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Inventory control is vital in supply chain management, especially for perishable goods. The paper depicts a probabilistic inventory model for robust products where deterioration and demand change over time and depend on reliability. This paper also talks about the conventional back order reliability inventory model in a fuzzy, cloudy environment. This is because products deteriorate and demand fluctuates all the time. This study shows a novel approach to modeling inventory that deals with these problems. It does this by including uniform distribution deterioration, demand that depends on both time and product reliability, and cloudy-fuzzy numbers to show uncertainty. Although we start with the crisp model and fuzzifying it to obtain a decision under the cloudy fuzzy demand rate (which is an extension of dense fuzzy ) demand rate, before putting it to use in practice. For ranking the fuzzy numbers, a new defuzzification method was used. Subsequently, extensive analysis is done to compare the crisp, general fuzzy solutions to the cloudy fuzzy solutions. The numerical examples and graphical are examined to demonstrate that the novel approach is useful in the model itself. The suggested model aims to maintain high service reliability while minimizing the total cost of inventory. Numerical analyses indicate that the model is effective, exhibiting that it can lower costs and improve reliability compared to older models using MATLAB software. This study builds a strong framework for managing inventory in supply lines for perishable goods, which opens up opportunities for more progress in this area. [ABSTRACT FROM AUTHOR]

Traditional assessment methods in education often rely on rigid grading structures that may fail to capture the nuances of language skills, especially in subjective areas like writing. This article explores how fuzzy logic, a mathematical system that handles imprecision, can enhance English writing assessment by providing a more flexible, holistic view of students' abilities. Formulation of the problem. English writing is a fairly subjective practice that, due to its interpretative nature, can often present instructors with a challenge when it comes to administering evaluations that are impartial and purely objective. This paper aims to propose a proper way of investing rigor and focus on the core principles of English writing into the process of reviewing student work through the applied integration of mathematics’ fuzzy logic. Materials and methods. The resources included in this article are a variety of robust and innovative works of academic literature that have proven their relevance and advancement to the field of mathematics and also pedagogical assessment methodology. The primary studies and their respective demonstrations of research are productively referenced throughout this paper to concretely elucidate how fuzzy logic can make a difference in forming adequate feedback for English writing students. Results. The results point to fuzzy logic-based assessments of English writing having merit that is long overdue in English classrooms. Conclusions. Overall, this article recognizes that fuzzy logic-based assessments of English writing are a ruthlessly efficient, convenient, and innovative strategic approach to scrutinizing student work with fairness, absence of creative bias, and extensiveness.

Maintaining consistency is a crucial element in ensuring the reliability of pairwise comparisons provided by experts in the Analytic Hierarchy Process (AHP) and Fuzzy AHP. Many researchers have put forth various definitions concerning the consistency of fuzzy Pairwise Comparison Matrices (PCMs) using fuzzy set theory. However, in most applications of fuzzy AHP, fuzzy PCM consistency is evaluated by defuzzifying the fuzzy comparisons, similar to the approach used for crisp PCMs. This paper introduces a novel method for transforming fuzzy comparisons into crisp comparisons through defuzzification, specifically utilizing the geometric mean. Furthermore, it proposes a method to enhance the consistency of the PCM. The presented methodology is applied to several problems previously addressed in the literature.

The sustainable use and disposal of carbon materials without affecting the profit (gain) of an industry is an important task of the policy makers recent times. The present study deals with a vendor–buyer inventory model for deteriorating and imperfect quality items considering the carbon emissions under different environments, namely general fuzzy and triangular interval type-2 fuzzy environments. In fact, we develop a vendor-buyer inventory model under a fuzzy bi-matrix game approach and construct a joint payoff function along with a joint effective emission cost function. Carbon emission is related to the fuel consumption during transportation, disposal of the deteriorating items and warehouse energy consumption per unit item. The notion of this study is to optimize the total average inventory cost along with the amount of carbon emission cost under flexible demand rate and uncertain cost parameters. Basically, we have developed a new optimization problem incorporating vendor–buyer's objective function with carbon emission based on the expected payoff function of the bi-matrix game. Numerical findings reveal that a type-2 fuzzy system could be able to optimize the average inventory cost as well as total emission cost all the time. Finally, sensitivity analysis graphical illustrations are made to validate the model. [ABSTRACT FROM AUTHOR]

