Mamdani Fuzzy Inference System Research Articles

Enhancing green ports in Dar es Salaam Port: facility optimization for emission reduction through Mamdani and Sugeno Fuzzy inference systems

Enhancing green ports in Dar es Salaam Port: facility optimization for emission reduction through Mamdani and Sugeno Fuzzy inference systems

Fuzzy Logic Harmony in Water: Mamdani Inference System Applied to Evaluate Pristine Pond Water Quality

Aquatic ecosystems that are subject to urbanization and environmental changes, such as the Kapaleeswarar and Chitrakulam tanks, depend on evaluating water quality. Their complicated data present challenges for conventional approaches. The usefulness of the Mamdani fuzzy inference system in determining the water quality in these tanks is investigated in this work. It creates a comprehensive assessment based on subject-matter expertise by handling ambiguous descriptors with linguistic variables and fuzzy sets. The system’s procedures for implementation are described in detail, with an emphasis on how well they can manage interrelated variables. The study shows how well the system measures the water quality in tanks and suggests ways to improve it. Tank evaluation that incorporates the Mamdani system encourages comprehensive resource management and cultural preservation.

Generalized ATOVIC System Based on Triangular Fuzzy Numbers for Pattern Classification

Diseases such as diabete and cancer must be detected and treated precociouse. They are generally detected using recognition and classification systems. The latter must be choosen according their accuracies and other measures. In addition, some difficulties and ambiguities can exist related with features representing data sets such as their variant types (eg., integer, nominal, etc.) or overlapping between them. Also, data can be imprecise or incomplete and can influence negatively classification results. Fuzzy sets and their generalizations, resolve problems of data uncertainty by modeling them with linguistic variables. The latter are presented with membership functions having different shapes. Triangular fuzzy numbers which represent tringular functions are very rarely used in classification systems. The cause can be that triangular shape does not preserve in case of some calculations. This research proposes to generalize the classification system "‘Amended Fused TOPSIS-VIKOR for classification"’ (ATOVIC) with triangular fuzzy numbers and to apply it to some UCI date sets. The objective is to unveil the impact of triangular fuzzy numbers in classification. The results of generalized fuzzy ATOVIC are compared to those of its crisp version and those of some existing classification systems using fuzzy sets. Results of ATOVIC generalized with tringular fuzzy numbers are promising. Especially its results on breast cancer Wisconsin and IRIS data sets are higher than Mamdani and Segano fuzzy inference systems using triangular fuzzy numbers from literature.

A Fuzzy Inference System for Predicting Air Traffic Demand based on Socioeconomic Drivers

The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a Graphical User Interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed Air Passenger Demand Prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Optimal air quality management using novel dual Mamdani and neuro fuzzy inference system for real-time accurate prediction

Optimal air quality management using novel dual Mamdani and neuro fuzzy inference system for real-time accurate prediction

Groundwater quality assessment for drinking, irrigation purposes and fuzzy logic-based water quality index for industrial suitability in Walajapet taluk, Ranipet district, Tamil Nadu, India

ABSTRACT Ranipet district in Tamil Nadu is one of the major industrial zones in India. Groundwater has been extensively used in this region for drinking, agricultural, and industrial purposes. For finding the groundwater quality, representative samples have been collected in the premonsoon (PRM) and postmonsoon (POM) seasons of 2023 and analysed for major physicochemical parameters. The drinking suitability was analysed based on drinking water quality index, and it showed that 68.52 and 70.37% of samples from the PRM and POM seasons are classified as poor groundwater for drinking. The groundwater's suitability for irrigation was analysed based on various irrigation quality indices, and it highlighted that most of the groundwater was suitable for agriculture. Industrial suitability was determined by several industrial quality indices. Due to the simultaneous application of several indices, a sample is categorised into many water quality groups, introducing bias into handling and decision-making. The Mamdani fuzzy inference system creates a unique integrated industrial water quality index to address this issue. The index revealed that only 24.07 and 9.26% of samples from the PRM and POM seasons, respectively, were free of corrosion or scaling, while the remaining samples were inappropriate for industrial use.

