Application Of Fuzzy Logic Research Articles

Decision Fusion Using Fuzzy Logic Type 1 for Two Aviation Scenarios

In this paper we discuss some aspects of decision fusion using fuzzy logic of type 1 for two aviation scenarios. We study the fuzzy inference system/engine and briefly discuss the MATLAB-GUI based tool developed by the authors to evaluate any existing or new fuzzy implication functions. Two examples: i) formation flight, and ii) flying along an air lane are numerically evaluated and the performance of decision fusion systems incorporating fuzzy logic is illustrated. The results indicate that some existing as well as new implication functions, devised by the authors, work very well for the examples considered. This opens up new possibilities of applications of fuzzy logic based decision fusion to varieties of aerospace/aviation problems in data fusion, decision making, and decision fusion.

Investigation of enterprise economic management model based on fuzzy logic algorithm

With the development of modern society and economy, modern business models have changed. Enterprise management is no longer the main function, which requires new management concepts to coordinate production and operation and create new management models. Corporate economic management is one of the main tasks of corporate governance. In order to improve corporate governance, the effective corporate economic evaluation model must be established. The advantage of fuzzy logic algorithm is that it can effectively express fuzzy data and is easy to understand, which is more transparent. The application of fuzzy logic algorithm in the research of enterprise economic management can fully reflect the advantages of fuzzy logic algorithm and improve enterprise governance. In this paper, the fuzzy logic algorithm was used to study the economic management model of enterprises. This paper first introduced the enterprise economic management model, including the definition, characteristics and shortcomings of the enterprise economic management model. The performance evaluation methods of enterprise economic management were introduced, including the clarity of performance evaluation objectives, the optimization of performance evaluation means and the feedback of performance evaluation results. Finally, the fuzzy logic algorithm was used to evaluate the performance of enterprise economic management, and the indicators and performance evaluation model of enterprise economic management were established. In the experimental part, the fuzzy logic algorithm was used to evaluate the economic management of four enterprises. The experimental results showed that Enterprise A had the highest ability to evaluate the economic management of enterprises, and the comprehensive evaluation result of economic management was above 0.9, so it had a very high enterprise economic management ability. The application of fuzzy logic to the study of enterprise economic management could directly show the advantages and disadvantages of the enterprise economic management model through quantitative analysis of the enterprise economic management model. This enabled enterprises to understand the current economic management situation and improve it based on the facts, which helped enterprises to increase their economic management ability.

Inventory Management Under Uncertainty Condition with Fuzzy Logic: A Literature Review

Fuzzy set theory, or fuzzy logic, has been in use in inventory systems since the 1980s, with vaguely defined, ill-defined, or imprecise values, or decisions based on individual subjective beliefs. Provides a framework for describing the parameters that can be passed. There are many uncertainties in inventory management that can arise due to many things, such as: Order Changes, Random Supplier Capabilities, or Unexpected Events. Fuzzy logic as a method of inventory control that provides a framework by considering parameters that are vague or poorly defined, or whose values are imprecise and determined based on individual subjective beliefs make it available. The purpose of this white paper is to review previous studies using fuzzy logic in inventory management to see the uncertainty variables used. The method used is a thematic analysis of articles on the application of fuzzy logic in various industries. The results of the review show that variables like fuzzy logic make it easy to obtain results from uncertainty variables that can be applied to industrial activities to enable manufacturing activities to be carried out effectively and efficiently increase.

Bibliometric Analysis on the Application of Fuzzy Logic into Marketing Strategy

Fuzzy marketing considers the degree to which a customer belongs to specific segments and subsequently allows them to be targeted with messages that engage them emotionally. To better understand the application and importance of fuzzy logic in marketing strategy, we developed a systematic literature review with bibliometric analysis to examine a sample of 96 studies from the SCOPUS database to identify research activity on this topic until December 2022. It was possible to create a connection between these concepts, marketing and fuzzy logic, to increase the efforts of marketing professionals. For instance, the results show that adopting approaches such as fuzzy marketing or the fuzzy marketing mix model enhances the company’s capability to build stronger customer relationships, enhance profitability, and improve marketing performance.