The postweaning phase is stressful for rabbits due to maternal separation and the introduction of solid feed. Stress can be aggravated when animals are subjected to thermal discomfort. Therefore, thermal variables, such as air temperature, which influence the productive and physiological performance of animals, require greater control in this phase of life of rabbits to ensure their well-being and productive efficiency. Thus, the objective of the present study was to develop and compare fuzzy inference systems (FISs) with different configurations to predict the feed intake (FI) of New Zealand White (NZW) rabbits subjected to different thermal conditions after weaning. The experiment lasted 14 days, and twelve rabbits between 30 and 43 days old were used. The animals were housed in air-conditioned wind tunnels and subjected to air temperatures of 20, 24, 28 and 32 °C. For the FIS configurations, Mamdani inference with five defuzzification methods (center of gravity (COG), bisector of area (BOA), largest of maximum (LOM), mean of maximum (MOM) and smallest of maximum (SOM)) and Sugeno inference with two defuzzification methods (weighted average (WA) and weighted sum (WS)), were evaluated. In both inference methods, the input variables (air temperature and time after weaning) were represented by triangular, Gaussian or trapezoidal functions. In turn, the output variable (FI, g) was represented by triangular, Gaussian or trapezoidal functions in the Mamdani FIS and by singleton functions in the Sugeno FIS. Thus, all developed FISs were validated, and their results were compared to the experimental data using statistical indices. As a result, adequate FI prediction performances were obtained for rabbits using both inference methods, regardless of the configurations used in their development. However, the smallest simulation errors were obtained using the Sugeno FIS with Gaussian inputs and WA defuzzification and is therefore a system with greater generalization capacity for unknown scenarios. Thus, the developed models can be used as a support system for decisions on the management of rabbits, aiding the efficient production and welfare of the animals, as well as the maintenance of thermal variables through the activation of installed climate systems inside the rabbit production environment. Trial registration number: 085/17. Date of registration: 14/12/2017. [ABSTRACT FROM AUTHOR]

Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d'Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) to classify variables in four clusters, although the manual application of the technique is complex and prone to error. In one of the previous works, a novel concept of reduced conical matrix was introduced, and the SmartISM software was developed for the user-friendly implementation of ISM and MICMAC. The web-based SmartISM software has been used more than 48,123 times in 87 countries to generate ISM models and MICMAC diagrams. This work attempts to identify existing approaches to fuzzy ISM and fuzzy MICMAC and upscale the SmartISM to incorporate fuzzy approaches. The fuzzy set theory proposed by Zadeh 1965 and Goguen 1969 helps the decision makers to provide their input with the consideration of vagueness in the real environment. The systematic review of 32 studies identified five significant approaches that have used different linguistic scales, fuzzy numbers, and defuzzification methods. Further, the approaches have differences in either using single or double defuzzification, and the aggregation of inputs of decision makers either before or after defuzzification, as well as the incorporation of transitivity either before or after defuzzification. A roadmap was devised to aggregate and generalize different approaches. Further, two of the identified approaches have been implemented in SmartISM 2.0 and the results have been reported. Finally, the comparative analysis of different approaches using SmartISM 2.0 in the area of digital transformation shows that, with a wide flexibility of fuzzy scales, the results converge and improve the confidence in the final model. The roadmap and SmartISM 2.0 will help in the implementation of fuzzy ISM and fuzzy MICMAC in a more robust and informed way. [ABSTRACT FROM AUTHOR]