Fuzzy Decision Support Systems to Improve the Effectiveness of Training Programs in the Field of Sports Fitness

To maximize training programs and improve individual performance, the sports fitness profession is always looking for new and innovative solutions. Fuzzy decision support systems provide a strong basis for improving training regimen efficacy through flexible and adaptive decision-making. In sports fitness, doing things differently might affect the way one can train and even lead to injury. Particularly if exercises call for expert training regimens, determining if fitness requirements are sufficiently satisfied is a difficult task. Athletes participate in training programs focusing on strengthening their bodies and minds to perform better. Each sport has unique needs, and athletes should prepare to meet those demands while still meeting their sport’s general fitness activity requirements. A novel strategy for assisting athletes with fitness-related decision-making is presented in this study as the Fitness Mamdani Decision System. At the outset, the system provides an adaptive decision framework to improve the effectiveness of training programs by applying the principles of intuitionistic fuzzy numbers and fuzzy logic. The study wants to know how to train effectively, and it takes important factors like your mood, degree of preparation, sleep quality, and stress levels into account. Using language-specific terminology and triangle membership functions, the Mamdani fuzzy inference system generates rules based on analyzing these crucial elements. Regarding measures like adaptability index, training load capacity, long-term program efficiency, and participation ratio among sports fitness individuals, the system is guided by fuzzy rules that infer decisions.

Mamdani's Fuzzy Logic-Based Tapioca Optimal Production Amount Prediction System

This research focuses on the imbalance between supply and demand in the tapioca industry, especially on the scale of Small and Medium Industries (SMI). Such challenges involve price fluctuations and lack of efficiency in determining the optimal production amount. Using fuzzy logic as an artificial intelligence method, this research aims to develop a system for predicting the optimal amount of tapioca production that can increase efficiency, reduce waste of raw materials and energy, and stabilize prices. The data used in this study was obtained through interviews with resource persons who are involved in the field of tapioca SMI. Furthermore, the data was processed using the Mamdani Fuzzy Inference System method. The results showed that in the case of production when demand is 400 kg and cassava availability is 2000 kg, the optimal production amount of tapioca is 630 kg. This value is also consistent when proven using the Matlab R2015a application. This shows that the model can be relied upon in determining the decision on the amount of tapioca production by considering demand factors and raw material availability.

A Mamdani fuzzy inference system with trapezoidal membership functions for investigating fishery production

Seas, marine ecosystems, and coastal regions are crucial components of our environment. Numerous scientific strategies have been adopted to boost fisheries and aquaculture productivity. This study proposes a fuzzy-logic-based model to produce fisheries in India, which ranks fourth worldwide for fisheries production. Five input variables, such as fish seed, export, post-harvesting, released fund, and temperature, are considered inputs, and the production of fisheries is taken as the output variable. A Mamdani-type fuzzy inference system with trapezoidal membership functions is prepared with 243 rules in the IF-THEN format. This mathematical model investigates the impacts of input parameters on the production of Indian fisheries. We fit the model with the real-world data and show that fish seed, export, released fund, and post-harvesting facilities positively impact fisheries production. However, a very high temperature is unsuitable for high production, even if all other parameters lie at their desired level.

Intelligent Traffic Light Time Cycle Simulation Model using Fuzzy Mamdani

The growth of motorized vehicles in Indonesia has increased significantly. According to data from the Central Bureau of Statistics, the number of motorized vehicles in Indonesia has increased by around 10% each year in the last five years. One of the negative impacts of the increasing number of motorized vehicles is traffic congestion. Traffic congestion has become a serious problem in several cities in Indonesia. One of the causes is the increase in the number of vehicles at road intersections, which has an impact on congestion and the safety of road users. The rapid growth in the number of vehicles requires a more comprehensive strategy to reduce congestion and accidents at road intersections. Therefore, the need for Intelligent Transportation System, especially on the time-cycle configuration of intelligent red light is very important. This research aims to model the time-cycle of the red light using the Mamdani Fuzzy Inference System to simulate the green light time configuration so as to reduce the waiting time of road users at highway intersections. The simulation results show that the time-cycle configuration and green light time length of the Mamdani Fuzzy calculation are more varied relative to the number of vehicles. The values are relatively smaller than 6 to 54 seconds from the time configuration set by the local Department of Transportation. This shows a time efficiency for road users of up to 27%, which means that road users can complete trips 6 to 13 seconds faster.

