Fuzzy Values Research Articles

Comparison of Infinite Capacity FM/FEk/1 Queuing Performance Using Fuzzy Queuing Model and Intuitionistic Fuzzy Queuing Model with Erlang Service Rates

In this work, we suggest an analytic technique with triangular fuzzy and triangular intuitionistic fuzzy numbers to compute the membership functions of considerable state-executing proportion in Erlang service models. The inter-entry rate, which is Poisson, and the admin (service) rate, which is Erlang, are both fuzzy-natured in this case, with FEk designating the Erlang probabilistic deviation with k exponentially phase. The numeric antecedents are shown to validate the model's plausibility, FM/FEk/1. A contextual inquiry is also carried out, comparing individual fuzzy figures. Intuitionistic fuzzy queueing models that are comprehensible are more categorical than fuzzy queueing models. Expanding the fuzzy queuing model to an intuitionistic fuzzy environment can boost the implementation of the queuing model. The purpose of this study is to assess the performance of a single server Erlang queuing model with infinite capacity using fuzzy queuing theory and intuitionistic fuzzy queuing theory. The fuzzy queuing theory model's performance evaluations are reported as a range of outcomes, but the intuitionistic fuzzy queuing theory model provides a myriad of values. In this context, the arrival and the service rate are both triangular and intuitionistic triangular fuzzy numbers. An assessment is made to find evaluation criteria using a design protocol in which fuzzy values are kept as they are and not made into crisp values, and two statistical problems are solved to understand the existence of the method.

Aczel–Alsina Power Aggregation Operators for Complex Picture Fuzzy (CPF) Sets with Application in CPF Multi-Attribute Decision Making

Complex picture fuzzy sets are the updated version of the complex intuitionistic fuzzy sets. A complex picture fuzzy set covers three major grades such as membership, abstinence, and falsity with a prominent characteristic in which the sum of the triplet will be contained in the unit interval. In this scenario, we derive the power aggregation operators based on the Aczel–Alsina operational laws for managing the complex picture of fuzzy values. These complex picture fuzzy power aggregation operators are complex picture fuzzy Aczel–Alsina power averaging, complex picture fuzzy Aczel–Alsina weighted power averaging, complex picture fuzzy Aczel–Alsina power geometric, and complex picture fuzzy Aczel–Alsina weighted power geometric operators. We also investigate their theoretical properties. To justify these complex picture fuzzy power aggregation operators, we illustrate a procedure of a decision-making technique in the presence of complex picture fuzzy values and derive an algorithm to evaluate some multi-attribute decision-making problems. Finally, a practical example is examined to illustrate the decision-making procedure under the consideration of derived operators, and their performance is compared with that of various operators to show the supremacy and validity of the proposed approaches.

Approximate Solution of Linear Interval Fuzzy Ordinary Differential Equations

Approximate solution of fuzzy initial value problem composed of interval fuzzy ordinary differential equation subject to initial conditions given as fuzzy intervals is the main objective of this work. The considered fuzzy initial value problem was given with Hukuhara definition of the difference and derivatives for interval-valued functions, in which two cases are considered depending on the comparison between the lower and upper functions of the solution derivative. The followed definition for presenting the fuzzy interval is generalized using trapezoidal fuzzy number, while the followed approximated method used in this work is the variational iteration method and its modification to increase the accuracy and the rate of convergence to the exact solution.

A New Method of Solving Fuzzy Linear Systems

In this text, our work focuses on fuzzy linear systems where the number of rows and columns of the left correlation coefficient matrix are not equal. A method to handle a wider range of these systems is introduced by our selected more general definition of fuzzy values. Different expressions for the result are obtained based on generalized inverses for different types of the left-hand side expanded matrix. Finally, we verified our method with five cases where the fuzzy values were defined in a more general range. These cases show that the method is simple and effective whether the equations are consistent or not.

Modified Multi-Criteria Decision Making Methods to Assess Classification Methods

During the last few decades, many academic and professional groups gave attention to adopting the multi-criteria decision-making methods in a variety of contexts for decision-making that are given to the diversity and sophistication of their selections. Five different classification methods are tested and assessed in this paper. Each has its own set of five attribute selection approaches. By using the multi-criteria decision-making procedures, these data can be used to rate options. Technique for order of preference by similarity to ideal solution (TOPSIS) is designed utilizing a modified fuzzy analytic hierarchy process (MFAHP) to compute the weight alternatives for TOPSIS in order to obtain the confidence value of each classifier for each feature selection approach individually. Defuzzification of the fuzzy values to obtain the final criteria weights, the rank function is used. The modification of TOPSIS is assessed in tests using five prediction models (alternatives) and six performance measurements (criteria) to analyze the German credit data sets. Overall the results of the experiment show that the proposed strategies are successful in credit approval data.