Building models to optimize vehicle downtime in multimodal transportation

Sustainable development has become the main focus of transport policy and planning around the world. One of the practical goals when performing multimodal transportation is the optimization of logistics costs. That is, the object of research is the process of multimodal transportation. Empirical research shows that the problem of optimization of transportation costs can be solved by different methods. But the result will be similar. In the given approach, only one component of the transportation process is subject to optimization, which is the overload time. The solutions are based on the method of mass service theory and the method based on fuzzy logic. With the help of these methods, based on practical data, time parameters were calculated that characterize overloading from railway transport to road transport. The application of the method of a weakly formalized process in relation to transport logistics was considered, taking into account not only quantitative estimates but also qualitative, vaguely defined criteria that do not lend themselves to formalization, and the relationships between them. The model was developed for further research of this process, prediction of its behavior, optimization of functioning. It is based on the technology of fuzzy sets. The results obtained using the agent model based on the mass service network and the model based on fuzzy logic differ within the permissible specified limits of no more than 5–7 %. The application of fuzzy logic in the logistics of multimodal transportation is relevant and gives the best results compared to traditional methods of the theory of mass service systems. The article includes comparisons that reflect the advantages of the proposed approach. The obtained results are of a practical nature and can be used to make a decision on choosing a route and/or when transferring from one mode of transport to another

Two-Stage Fuzzy Traffic Congestion Detector

This paper presents a two-stage fuzzy-logic application based on the Mamdani inference method to classify the observed road traffic conditions. It was tested using real data extracted from the Padua–Venice motorway in Italy, which contains a dense monitoring network that provides continuous measurements of flow, occupancy, and speed. The data collected indicate that the traffic flow characteristics of the road network are highly perturbed in oversaturated conditions, suggesting that a fuzzy approach might be more convenient than a deterministic one. Furthermore, since drivers have a vague notion of the traffic state, the fuzzy method seems more appropriate than the deterministic one for providing drivers with qualitative information about current traffic conditions. In the proposed method, the traffic states are analysed for each road section by relating them to average speed values modelled with fuzzy rules. An application using real data was carried out in Simulink MATLAB. The empirical results show that the proposed study performs well in estimation and classification.

Implementation of the Mamdani Fuzzy Method to Evaluate the Performance of Lecturers in the Research Field

Today, information technology, especially soft computing technology, has grown very rapidly. One of the soft computing technologies that has been widely developed is fuzzy logic, because it can be used to measure various phenomena that are ambiguous, disguised or fuzzy. One of the research topics that uses the application of fuzzy logic is the assessment system in the field of research. Research by Lecturers at the University of Graha Nusantara Padangsidimpuan in Simlitabmas Data Still in the Guidance Category to upgrade to the Middle Category UGN Padangsidimpuan lecturers are challenged to be able to develop, devote, and apply the knowledge needed in research. For that we need an application that can be used to calculate and record the performance of Lecturers on the resulting Research. The purpose of this study is to apply fuzzy logic with the Mamdani method in assessing the research performance of lecturers at the University of Graha Nusantara Padangsidimpuan. This research uses Mamdani Fuzzy Logic. Fuzzy Mamdani method is a way to map an input space into an output space. This method is a mathematical framework used to represent uncertainty, ambiguity, imprecision, lack of information, and partial truth. The stages of research using the Mamdani method are Creating Input Variables taken from Sinta-accredited Articles, Simlitabmas Grant Articles and Articles in International and National Journals. Finding the Max-Min Value of Each Variable. Creating a fuzzy set using the Mamdani method. Creating Assertions with Defuzzification using Matlab.

The Application of Fuzzy Logic in Optimization Pulp in Pt.Toba Pulp Lestari, Tbk With the Mamdani Method

The Application of Fuzzy Logic in Optimization Pulp in Pt.Toba Pulp Lestari, Tbk With the Mamdani Method

An Application of Fuzzy Logic to Bank Ranking: A study of the Banking Sector in Bangladesh