Application of Mamdani Fuzzy Inference Method in Selection of Contract Employees of PT Kreasi Utama Mandiri

This study aims to improve the performance of the employee selection system. This research uses the Mamdani Fuzzy Inference System to determine the suitability of job applicants for the company. Recommendations for job applicants can be used as a reference for selecting permanent employees. The common problem in selecting permanent employees is the difficulty for the company in assessing and selecting competent job applicants, as the company has to compare the test results of each job applicant one by one, requiring a long time. In addition, the most difficult thing in making decisions is eliminating someone's subjectivity, so that every decision made is objective based on the criteria expected by the company. The results of this study proved that by applying mamdani fuzzy inference to the selection of contract employees at PT. Kreasi Utama Mandiri can facilitate management in selecting contract employees with an accuracy of 90% when compared to HRD assessments.

Mamdani-type fuzzy inference system for irrigational water quality

This paper aims to evaluate the quality of groundwater resources used for irrigation purposes using a Mamdani-type fuzzy inference system (MFIS). The MFIS is used to resolve ambiguities and uncertainties in economic, social, and natural systems and also facilitates the capture of expert knowledge in ways similar to human reasoning and thought processes. In this study, 20 groundwater samples were collected from various locations within the Mayiladuthurai district, Tamil Nadu, India, between January 2016 and December 2019. These samples underwent physical and chemical analyses to assess the suitability of the collected water resources for irrigation. The analysis utilizes the Mamdani Fuzzy Inference System, which combines values of Electrical Conductivity (EC) and Sodium Adsorption Ratio (SAR). Additionally, methods from the US Salinity Laboratory Staff were also employed. Ultimately, the groundwater quality in Mayiladuthurai is graded for irrigation use by this method. The results indicate that the MFIS reduces imprecision and uncertainty in data handling through the fuzzy membership function. The comparison of irrigation suitability results clearly demonstrates that the proposed MFIS method offers an improved assessment of the irrigation water quality level of the studied groundwater resources.

Optimizing Blood Glucose Regulation in Type 1 Diabetes Patients via Genetic Algorithm-Based Fuzzy Logic Controller Considering Substantial Meal Protocol

Effective management of blood glucose levels in individuals with type 1 diabetes, especially after meals, is crucial for diabetes care. Artificial pancreas systems (APS) perform automated insulin delivery in subjects with type 1 diabetes mellitus (T1DM). In this study, an optimized fuzzy logic controller was designed to achieve a euglycemic range after a substantial meal intake. All in silico simulations were performed using the MATLAB/Simulink environment, leveraging control variability grid analysis (CVGA), and the performance of the controller was evaluated. The proposed controller is based on a fuzzy-logic control law designed in three stages. First, a nonlinear framework of the glucose-insulin regulatory system was identified based on the heavy meal protocol of three patients given as follows: for subject ID 117-1, a total of 295 gCHO; for subject ID 126-1, 236 gCHO; and subject ID 128-1, 394 gCHO over a day. Then, an iterative tree structure was employed to establish a stabilizing control rule for insulin delivery, integrating inputs from two Mamdani Fuzzy Inference System (FIS) objects. Finally, a genetic algorithm refines the control system by fine-tuning the uncertainty of the fuzzy membership functions. Two scenarios were considered for three patients to assess the performance of the proposed controller. The results indicated its effectiveness under various conditions, achieving a time in the range of 61.25%, 71% and 61.10% respectively for the three subjects. The obtained results are analyzed and compared with IMC and multi-objective output feedback controllers. The findings of the study reveal that the proposed controller shows promising advancements in tailored strategies for type 1 diabetes patients, outperforming the other controllers in terms of blood glucose regulation.

Short-Term Electrical Energy Demand Forecasting Using Fuzzy Logic Technique

Minimization of electrical energy wastage and provision of its regular supply make electrical load forecasting an important aspect of the power infrastructure. An accurate load forecasting is crucial for the reduction of the cost of electrical energy generation and spinning reserve capacity. Therefore, in this study, fuzzy logic (FL) technique was employed for the projection of electrical energy demand on short-term basis. The FL model was trained using the six months hourly load and temperature data respectively obtained from 132/33 kV Ikeja West Transmission Station, Ayobo and Nigerian Meteorological Agency, Oshodi, Lagos State, Nigeria. Triangular and trapezoidal membership functions (MFs) were used in the training of the model with Mamdani fuzzy inference system. The time, load and temperature inputs were fuzzified into six, four and three MFs respectively while the fuzzification process used 25 fuzzy rule bases. The centroid method of defuzzification was used to change the results into readable values. The adequacy of the FL model was determined using five metrics including mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), chi square (χ2) and F- test. The obtained results revealed that the FL model developed excelled in all the five tests of adequacy considered with MSE, MAE, MAPE, χ2 and F-test values of 4.17, 6.74, 11.51%, 7.93 and 1.27 respectively and hence, performed satisfactorily. This work established that the fuzzy logic approach is appropriate for electrical load projection on short-term basis.

