three experimental catchments in Iran with adequate historical data. The result indicates that the Fuzzy technique outperforms the conventional return-period approach for designing an ogee spillway, obtaining an average 29% larger spillway dimension in all case studies. Beyond that, the Fuzzy procedure is applied to another catchment with short rainfall-runoff data, which is impossible to implement the conventional return-period method for the hydrologic design of an ogee spillway.

Use of a fuzzy qualitative model to reanalyze radon relationship with atmospheric variables in a coastal area near a NORM repository

A soft computing methodology, PreFuRGe (Predictive Fuzzy Rules Generator), was applied to study atmospheric radon in relation to other meteorological variables. This algorithm is based on fuzzy clustering and generates a rule-based system that provides a qualitative model that describes the behavior of radon. Two one-year radon datasets were used to verify the consistency of the fuzzy methodology, by comparing the obtained models with published results obtained with statistical techniques. These datasets were collected in the city of Huelva, to the southwest of Europe, and were already studied in peer-reviewed publications. The proposed fuzzy methodology was able to provide results consistent with previous studies, identifying the main drivers in radon behavior, atmospheric stability and wind speed, together with a complete characterization of the daily cycle and its seasonality. PreFuRGe highlighted features that would otherwise require detailed examination combining a number of classical statistical techniques, proving to be a useful tool to characterize heterogeneous datasets. • A fuzzy logic based methodology was used for the first time on atmospheric radon. • The technique provides a full qualitative characterization of the studied variables. • The consistency of the fuzzy methodology was verified against published results. • The proposed methodology can be applied on heterogeneous sets of variables.

Fuzzy Multi-Criteria Analyses on Green Supplier Selection in an Agri-Food Company

The way agri-food companies conduct business has changed as a result of changes in the market. These companies must start working in a more environmentally friendly manner. This study aims to examine, assess, and compare how various fuzzy methodologies are applied in green supplier selection (GSS), using an agri-food industry as an example. The company Biljana Brko, which engages in GSS, was observed in this study. The selection aids in the acquisition of raw materials and materials whose environmental impact will be minimized. Ecological and economic factors were taken into consideration when choosing green suppliers. Experts who assessed the weight of the criteria and the suppliers with linguistic values were chosen to carry out this selection. In order to do this, a fuzzy set that effectively applies these linguistic values was employed. The fuzzy SWARA (FSWARA) approach was utilized to calculate the weights of the criteria, revealing that the criterion of Environmental Management System has the highest weight. Drawing on the opinions of experts, suppliers were ranked using the fuzzy MABAC, MARCOS, and CRADIS techniques. The results show that supplier S2 receives the highest ratings. Along with this provider, supplier S3 is noteworthy because it excelled in the sensitivity analysis across a variety of scenarios. In light of this, Biljana Brko should give preference to these suppliers. Further, the results of the three adopted techniques were compared. The comparison reveals that the ranking order produced by all three techniques is remarkably similar. This supplier order differed slightly from the FMABAC method just in one scenario. Hence, this work demonstrates that the three fuzzy techniques can solve the GSS problem and other problems by ranking alternatives.

Enhancing the measurement of firm inefficiency accounting for corporate social responsibility: A dynamic data envelopment analysis fuzzy approach

This paper contributes to research on the corporate social responsibility (CSR) field and the inefficiency measurement of firms by proposing a new method for evaluating inefficiency accounting for firms’ CSR activities. The new approach considers the imprecise nature of CSR data through the fuzzy data envelopment analysis (FDEA) method and further extends it by allowing for the dynamic interdependence of firms’ production decisions through adjustment costs, related to firms investments. In addition, the new method deals with zero or negative values for inputs and/or outputs of the data. The empirical application used in this paper considers a dataset of CSR activities of European firms for three industries (capital, consumption, and other) over the period 2014–2016. Two main results are found with this data. First, the study shows that fuzzy dynamic inefficiencies tend to be lower than these obtained from the conventional crisp evaluation of inefficiency. Second, the study finds some differences in dynamic inefficiencies at distinct levels of fuzziness. Overall, the results seem to confirm that the usage of dynamic fuzzy methodology adds some value to the standard crisp approach.