Creditors, investors, policymakers, and other stakeholders are all significantly impacted by banks’ performance ratings since these ratings affect how well banks are able to compete in the banking industry, which is crucial for the growth of this industry. The criteria used to evaluate a bank’s success in the banking industry are nebulous and vague. Consequently, it is no longer possible to precisely determine the state of a bank using the analytical method. Furthermore, there is no standard framework that can evaluate private commercial banks using the CAMELS criterion and eliminates ambiguity that we can witness in Bangladesh. The literature shows that two multi-criteria decision-making procedures, FAHP and TOPSIS, are employed in many countries to rank banks according to the CAMELS criteria. However, in Bangladeshi private commercial banks, we have never used such models using the CAMELS criteria. In order to assess the performance of Bangladeshi private commercial banks, this study aims to propose a Fuzzy Multi-Criteria Decision Model (FCDM) that can handle uncertain and ambiguous data. The CAMELS (Capital Adequacy, Asset Quality, Management Efficiency, Earnings, Liquidity, and Sensitivity to Market Risk) criteria are used to analyze and rank the ten commercial banks in Bangladesh. The suggested model incorporates the Fuzzy Analytic Hierarchy Process (FAHP) and Technique of Order Performance by Similarity to Ideal Solution (TOPSIS) methodologies. The weights are input into the TOPSIS algorithm to rank the Banks after determining the weight vector of the CAMELS criteria based on the opinions of experts using FAHP. The outcome displays the ten Bangladeshi commercial banks’ final rankings.

Machine learning - a novel approach to predict the porosity curve using geophysical logs data: An example from the Lower Goru sand reservoir in the Southern Indus Basin, Pakistan

Porosity estimation is one of the essential issues in oil and natural gas industries to evaluate the reservoir characteristics properly. Therefore, it is imperative to predict porosity with the optimum way to reduce logging tests. In this paper, fuzzy logic (FL) and neural networks (NNs) are considered effective approaches to predict the Lower Goru sand reservoir's porosity curve. The input dataset for the study contained four known logs, gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) of five wells drilled. For the fuzzy logic model, ten bins were used. The closeness of fit (Cfit) curves were computed using the most likely and second most likely curves. The weighted average final probability Pi, or the most likely solution, was also calculated. The curve histogram distribution and set of curve bin distribution cross plots were built using a fuzzy model. In the fuzzy logic model, the Gaussian membership function provided the optimum match for the examined geophysical log data. Fuzzy logic models indicate Cfit values ranging from 94 to 100% for Sawan-01, Sawan-02, Sawan-03, Sawan-07, and Sawan-08, with standard deviations of 1.248, 1.241, 1.254, 1.336, and 1.374, respectively. The neural networks model was trained using the backpropagation (BP) algorithm. The neural networks model has a Cfit_nn of 88%–100% across five wells with standard deviations of 0.016, 0.014, 0.015, 0.017, and 0.018. The results show that the predicted modeling evaluations using fuzzy logic and neural networks techniques fit the geophysical log data quite well. The multiple linear regression (MLR) assessments were conducted using the same geophysical log datasets of five studied boreholes for comparison. The coefficients of determination (R2) for the fuzzy logic (PHIT_ml) and neural networks (PHIT_nn) models were 0.960127, and 0.973039, respectively, whereas the values of the PHIT curve for multiple linear regression (PHIT_mlr) 0.926329. The high R2 values show that fuzzy logic and neural networks are more effective methods for PHIT curve prediction than the multiple linear regression approach. The relevant correlation was derived by comparing synthetic log values to actual log values. The evaluations between recorded and predicted values applying the two distinct approaches fuzzy logic and neural networks revealed that both are effective at synthesizing PHIT logs. The confirmation of this efficiency was further verified by the low values obtained in the root mean square error (RMSE) analysis. The study conducted on the Sawan Gas Field wells revealed that both fuzzy logic and neural networks are reliable approaches for predicting the PHIT curve. By using a composite of GR, RHOB, NPHI, and DT logs, these techniques can provide a realistic fit for both actual and synthesized PHIT curves. The findings of this study suggest that the implementation of these methods can contribute to the improvement of hydrocarbon exploration and production in the region by reducing uncertainty in predicting the PHIT curve. Moreover, the methods used in this study have the potential for wider application beyond the Sawan Gas Field. These methods can be applied globally to predict the PHIT curve and evaluate the reservoir prospects. The successful application of fuzzy logic and neural networks in this paper provides a solid foundation for future research on using machine learning techniques in reservoir characterization and modeling.