Multi‐sensor data fusion for swine house environment evaluation and control

AbstractData fusion from various sensors can obtain more accurate and reliable results than a single monitoring sensor. The data of air quality in the swine breeding environment comes from various sensors; therefore, the data must be fused. In this study, statistical criteria, batch estimation theory, a fuzzy comprehensive evaluation (FCE) method, as well as an improved analytic hierarchy process (IAHP), were combined to provide accurate data for multi‐sensor data fusion modeling. To make a fair comparison, four statistical criteria were employed to process abnormal data gross errors. Besides, an improved batch estimation adaptive weighted fusion method was adopted to, respectively, integrate the data from temperature, humidity, and concentrations of ammonia (NH3), carbon dioxide (CO2), and hydrogen sulfide (H2S) of air quality at the data level. The predetermined weights were given using IAHP combined with the knowledge of experts. To decide the fuzzy membership values of each environmental parameter, we presented a method to establish fuzzy membership functions and devised a calculation method for related parameters. The gaussmf type membership function was employed to build a global fusion model that fits well with IAHP–FCE. The IAHP–FCE method was exploited to comprehensively integrate multi‐sensor data at the decision level. Experimental results show that the presented method can improve the data accuracy and provide decision results and scientific basis for the precision control of swine breeding environment.

Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network

This paper proposes an innovative approach to improve the performance of 3SAT logic programming in the Hopfield neural network. The merged structures of the 3SAT and Hopfield network have specific weaknesses, one of which is that, at times, the system attained local minimum solutions rather than global minimum solutions. A new model of integration randomised alpha-cut fuzzy logic with 3SAT in the Hopfield network is built to convey information more effectively. 3SAT and fuzzy logic can work together to solve Hopfield networks' combinatorial optimisation issues. Procedures of fuzzifying and defuzzifying the neurons might ease the computational burden of determining the correct neuron states. Until the proper neuron state is established, unsatisfied neuron clauses will be modified using a randomised alpha-cut approach in the defuzzifier step. An incorporated design built a random approach to select the alpha-cut values of 0.1, 0.25, and 0.5. At this point, a fuzzy value switches into a crisp output back through the defuzzifier process. Based on the results obtained, the proposed hybrid strategy effectively improves the indicators of RMSE, SSE, MAE, MAPE, global minima and total computational time. A computer-generated data set was used to measure how well the hybridised techniques performed. The performance of the proposed network was trained and validated using Matlab 2020b. The results are significant because this model significantly affects how successfully Hopfield networks merged with fuzzy logic can tackle the 3SAT challenges. The obtained data and ideas will help to create novel approaches to data collection for upcoming logic programming exploration.

A hydroponic greenhouse fuzzy control system: design, development and optimization using the genetic algorithm

Aim of study: The design and development of a hydroponic greenhouse fuzzy control system. Area of study: The evaluation was performed using experimental data obtained from the literature. The construction and evaluation of the fuzzy control hydroponic greenhouse system was carried out in a greenhouse in Tehran, Iran. Material and methods: The greenhouse environmental conditions, including temperature, humidity, and carbon dioxide, were controlled. The design of a fuzzy controller begun with the selection of linguistic variables, process status, and input and output variables. The fuzzy control system consisted of three modules: 1) fuzzy module, 2) cost function, and 3) genetic algorithm for the adjustment of the greenhouse environmental conditions.The next step was to select a set of linguistic rules and the type of fuzzy inference process. The rules were set once, and the fuzzy set and output value needed to be specified after the inference, along with the development of a non-fuzzy strategy. Main results: The mean temperatures provided by the fuzzy control system during the day and night were 34.25°C and 23.22°C, respectively, which were improved to 31.17°C and 21.96°C after optimization. The mean humidity was 39.4% and 56.5% during the day and the night, respectively, which turned 60.22% and 74.59% after optimization. The control system also achieved desirable conditions in terms of CO2 amount. Research highlights: The results showed that the measured values of temperature and relative humidity of the greenhouse were improved after optimization with genetic algorithm.