Purpose: Fuzzy multi-choice problems are widely used in the real world in the fields of industry, agriculture, science, technology, etc. Therefore, studying and solving such problems is essential. This study introduces some methods to solve fully fuzzy multi-choice linear programming problems.Methodology: To solve the fully fuzzy multi-choice linear programming problems, we use the linear least squares polynomial to convert the multi-choice linear programming problem to a mixed integer linear programming problem. Also, we convert the problem from the fuzzy mode to the crisp mode by using the defuzzification methods (Roubens ranking function.Findings: To investigate the efficiency of the suggested method, we solve and compare two fully fuzzy multi-choice linear programming models using the proposed methods. In general, the resulting algorithms are simple and very inexpensive to implement, and they are more efficient than those of previous studies.Originality/Value: In this article, all parameters and coefficients of the problem are triangular fuzzy numbers, and the right side of the problem's constraints are the fuzzy multi-choice parameters. The number of fuzzy multi-choice parameters in the previous articles has been considered only two fuzzy choices. However, in this article, the number of these parameters can be arbitrary. Also, this article uses the fuzzy linear least squares method to approximate the fuzzy multi-choice parameters.

Purpose: Fuzzy multi-choice problems are widely used in the real world in the fields of industry, agriculture, science, technology, etc. Therefore, studying and solving such problems is essential. This study introduces some methods to solve fully fuzzy multi-choice linear programming problems. Methodology: To solve the fully fuzzy multi-choice linear programming problems, we use the linear least squares polynomial to convert the multi-choice linear programming problem to a mixed integer linear programming problem. Also, we convert the problem from the fuzzy mode to the crisp mode by using the defuzzification methods (Roubens ranking function). Findings: To investigate the efficiency of the suggested method, we solve and compare two fully fuzzy multi-choice linear programming models using the proposed methods. In general, the resulting algorithms are simple and very inexpensive to implement, and they are more efficient than those of previous studies. Originality/Value: In this article, all parameters and coefficients of the problem are triangular fuzzy numbers, and the right side of the problem's constraints are the fuzzy multi-choice parameters. The number of fuzzy multi-choice parameters in the previous articles has been considered only two fuzzy choices. However, in this article, the number of these parameters can be arbitrary. Also, this article uses the fuzzy linear least squares method to approximate the fuzzy multi-choice parameters. [ABSTRACT FROM AUTHOR]

Maintaining consistency is a crucial element in ensuring the reliability of pairwise comparisons provided by experts in the Analytic Hierarchy Process (AHP) and Fuzzy AHP. Many researchers have put forth various definitions concerning the consistency of fuzzy Pairwise Comparison Matrices (PCMs) using fuzzy set theory. However, in most applications of fuzzy AHP, fuzzy PCM consistency is evaluated by defuzzifying the fuzzy comparisons, similar to the approach used for crisp PCMs. This paper introduces a novel method for transforming fuzzy comparisons into crisp comparisons through defuzzification, specifically utilizing the geometric mean. Furthermore, it proposes a method to enhance the consistency of the PCM. The presented methodology is applied to several problems previously addressed in the literature. [ABSTRACT FROM AUTHOR]

In this article, we present a novel methodology for inventory management in the pharmaceutical industry, considering the nature of its supply chain. Traditional inventory models often fail to capture the particularities of the pharmaceutical sector, characterized by limited storage space, product degradation, and trade credits. To address these particularities, using fuzzy logic, we propose models that are adaptable to real-world scenarios. The proposed models are designed to reduce total costs for both vendors and clients, a gap not explored in the existing literature. Our methodology employs pentagonal fuzzy number (PFN) arithmetic and Kuhn–Tucker optimization. Additionally, the integration of the naive Bayes (NB) classifier and the use of the Weka artificial intelligence suite increase the effectiveness of our model in complex decision-making environments. A key finding is the high classification accuracy of the model, with the NB classifier correctly categorizing approximately 95.9% of the scenarios, indicating an operational efficiency. This finding is complemented by the model capability to determine the optimal production quantity, considering cost factors related to manufacturing and transportation, which is essential in minimizing overall inventory costs. Our methodology, based on machine learning and fuzzy logic, enhances the inventory management in dynamic sectors like the pharmaceutical industry. While our focus is on a single-product scenario between suppliers and buyers, future research hopes to extend this focus to wider contexts, as epidemic conditions and other applications. [ABSTRACT FROM AUTHOR]