Rule Based Mamdani Fuzzy Inference System to Analyze Efficacy of COVID19 Vaccines

INTRODUCTION: COVID-19 was declared as most dangerous disease and even after maintaining so many preventive measures, vaccination is the only preventive option from SARS-CoV-2. Vaccination has controlled the risk and spreading of virus that causes COVID-19. Vaccines can help in preventing serious illness and death. Before recommendation of COVID-19 vaccines, clinical experiments are being conducted with thousands of grown person and children. In controlled situations like clinical trials, efficacy refers to how well a vaccination prevents symptomatic or asymptomatic illness.
 OBJECTIVES: The effectiveness of a vaccine relates to how effectively it works in the actual world.
 METHODS: This research presents a novel approach to model the efficacy of COVID’19 vaccines based on Mamdani Fuzzy system Modelling. The proposed fuzzy model aims to gauge the impact of epidemiological and clinical factors on which the efficacy of COVID’19 vaccines.
 RESULTS: In this study, 8 different aspects are considered, which are classified as efficiency evaluating factors. To prepare this model, data has been accumulated from various research papers, reliable news articles on vaccine response in multiple regions, published journals etc. A set of Fuzzy rules was inferred based on classified parameters. This fuzzy inference system is expected to be of great help in recommending the most appropriate vaccine on the basis of several parameters. 
 CONCLUSION: It aims to give an idea to pharmaceutical manufacturers on how they can improve vaccine efficacy and for the decision making that which one to be followed.

Intelligent decision support system for pulmonary tuberculosis detection using bipolar fuzzy utility matrix and bipolar Mamdani fuzzy inference system

Tuberculosis (TB) stands as the second leading global infectious cause of death, following closely behind the impact of COVID-19. The standard approach to diagnose TB involves skin tests, but these tests can yield inaccurate results due to limited access to healthcare and insufficient diagnostic resources. To enhance diagnostic accuracy, this study introduces a novel approach employing a Bipolar Fuzzy Utility Matrix Inference System (BFUMIS) and a Bipolar Mamdani Fuzzy Inference System (BMFIS) to assess TB disease levels. By considering factors associated with the causation of TB, the study devises suitable membership functions for bipolar fuzzy sets (BFS) using both triangular and trapezoidal fuzzy numbers. Using a point factor scale, the study clusters the rules systematically and assesses the level of uncertainty within these grouped rules by utilizing bipolar triangular fuzzy numbers (BTFN). To handle the BTFN, this study proposes converting bipolar triangular fuzzy into bipolar crisp score (CBTFBCS) algorithm as a defuzzification method. The optimal bipolar fuzzy utility sets (BFUS) are determined from the bipolar fuzzy utility matrix to identify patients’ TB disease levels. These sets play a pivotal role in characterizing the severity of TB disease levels in patients. Additionally, rigorous validation of the utility framework is accomplished through measures of bipolar fuzzy satisfactory factors and sensitivity analyses. Furthermore, the study introduces the BMFIS, which presents a novel perspective on the conventional fuzzy inference system. This innovative system integrates the Mamdani fuzzy inference system (MFIS) into a bipolar fuzzy context, enriching the diagnostic process with enhanced insights. To demonstrate the efficacy of the proposed methods, extensive validation is carried out using actual clinical data. The performance metrics used in this validation effectively demonstrate the superiority of the proposed approach.