Multi-attribute decision making based on the q-rung orthopair fuzzy Yager power weighted geometric aggregation operator of q-rung orthopair fuzzy values

Multi-attribute decision making based on the q-rung orthopair fuzzy Yager power weighted geometric aggregation operator of q-rung orthopair fuzzy values

Identification of Acral Melanoma using Fuzzy Algorithm compared with Convolutional Neural Network for improved accuracy using Dermoscopic Images

Aim: Acral melanoma detection using a fuzzy method vs. convolutional neural network CNN utilizing dermoscopic pictures. Materials and Methods: To assess and compare the acral melanoma detection, the researchers used the fuzzy algorithm and the Convolutional neural network algorithm. The overall sample size is 40 due to the utilization of 20 samples acral melanoma is identified by evaluating the effectiveness with pretest power of 80% (G-power) α=0.05, confidence level 95%. Result: The proposed fuzzy algorithm helps in increasing the higher accuracy compared to Convolutional Neural Network CNN with improved accuracy of the fuzzy algorithm algorithm is 80% and the CNN algorithm is 75 %. The accurate rate is 80with data features found in fuzzy algorithms and each algorithm has different precision. Conclusion: This study shows a higher accuracy for the fuzzy algorithm higher significant value than the convolutional neural network.

The Principle of Organization of Motivational Behavior and Automatic Goal Setting of Autonomous Intelligent Mobile Systems

The expediency of organizing the motivational behavior of autonomous intelligent mobile systems focused on solving various complex problems in unstable a priori undescribed problematic environments is substantiated. That need is due to the fact that this type of goal-seeking behavior allows intelligent systems of various purposes to ensure safe and efficient activity in unstable operating conditions. A model of knowledge representation of autonomous intelligent mobile systems is proposed without regard to a specific subject area and built on the basis of active fuzzy semantic networks. In such networks, vertices are labeled with slots that have many characteristics, which enables in the process of goal-seeking activity to label active vertices with objects and events occurring in the problematic environment that meet their requirements. Edges in such networks are labeled with generalized fuzzy values of corresponding conditions that must be met in a problematic environment between an autonomous intelligent mobile system, various objects, and occurring events. This model of a formalized description of various situations and subsituations of the problematic environment allows intelligent systems to adapt to a priori undescribed operating conditions and, on this basis, automatically plan goal-seeking activities under conditions of uncertainty. To control the motivational behavior and self-organization of autonomous intelligent mobile systems, tools have been developed intended to identify threats to productive activities that arise in a problematic environment. A generalized production-based model of knowledge representation has been built without regard to a specific subject area, which allows intelligent systems to quickly respond to various types of security threats that arise in a problematic environment and maintain operability in the process of performing complex tasks. In conclusion, one of the effective ways of further development of the proposed principle of organizing the safe and efficient operation of autonomous intelligent mobile systems is outlined, which is related to the management of their collective activities in the process of performing a complex task with changes occurring spontaneously in a problem environment, accompanied by the emergence of various types of threats in it that prevent their effective operation.

Optimization Fuzzy Geographically Weighted Clustering with Gravitational Search Algorithm for Factors Analysis Associated with Stunting

Stunting is a significant threat to the quality of human resources in Indonesia because stunting does not only involve physical growth disorders but can also cause children to be vulnerable to disease and experience disorders of brain development and intelligence. Many factors cause stunting, not only malnutrition in pregnant women and toddlers. Grouping can be done to make it easier to see the characteristics of the factors causing stunting in Indonesia. The grouping is done based on the similarity of the characteristics of the factors causing stunting in each province. This study used Fuzzy Geographically Weighted Clustering (FGWC) with Gravitational Search Algorithm (GSA) to group and assess the best cluster using the Partition Coefficient validity index, Classification Entropy, Separation Index, Xie & Beni's Index, and IFV Index. Furthermore, a difference test was conducted to determine the dominant factor causing stunting in the formed cluster. The results showed that the FGWC-GSA gave the best clustering results on the fuzziness value of 2 with the number of clusters 2. Cluster 1 consisted of 16 provinces, and cluster 2 consisted of 18 provinces. Based on the T-test, the variables of infants who received exclusive breastfeeding had significant differences between clusters. Therefore, cluster 2 is a cluster that has dominant problems related to exclusive breastfeeding.