Application of Fuzzy Logic in Artificial Intelligence

Abstract: In 1965, Lotfi Zadeh created fuzzy logic as a development of Boolean logic in light of the numerical hypothesis of fuzzy sets, which is a speculation of conventional set hypothesis. Fuzzy logic offers a great deal of adaptability for exploiting reasoning, adding an idea of degree when checking states and allowing states to be other than obvious or misleading. , taking into account possible errors and weaknesses. We begin by giving a brief overview of fuzzy logic before talking about its application to artificial and computational intelligence. Then, with a focus on fuzzy expert systems, we discuss cutting-edge applications of fuzzy logic, with one representative example being thoroughly examined. The article's conclusion makes recommendations for how fuzzy logic and artificial intelligence might complement one another. We focus our endeavors on few plainly characterized sub-spaces of artificial intelligence, staying away from scattering, and keeping a good arrangement between essential examination and applications.

Application of interval type-2 fuzzy logic and type-1 fuzzy logic-based approaches to social networks for spam detection with combined feature capabilities.

Social networks are large platforms that allow their users to interact with each other on the Internet. Today, the widespread use of social networks has made them vulnerable to malicious use through different methods such as fake accounts and spam. As a result, many social network users are exposed to the harmful effects of spam accounts created by malicious people. Although Twitter, one of the most popular social networking platforms, uses spam filters to protect its users from the harmful effects of spam, these filters are insufficient to detect spam accounts that exhibit new methods and behaviours. That's why on social networking platforms like Twitter, it has become a necessity to use robust and more dynamic methods to detect spam accounts. Fuzzy logic (FL) based approaches, as they are the models such that generate results by interpreting the data obtained based on heuristics viewpoint according to past experiences, they can provide robust and dynamic solutions in spam detection, as in many application areas. For this purpose, a data set was created by collecting data on the twitter platform for spam detection. In the study, fuzzy logic-based classification approaches are suggested for spam detection. In the first stage of the proposed method, a data set with extracted attributes was obtained by applying normalization and crowdsourcing approaches to the raw data obtained from Twitter. In the next stage, as a process of the data preprocessing step, six attributes in the binary form in the data set were subjected to a rating-based transformation and combined with the other real-valued attribute to create a database to be used in spam detection. Classification process inputs were obtained by applying the fisher-score method, one of the commonly used filter-based methods, to the data set obtained in the second stage. In the last stage, the data were classified based on FL based approaches according to the obtained inputs. As FL approaches, four different Mamdani and Sugeno fuzzy inference systems based on interval type-1 and Interval Type-2 were used. Finally, in the classification phase, four different machine learning (ML) approaches including support vector machine (SVM), Bayesian point machine (BPM), logistic regression (LR) and average perceptron (Avr Prc) methods were used to test the effectiveness of these approaches in detecting spam. Experimental results were obtained by applying different FL and ML based approaches on the data set created in the study. As a result of the experiments, the Interval Type-2 Mamdani fuzzy inference system (IT2M-FIS) provided the highest performance with an accuracy of 0.955, a recall of 0.967, an F-score 0.962 and an area under the curve (AUC) of 0.971. However, it has been observed that FL-based spam models have a higher performance than ML-based spam models in terms of metrics including accuracy, recall, F-score and AUC values.

Application of Fuzzy Logic and SNA Tools to Assessment of Communication Quality between Construction Project Participants

The purpose of this research is to study the communication network among the participants in a construction project. The proposed approach contains the solving of the following tasks: identification of the key factors that affect the communication quality; calculation of the “communication quality” indicator between each pair of participants using fuzzy logic; and evaluation of the key measures of centrality using the tools of a Social Network Analysis. Solving the set tasks can help break the problem of the low level of efficiency of construction project deliveries. This study was conducted when the design stage was already complete and the construction project had 17 participants. We used the following factors influencing communication quality: timeliness, understanding, bidirectionality, use of IT tools in communication acts and adequacy. The use of fuzzy logic tools can minimise the subjectivity of the qualitative data obtained from the questionnaires. The analysis of five centrality measures showed that “Project manager”, “Chief engineer”, “Construction site manager”, “Construction works manager” and “Architect” had the greatest values in comparison with other network participants. On the one hand, this organisational structure of communication can be considered correct because the main information flows through the project managers, who make the decisions. On the other hand, this indicates a significant dependence of the construction project on these participants and, in the event of certain risk events, may lead to a decrease in the efficiency of its implementation. The methods presented in this article can be used in project management to analyse a real communication network.