This article deals with a backorder economic order quantity (EOQ) model where the demand rate splits into two parts, one of which assumes constant value and the other part varies with the number of customers. First of all, a crisp model is developed to optimize the average inventory cost under some constraints. Due to the flexible nature of the several cost parameters involved in the model, considering a case study a fuzzy mathematical model is also developed. Moreover, since the fuzzy set has the versatile nature used by several decision makers participating in the inventory process itself so, a power set of the native fuzzy set is considered to develop the original fuzzy model. In fact, a Hasse diagram of fuzzy power set is considered based on partial order relations. Simultaneously, metric distances among various fuzzy sets have been calculated. Then a fuzzy optimization problem is defined and the model has been defuzzified with the help of some novel ranking methods utilizing the volume of the proposed Hasse diagram. A solution algorithm is developed to solve the problem. Numerical study reveals that the optimum solution exists due to the application of Hasse diagram whose sides are computed by taking supremum among several fuzzy sets compared to some other existing state-of-arts. Finally, sensitivity analysis and graphical illustrations are done to justify the novelty of the proposed approach. [ABSTRACT FROM AUTHOR]

Exercise and Physical Activity are important factors to improve the student's health and academic status. Student exercise should be continuously monitored to eliminate risk factors and health issues. The previous monitoring system faced difficulties while handling the vast amount of data obtained from multiple sensors because it was affected by uncertainty and noise issues. The research difficulties are addressed with the help of the Multi-Attribute Fuzzy Evaluation Model (MAFEM), which monitors student's health using sensor data. The MAFEM approach uses the fuzzy set and fuzzy logic to derive the relationship between the features. In addition, the method uses preprocessing, fuzzification, defuzzification and rule evaluation processes. These steps are adjusted according to the threshold value that maximizes the personalization and holistic assessment efficiency because the system uses multiple attributes. During the analysis, MM-Fit dataset information is utilized to evaluate the system efficiency in which the system ensures the minimum computation complexity $O\left ({{ r.m.n }}\right)$ and minimum latency value $\left ({{ \approx 70mAh }}\right)$ .. In addition, the accuracy metrics are also applied to evaluate the system's effectiveness, with 97.11% precision, 0.23 RMSE and 0.26 MSE values.

Position verification is essential in connected and autonomous vehicle technology to enable secure vehicle-to-everything communication. Previous attempts to verify location information have used specific hardware, traffic parameters, and statistical model-based techniques dependent on neighbouring vehicles and roadside infrastructure and whose judgements can be influenced by untrustworthy entities. Considering the back-and-forth communications during verification, these techniques are also unsuitable in the dynamic vehicular networking environment. In this context, this paper proposes a self-reliant trustbased position verification technique using dynamic geofencing, neural network, and Mamdani fuzzy logic controller. The method uses vehicular dynamics, such as distance between the sender and receiver vehicles, magnitude of the speed difference, and direction, to verify the trustworthiness of vehicle positions. An experimental analysis of a dataset of simulated driving scenarios in MATLAB demonstrates that the feedforward neural network records the highest direction classification performance at 99.8% in conjunction with the centroid defuzzification method. Subsequently, further quantitative analysis, including the Receiver Operating Characteristic curve with Area Under Curve and trust level distribution histograms, indicates that the suggested classification model outperforms a random classifier and effectively identifies false position data from the actual during trust computation.

The work is devoted to modeling the temperature control system for the process of bacterial oxidation of refractory gold-bearing sulfide ores. The simulation was performed using the Fuzzy Logic Toolbox editor in the MATLAB software package. The substantiation of the set of input and output variables, the type of membership functions was carried out, the base of rules for the inference engine was formed. The main stages of modeling are described and the results are analyzed. The use of trapezoidal membership functions is substantiated.