Behavior Analysis of Feeding Unit of a Paper Industry in Fuzzy Environment

The feeding unit is an important functional part in the paper industry that requires the development of effective maintenance plan based on its reliability analysis. This study analyzes reliability of the considered unit employing trapezoidal fuzzy numbers in fuzzy [Formula: see text]–[Formula: see text] methodology with Petri net modeling. In this analysis, the collected crisp input data of each subsystem has been fuzzified using trapezoidal fuzzy numbers. Then fuzzy values of several reliability factors of feeding unit, namely, failure rate, repair time, reliability, availability, etc. at ±15%, ±25% and ±40% spread levels have been calculated. Further, the obtained fuzzy values of reliability factors have been defuzzified to analyze system behavior of feeding unit. The analysis is useful for plant maintenance and operation management to understand the behavior of feeding unit in a more realistic manner.

Precision Nitrogen Fertilizer and Irrigation Management for Apple Cultivation Based on a Multilevel Comprehensive Evaluation Method of Yield, Quality, and Profit Indices

Precise and efficient fertilizer and irrigation management is critical for apple production in the Loess Plateau, China. In this study, we established three levels of nitrogen application and irrigation in nine treatments in an apple orchard based on a completely randomized block design. Then, we analyzed different apple indicator responses to nitrogen and irrigation and their related interactions. More importantly, we used the multilevel comprehensive fuzzy evaluation value (MFCE) method to combine the weights of all indicators to obtain the comprehensive growth indicators for apples. Finally, we analyzed the effect of nitrogen and irrigation coupling on the comprehensive growth of apples and then determined the optimal interval for nitrogen application and irrigation. The results indicated that an increase in the amount of irrigation was beneficial for apple yield, but excessive nitrogen fertilizer application significantly reduced apple yield. The apple indicators were not sensitive to irrigation and nitrogen application and their related interactions; they were mainly controlled by the apple cultivar. On the other hand, an increase in the amount of nitrogen fertilizer or a reduction in the amount of irrigation could improve apple quality. The results of the comprehensive evaluation showed that the T5 treatment was most beneficial for the comprehensive growth of apples. To obtain the optimal interval for nitrogen application and irrigation more precisely, we used multiple linear regression based on the MFCE values of apples, nitrogen, and irrigation in R language. Nitrogen and irrigation showed a positive effect on the comprehensive growth of apples when the irrigation amount was low. However, nitrogen application and irrigation had a negative effect on the comprehensive growth of apples when the irrigation level was high. After optimization, the optimal nitrogen application and irrigation amounts were 170.5–189.5 kg·hm−2 and 38.4–42.7 mm, respectively. We recommend using this irrigation and fertilizer management scheme for apple orchards in China’s Loess Plateau.

Solution for a System of First-Order Linear Fuzzy Boundary Value Problems

In this paper, we consider homogeneous and non-homogeneous system of first order linear fuzzy boundary value problems (SFOLBVPs) under granular differentiability. Using the concept of horizontal membership function, we introduced the notion of first order granular differentiability for n-dimensional fuzzy functions. We present granular integral and its properties. Theorems on the existence and uniqueness of solutions for homogeneous and non-homogeneous SFOLFBVPs are proved. We develop an algorithm for solution of non-homogeneous SFOLBVPs under granular differentiability. We provide some examples to illustrate the validity of the proposed algorithm.

A Framework for Susceptibility Analysis of Brain Tumours Based on Uncertain Analytical Cum Algorithmic Modeling.

Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients' susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.

Quantification of body ownership awareness induced by the visual movement illusion of the lower limbs: a study of electroencephalogram and surface electromyography.

The visual movement illusion (VMI) is a subjective experience. This illusion is produced by watching the subject's motion video. At the same time, VMI evokes awareness of body ownership. We applied the power spectral density (PSD) matrix and the partial directed correlation (PDC) matrix to build the PPDC matrix for the γ2 band (34-98.5Hz), combining cerebral cortical and musculomotor cortical complexity and PPDC to quantify the degree of body ownership. Thirty-five healthy subjects were recruited to participate in this experiment. The subjects' electroencephalography (EEG) and surface electromyography (sEMG) data were recorded under resting conditions, observation conditions, illusion conditions, and actual seated front-kick movements. The results show the following: (1) VMI activates the cerebral cortex to some extent; (2) VMI enhances cortical muscle excitability in the rectus femoris and medial vastus muscles; (3) VMI induces a sense of body ownership; (4) the use of PPDC values, fuzzy entropy values of muscles, and fuzzy entropy values of the cerebral cortex can quantify whether VMI induces awareness of body ownership. These results illustrate that PPDC can be used as a biomarker to show that VMI affects changes in the cerebral cortex and as a quantitative tool to show whether body ownership awareness arises.