Security and Safety in Cyber-Physical System (CPS): An Inclusive Threat Model

A Cyber-Physical System (CPS) is a combination of computational algorithms and physical processes that are integrated together. Cyber-Physical Systems (CPS) integrate computer and communication capabilities with the monitoring and management of physical parts, establishing a mutually beneficial interaction between the cyber and physical components. Industrial Control System (ICS) is one example of CPS which integrates the physical (OT) and cyber domains (IT), which makes them more vulnerable to attacks. Two essential characteristics of Cyber-Physical Systems (CPS) are safety and security. Threat models are methods for identifying, analysing, and proposing security control countermeasures for threats and their capabilities. However, the threat model methods which are used for the traditional IT systems are not sufficient as they do not include the physical interactions, consequences and impacts to the safety aspects in the Operational Technology (OT). On the other hand, a risk assessment analyses attack scenario, examines cybersecurity from the attacker's point of view, and gives cost-benefit data to support the expenditure on security measures. This study proposes an inclusive attacker’s centric threat model and pro-active risk assessment model for CPS using Mamdani Fuzzy Inference System (FIS). The outcomes of the threat model prove that the lateral propagation of the threat is possible and threat may also propagate from the CPS assets to the IT segment. The risk assessment by using FIS shown that the safety and security risk for the CPS is significant and calculated as medium level. Hence, the risk factors that are considered in calculating the overall risk for a CPS need to be immediately addressed and mitigated.

A fuzzy inference system for predicting outbreaks in emerging infectious diseases

The world population is vulnerable to emerging and possibly re-emerging infectious diseases. Climate change, globalization, increased travelling, and urbanization are dynamic factors behind disease emergence and reemergence. This study formulates and analyzes a model with five inputs, viz. Change of Landscape (CoL), Gateway of Travel (GoT), Hygiene, Sanitization, Housing (HSH), Health Infrastructure (HI), and Regularity of Surveillance (RoS) and one output, viz. Possibility of Outbreak to determine the effect of these variables on disease emergence using a Mamdani fuzzy inference system. We study the impact of these factors on disease emergence and the possibility of an outbreak in a particular region.

Assessment of irrigational suitability of groundwater in Thanjavur district, Southern India using Mamdani fuzzy inference system

Groundwater is an important source for the agricultural needs in Thanjavur district, Tamil Nadu State, India. This study focused on assessing the groundwater suitableness for irrigation by employing Mamdani fuzzy inference system (MFIS). The groundwater quality data of premonsoon (PRM) and postmonsoon (POM) seasons were used for this purpose. Based on the mean value, the dominant cations were in the sequence of sodium > magnesium > calcium > potassium and the dominant anions order was bicarbonate > chloride > sulphate > nitrate. The Gibbs diagram showed that the major ionic concentration of groundwater was primarily influenced by rock-water interaction and the evaporation process. To determine groundwater suitability for irrigation purposes, sodium adsorption ratio, electrical conductivity, sodium percentage, residual sodium carbonate, magnesium ratio and Kelly's ratio were estimated. Then a Mamdani fuzzy inference system was developed with three input and one output variable. The fuzzy output categorized sample as “good” (PRM-36%, POM-44%) “good to permissible” (PRM-7%, POM-2%), “permissible” (PRM-24%, POM-23%), “permissible to poor” (PRM-5%, POM-4%) and “poor” (PRM-28%, POM-27%). On comparing the output of conventional irrigation water quality classification method, the developed model was found to be more accurate than the conventional method for classifying the irrigation water suitability. This model will assist decision-makers to develop strategies for the long-term management of groundwater resources in the study area.

Reckoning the performance of management institutions – A Mamdani fuzzy inference system approach

PurposeThe purpose of this research is to present an integrated methodological framework to aid in performance stewardship of management institutions according to their strategies based on a holistic evaluation encompassing social, economic and environmental dimensions.Design/methodology/approachA Mamdani fuzzy inference system (FIS) approach was adopted to design the quantitative models with respect to balanced scorecard (BSC) perspectives to demonstrate dynamic capability. Individual models were developed for each perspective of BSC using Mamdani FIS. Data was collected from subject matter experts in management education.FindingsThe proposed methodology is able to successfully compute the scores for each perspective. Effective placement, teaching learning process, faculty development and systematic feedback from the stakeholders were found to be the key drivers for revenue generation. The model is validated as the results were well accepted by the head of the institution after implementation.Research limitations/implicationsThe model resulting from this study will assist the institution to cyclically assess its performance, thus enabling continuous improvement. The strategy map provides the causality of the objectives across the four perspectives to aid the practitioners to better strategize. Also this study contributes to the literature of BSC as well to the applications of multi-criteria decision-making (MCDM) techniques.Originality/valueMamdani FIS integrated BSC model is a significant contribution to the academia of management education to quantitatively compute the performance of institutions. This quantified model reduces the ambiguity for practitioners to decide the performance levels for each metric and the priorities of metrics.