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Groundwater is a vital resource for human consumption, particularly in rural areas with limited access to treated water. The conventional Water Quality Index models used for this purpose have limitations related to data volatility and judgment uncertainties. To overcome these limitations, our study introduces a novel approach that employs a Fuzzy Inference System to determine the Water Quality Index. The dataset used in our research includes multiple parameters such as pH, EC, TDS, Ca, Mg, Na, K, HCO3, Cl, SO4, TH, DWQI, and other physio-chemical and chemical parameters. Our approach utilizes linguistic variables, fuzzy rules, and the hyperbolic tangent set function to handle imprecise and uncertain water quality data. By employing Fuzzy C-Means clustering, we group similar water samples based on quality parameters and map membership values to linguistic terms representing water quality categories. Suitable defuzzification methods are then applied to convert fuzzy outputs into precise results. This proposed approach provides a comprehensive framework for accurate water quality assessment, enabling informed decision-making and more reliable and precise evaluations of groundwater quality. HIGHLIGHTS A unique usage of the Fuzzy Inference System (FIS) to determine the Water Quality Index (WQI).; This research evaluated quality using pH, total hardness, total dissolved solids, calcium, and manganese.; The recommended design method was compared against deterministic results to determine its feasibility.; This paper aims to provide a fuzzy-based paradigm for evaluating groundwater safety for human consumption.;

Intercultural competitiveness is an important guarantee for Business English majors to show their advantages and grasp the opportunities of the times in the Belt and Road Initiative. This paper evaluates the cross-cultural competitiveness of business English majors by constructing the CFCS-TOPSIS model and proposes strategies to improve the cross-cultural competitiveness of business English majors in the Belt and Road Initiative based on the evaluation results. The evaluation system of cross-cultural competitiveness of business English students is constructed, while the CFCS algorithm is used to denazify the fuzzy-valued indexes, and the obtained indexes are used as input factors to calculate the TOPSIS model. The research is conducted on students who major in business English at a university and the current level of their intercultural competitiveness is measured and analyzed using statistical analysis methods. The results indicate that business English majors in this university have a good level of intercultural competitiveness, but their intercultural skills and values still require improvement. Cross-sectional comparison of the cross-cultural competitiveness of business English majors in 15 colleges and universities focusing on the construction of the Belt and Road, the Pi values of U2, U5, U12, and U15 colleges and universities are 0.493, 0.536, 0.748, and 0.645, respectively. The indexes of understanding the international business environment (X1), cross-cultural communication theory (X2), and openness (X7) have the most significant influence on the cross-cultural competitiveness of business English majors. The most significant effect is caused by intercultural competitiveness.

Incorporating the concept of environmentally sustainable development into the Civics program in colleges and universities is an important initiative to promote the service of environmental sustainability by the Freshmen. Innovative teaching mode of ecological sustainability and civic education, and the deep integration of green development concept and civic education are crucial to cultivating high-quality talents with environmental sustainability. In this paper, the collected data were standardized and defuzzified, and the CRITIC combination assignment method was used to assign weights to the indicators to obtain the optimal combination weights. Based on this, the indicator data’s gray correlation coefficient and correlation degree were calculated to construct the effect assessment model. The evaluation content for integrating the cultivation of the concept of environmental sustainability and ideological education is proposed in the paper, and the effect evaluation of this was implemented for 11 years. The evaluation results show that in the three classes of college A in the pilot area, the integration of the concept of environmental sustainability and Civic and Political education of college students was significantly improved, and the mean values of the measured improvement were 4.96, 5.05, and 4.95, respectively, which showed a better effect. Among the dimension scores, the mean value of the scores of the three classes in the five dimensions is 3.624.This result indicates that the cultivation of the concept of environmental sustainability has a significant enhancement effect on the performance of Civic and Political Education, and the integration of the two is having a significant impact. The study in this paper incorporates the concept of environmental sustainability into the ideology and political science curriculum of colleges and universities is an effective strategy that not only enhances students’ environmental awareness, but also strengthens the effectiveness of ideology and political science education.