Dynamic optimization of open-pit coal mine production scheduling based on ARIMA and fuzzy structured element

A dynamic optimization method was created to address the production schedule issue in an open-pit coal mine while taking into account the characteristics of the fuzzy structured element. The fuzzy mining capacities of all “geologically optimal push-back bodies” were then examined using the moving cove method. One of the most crucial elements in the process of open-pit coal mine production scheduling optimization is coal pricing. As a result, this work also presents a dynamic optimization technique for production scheduling that incorporates the prediction of economic time series and the generation of dynamic economic indices. An appropriate time series model is created to forecast the future coal price based on previous data on coal prices. The prediction results are used in the calculation of optimal mining body generation to dynamically obtain the optimal production scheduling model. The Baorixile Open-pit Coal Mine in China’s Inner Mongolia Autonomous Region is using this method. The Autoregressive Integrated Moving Average Model ARIMA is constructed to anticipate the coal price in the future 23 years by evaluating and processing the coal price from 2009 to 2022, and the ideal production scheduling scheme of the mine economics is afterwards identified. The ideal fuzzy coal mining volume, the potential production life, and the fuzzy total net present value (NPV) of the annual production scheduling are all provided at the same time. The optimization findings can better give fundamental support for mine design and future production since the fuzzy problem is accurately expressed by correct formulations.

A new model based on the extended COPRAS method for improving performance during the accreditation process of Indian Higher Educational Institutions

AbstractThe standard of any country in the global world is dependent highly on the intellect content of the people. This intellect content is in turn dependent exclusively upon the quality of the higher education in the country. The aim of this research work is to perform an empirical evaluation to rank various quality parameters suggested by the National Board of Accreditation. This national board was established in India in 1994 by the All India Council for Technical Education with the main motive of assessing the quality of various Higher Educational Institutions. It provides marks to institutions out of 1000 on the basis of 10 parameters, which are further subdivided into 75 subparameters. This research work helps to guide educational institutions to access their weak points during and before applying for this accreditation to recover from them timely and in the most effective manner. An extended model of fuzzy COPRAS (COmplex PRoportional ASsessment) is proposed as an improved multicriteria decision‐making approach to first identify and then set preferences to help institutions improve their data related to accreditation key indicators. The work is divided into four steps. First, all the critical evaluation factors for the accreditation process are identified in consultation with senior, experienced and qualified academicians. They are then converted into fuzzy triangular numeric values and crisp weights. Lingual values and corresponding fuzzy weights of various preference key indicators are then identified and converted into crisp weights. Further critical factors are divided into price and profit values. Finally, key indicators are ranked using extended fuzzy COPRAS. A comparative ranking analysis is generated as end result through fuzzy COPRAS, TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and WASPAS (Weighted Aggregated Sum Product Assessment) approaches, which shows that the proposed model generates better ranking results. The proposed model is applicable to any accreditation process with a varied set of parameters and their subparameters.

Optimal Strategy Selection under Fuzzy Environment for Strategic Planning Methodology Selection: A SWOT Approach

Over the past few years, many researchers apply SWOT analysis in combination with other methodologies in order to obtain enhanced strategic planning frameworks and tackle the weaknesses of applying only one approach. To overcome some shortcomings of SWOT analysis, an integrated SWOT analysis, fuzzy net present value (FNPV), and fuzzy Shapley value (FSV) methodology was proposed in the present study as a conceptual framework to cost-effectively achieve and manage the objectives for efficiency, capacity, and availability as well as to provide practical insights on how to formulate strategic decisions. The fuzzy net present value method was used to determine the strategic importance of the SWOT matrix. Taking into account the theoretical foundation of the cooperative games, the fuzzy Shapley value was used to determine the optimal combination of SWOT matrix strategies to effectively overcome the ambiguities and uncertainty of real-world conditions. In the game, any type of strategy (WT, WO, ST, and SO) was considered as a player who had partial freedom to make some choices. According to the results of the present study, the combination of the WT1 WO1 ST2 SO2 strategy was selected as the most appropriate strategy combination to be implemented in the organization.