Canonical triangular interval type-2 fuzzy set linguistic distribution assessment TODIM approach: A case study of FMEA for electric vehicles DC charging piles

Linguistic distribution assessment (LDA) plays a prominent role in describing the subjective uncertainties and objective distribution of decision-maker (DM) opinions. Although there have been several studies on LDA, few of these studies have paid attention to the different understandings of language semantics provided by DMs. To address the issue, type-2 (T2) fuzzy sets (T2FSs) are a well-known approach because T2FSs can accurately model the understanding of individuals with linguistic uncertainty. A trapezium (or trapezoidal) interval type-2 fuzzy set (TrIT2FS) is a common application of T2 fuzzy logic. Accordingly, this paper discusses a novel LDA called a canonical triangular interval type-2 fuzzy set (CTriIT2FS)-LDA in a T2 fuzzy environment. The CTriIT2FS-LDA combines a CTriIT2FS simplified from a TrIT2FS with LDA, which can simplify the computation while retaining their advantages. To facilitate the calculation of the CTriIT2FS-LDA, the basic operations of the CTriIT2FSs and the CTriIT2F-LDA, such as the defuzzied (crisp) function, distance measurement, and entropy measurement, are defined. Based on the TODIM approach, a CTriIT2F-LDA-TODIM is developed for FMEA for interval type-2 fuzzy environments. Furthermore, the proposed approach is applied in a practical case for FMEA for the electric vehicle DC charging piles of the State Grid Corporation of China.

Fuzzy Neural Network with Ordered Fuzzy Numbers for Life Quality Technologies

The general goal of the research in this article is to devise an artificial neural network that requires less computational power than an ordinary one for assessing overall life satisfaction—a term often referred to as quality of life (QoL). The development of the mentioned ANN was possible due to the application of fuzzy logic, especially ordered fuzzy numbers (OFN). Research on the appliance of OFN aims at different issues such as the detection of an attack on a computer network, the anticipation of server load, management of multiplexing of data transmission paths, or transmission error rate forecasting that allows the improvement of the quality of life. It occurs due to, for instance, reduced energy demand, savings through better data transmission, and the distribution of computers’ power used in the cloud. Finally, the application of OFN on single neurons of a deep ANN allows achieving a network that is able to solve the same problem as a normal network, but with a lower number of neurons. Such networks in the future may be implemented easier in small solutions, such as solutions for the Internet of Things to improve the quality of human life. This approach is unique and has no equivalent in the literature. Due to the application of OFN in an ANN, fewer requirements for network architecture were needed to solve the same problems, and as a result, there is less demand for processor power and RAM.

IoT and Fuzzy Logic Applied to Smart Public Hall for COVID-19 Monitoring System

Purpose: Since the breakout of COVID-19 pandemic, various preventive measures have been put in place by WHO to prevent the spread of this disease. However, some people are unaware or less likely to follow rules regarding hygiene, physical distancing, properly wearing of face mask and body temperature measurement. One of the best solutions to this challenge is the introduction of the internet of things (IoT) technology to assist in implementation of the preventive measures. This paper presents an IoT-enabled solution that uses Fuzzy logic controller to assess the risks of being COVID-19 infected and monitor environment conditions in the public hall to limit the spread of the coronavirus. 
 Methodology: The proposed model employed sensors to measure in real-time the body temperature, hand sanitization, wearing of face mask, room ventilation and IP camera controlled by Fuzzy logic controller for decision making. In addition, it uses raspberry-pi for processing and data transmission to the cloud, liquid crystal display (LCD) for displaying data and web application was developed for user interface. The resulting sensor measurements were simulated using MATLAB software and the system made automatic decisions.
 Findings: A prototype was implemented effectively and the results obtained from the system were fast, accurate, efficient and cost effective when compared to other commercially available systems.
 Unique Contribution to Practice: The actual practice for implementing the preventive measures require the presence of the health care personnel (HCP) which is time consuming and risky for HCP. Therefore, this system works autonomously and effectively in monitoring and controlling the implementation of the COVID-19 preventive measures in the public hall.