Fuzzy logic has been developed since the 1960s. Research related to fuzzy logic application in hydraulics and pneumatics is mainly aimed at energy demand reduction and improvement in operational characteristics. This article summarizes the recent achievements in hydraulic and pneumatic fuzzy logic system design. First, the main application areas have been identified, including control and fault diagnosis. The control systems were additionally grouped according to the main objects of study, such as pumps, actuators, proportional valves, etc. Then, the results of the recent research were presented, and the main features of the designed fuzzy logic units were summarized for each group. Particular attention was paid to types of membership functions used for fuzzification and defuzzification, numbers of fuzzy sets defined for input and output signals, types of fuzzy operators, the applied inference algorithm and the defuzzification method. Based on the analysis of the listed parameters, conclusions were formulated regarding advantages, main issues and difficulties, as well as recommended directions for further development. [ABSTRACT FROM AUTHOR]

Since isosceles triangular and trapezoidal membership functions [4] are easy to manage, they were applied to various fuzzy approximate reasoning [10], [13], [14]. The centroids of isosceles triangular and trapezoidal membership functions are mentioned in this article [16], [9] and formalized in [11] and [12]. Some propositions of the composition mapping (f + · g, or f +* g using Mizar formalism, where f, g are a ne mappings), are proved following [3], [15]. Then different notations for the same isosceles triangular and trapezoidal membership function are formalized. We proved the agreement of the same function expressed with different parameters and formalized those centroids with parameters. In addition, various properties of membership functions on intervals where the endpoints of the domain are fixed and on general intervals are formalized in Mizar [1], [2]. Our formal development contains also some numerical results which can be potentially useful to encode either fuzzy numbers [7], or even fuzzy implications [5], [6] and extends the possibility of building hybrid rough-fuzzy approach in the future [8]. [ABSTRACT FROM AUTHOR]

Fuzzy logic systems have found extensive use in system identification. decision making, and pattern recognition problems from industries to academics. The membership functions play a pivotal role in overall role in fuzzy representation. as these are considered as the building blocks of fuzzy set theory and they decide the degree of truth in fuzzy logic. The extraction of the membership function is ambience dependent and thus complication exists in the process of evaluation. In this assessment the main work deals with the derivation of fuzzy membership function where numerical data is available. The numerical cubic spline and defuzzification technique are used here. In this paper we mainly used triangular fuzzy number to construct the membership function. A case study is furnished to emphasize the advantage of adopting the method. [ABSTRACT FROM AUTHOR]

Simple Summary: With the rapid technological advances, the application of artificial intelligence (AI) has witnessed significant growth in the agricultural industry, specifically in the poultry sector. The use of AI in estimating poultry weight can significantly impact production economics and overall efficiency in the poultry sector. Therefore, this paper presents an innovative AI approach based on the fuzzy logic (FL) method for estimating poultry weight. The FL models were created using expert knowledge and key input variables such as indoor temperature, humidity, and feed consumption. This study's findings demonstrate that FL-based methods exhibit great promise for achieving accurate and efficient poultry weight estimation. Integrating the FL technique in the poultry industry can bring numerous benefits, including improved decision-making processes, enhanced efficiency, and reduced costs. Traditional manual weighing systems for birds on poultry farms are time-consuming and may compromise animal welfare. Although automatic weighing systems have been introduced as an alternative, they face limitations in accurately estimating the weight of heavy birds. Therefore, exploring alternative methods that offer improved efficiency and precision is necessary. One promising solution lies in the application of AI, which has the potential to revolutionize various aspects of poultry production and management, making it an indispensable tool for the modern poultry industry. This study aimed to develop an AI approach based on the FL model as a viable solution for estimating poultry weight. By incorporating expert knowledge and considering key input variables such as indoor temperature, indoor humidity, and feed consumption, FL-based models were developed with different configurations using Mamdani inferences and evaluated across eight different rearing periods in Samsun, Türkiye. This study's results demonstrated the effectiveness of FL-based models in estimating poultry weight. The models achieved varying average absolute error values across different age groups of broilers, ranging from 0.02% to 5.81%. These findings suggest that FL-based methods hold promise for accurate and efficient poultry weight estimation. This study opens up avenues for further research in the field, encouraging the exploration of FL-based approaches for improved poultry weight estimation in poultry farming operations. [ABSTRACT FROM AUTHOR]

In the beginning, Zadeh's 1965 research on fuzzy sets inspired fuzzy logic (FL). FL continues to be comprehended through the estimation, ambiguity, partial reality, and inaccuracy. The integration of system intelligence will be accomplished through soft computing techniques. The study focuses on the widespread FL applications in manufacturing processes and standard computerization approaches. The sugar processing facility with the highest percentage of success in extracting the juice took first place among them. Because of the more significant seasonal component, regularity must be preserved to improve system performance. As a result, the proposed methodology successfully limits the volume or range of the Donnelly channel using the three inputs for the fuzzy controller. However, due to nonlinearities, the amount of cane fibre passing through the sugar mill's carrier fluctuates, which affects the mill's effectiveness. Additionally, the algorithm's use of three fuzzy input controllers to increase cane volume in the Donnelly chute during cane juice extraction results in a critical motor speed for the rake carrier ascribed to the range and quantity of cane on the carrier with the rolling rate. The toolbox function of FL in MATLAB® was used to create the simulation results for the 3-input.

Decision-making trial and evaluation laboratory (DEMATEL) is one of the multicriteria decision-making methods based on asymmetric linguistic comparison matrices that has received a great deal of attention, and it is a widely used method in various fields. One of the drawbacks of DEMATEL is a convergence problem that may occur when the infinite sum of normalized influences does not converge. Based on the observations of some examples, the new concept of DEMATEL, the DEMATEL of a finite sum of influences (FSI DEMATEL), is proposed. Instead of an infinite sum, a finite sum of influences is used in FSI DEMATEL so that the convergence problem is avoided. The advantage is that FSI DEMATEL can handle more decision-making problems than the DEMATEL. It can also be used for fuzzy evaluations. FSI DEMATEL can be used as the multicriteria decision-making method to evaluate the relationships between the factors in many different fields. [ABSTRACT FROM AUTHOR]

This study presents a new way of increasing 3SAT logic programming's efficiency in the Hopfield network. A new model of merging fuzzy logic with 3SAT in the Hopfield network is presented called HNN-3SATFuzzy. The hybridised dynamic model can avoid locally minimal solutions and lessen the computing burden by utilising fuzzification and defuzzification techniques in fuzzy logic. In addressing the 3SAT issue, the proposed hybrid approach can select neuron states between zero and one. Aside from that, unsatisfied neuron clauses will be changed using the alpha-cut method as a defuzzifier step until the correct neuron state is determined. The defuzzification process is a mapping stage that converts a fuzzy value into a crisp output. The corrected neuron state using alpha-cut in the defuzzification stage is either sharpening up to one or sharpening down to zero. A simulated data collection was utilised to evaluate the hybrid techniques' performance. In the training phase, the network for HNN-3SATFuzzy was weighed using RMSE, SSE, MAE and MAPE metrics. The energy analysis also considers the ratio of global minima and processing period to assess its robustness. The findings are significant because this model considerably impacts Hopfield networks' capacity to handle 3SAT problems with less complexity and speed. The new information and ideas will aid in developing innovative ways to gather knowledge for future research in logic programming. Furthermore, the breakthrough in dynamic learning is considered a significant step forward in neuro-symbolic integration. [ABSTRACT FROM AUTHOR]

The idea of fuzzy numbers is determined according to their uncertainty points, which include interval values, triangular, trapezoidal, and heptagonal fuzzy numbers, etc. This article contains two types of fuzzy numbers: the heptagonal fuzzy number (HpFN) and heptagonal interval-valued fuzzy numbers (HpIVFN). For both the HpFN and HpIVFNs, the new distinct rational perspective and viewpoints are presented. First, the membership function (MF) is generalized to the linear and non-linear for observing the symmetric and asymmetric HpFN and HpIVFN. In light of this, α-cut is constructed for both linear and non-linear MF of HpFN and HpIVFN. The various types of HpIVFNs are classified along with the graphical representation. Then, new defuzzification under HpFN is determined using three methods, namely the centroid method, mean of α-cut method, and removal of area methods, and a comparison is demonstrated. A ranking method is also performed for HpFN. Using the proposed defuzzification approach in the weighted aggregates sum product assessment (WASPAS) method, an analysis is conducted to identify the countries with the highest vulnerability to stroke disease. The results indicated that world Bank upper-middle-income countries is significantly affected by stroke disease. The obtained outcome is validated through comparative and sensitivity analyses.