The Application of Fuzzy Logic With the Mamdani Method to Determine the Risk Zone for The Spread of Covid-19 in North Sumatra

The method fuzzy is one part of the fuzzy inference system that is useful for drawing conclusions or the best decision in uncertain problems. Currently, about 200 countries in the world are experiencing the Covid-19 pandemic. Various policies were carried out to overcome this. In addition to the health protocols that are always implemented, the community must also know the conditions of the area where they live so that they can anticipate any activities carried out. The method is used fuzzy to determine the risk zone for the spread of Covid-19 in North Sumatra Province based on 3 variables, namely positive cases, suspected cases, and dead cases. The data used in this study is weekly Covid-19 data from March 2021 to July 2021 for 4 selected areas, namely Medan City, Pematang Siantar City, Simalungun Regency, and Central Tapanuli Regency. All variables are represented using triangular curves and their membership functions are determined. Then the implication function of the min is used to determine the rules used. From the results of the implication function, the composition between all the rules obtained is carried out by taking the maximum rule then that value is used to modify the fuzzy area so that a new membership function is obtained. The method is used centroid to obtain a crisp or the final result is the value of the risk zone. This research also utilizes the Matlab to calculate the results. The results from the use of the Mamdani method will be compared with the real risk zone data so as to produce a 75% average percentage similarity for the data for 4 regions in North Sumatra so it can be concluded that the model made is good enough to determine the risk zone for the spread of Covid-19 in North Sumatra Province.

Application of fuzzy logic in evaluating the authenticity of hadith and narrators

Hadith in Islam is the Arabic word used to represent the traditions and sayings of the prophet. These traditions are normally categorised into four levels depending on their degree of authenticity. This accepted ranking system ignores the categories that might fall between these four ranks. Traditionally, hadith scholars apply a multi-criterion to conclude the degree of authenticity, which mainly depends on their training, experience, and various information reported about the hadith narrators. The research aims to use an arithmetic decision-making approach to judge the degree of validity and degree of authenticity of hadith, considering all factors utilised by hadith scholars such as the reliability and accuracy of the narrator as well as the chain of transmission. The proposed approach will also consider different opinions in the literature about narrators' accuracy, reliability, and memorisation capabilities. The arithmetic method was applied to three hadiths ranking: true, good, and weak, and then the weighted arithmetic mean was calculated for each of them which represent the degree of validity of each hadith. The obtained results agree to a high degree with judgment reported by hadith scholars.

A Comparative Analysis of Computer Vision Libraries in the Context of a Jakarta Traffic Simulator

Traffic management is essential for preventing traffic congestion and regulating traffic flow. Jakarta has implemented several methods to reduce traffic congestion in response to the growing number of vehicles on the road. This study proposes integrating computer vision and fuzzy logic into traffic lights for use in traffic management. The study focuses on comparing and implementing three computer vision libraries: ImageAI, Cloud Vision, and OpenCV Libraries. This comparison is based on Jakarta traffic data captured by closed-circuit television (CCTV). After evaluating the libraries, the result indicates the ImageAI achieves better performance than the other libraries across testing measurements and it was used to develop a module simulator. This simulator was used to compare the amber time produced by each lane at a simple intersection under fixed time versus proposed-actuated traffic lights. The simulation of the proposed actuated traffic light scenario demonstrates that the use of computer vision and fuzzy logic in the decision-making process can marginally improve traffic simulation compared to the fixed-time traffic light scenario. This study illustrates the application of computer vision and fuzzy logic to traffic management. The application of these technologies to traffic lights has the potential to increase traffic flow and decrease congestion.

ПРОГНОЗИРОВАНИЕ УРОЖАЙНОСТИ СЕЛЬСКОХОЗЯЙСТВЕННЫХ КУЛЬТУР МЕТОДАМИ НЕЧЕТКОЙ ЛОГИКИ

The paper deals with the crop yield issues. Analysis, modelling and prediction of crop yields were performed based on the dynamics of changes in natural and climatic characteristics by choosing configurations of the fuzzy logic methods. The study evaluated the time series of winter wheat yields for 2008–2018 and climatic characteristics (air temperature and precipitation) for 1955–2018 using the Dixon, SmirnovGrubbs, Student and Fisher criteria. The results of the analysis are crucial for identifying the level of empirical adequacy. A computer model of crop yield dependence and climate change dynamics was developed using Fuzzy Logic application of MATLAB software. After completing the stages of training and testing the computer model variants, the authors could predict crop yields for the coming years using previously obtained meteorological values.