Fuzzy Decision Research Articles

Comprehensive benefits evaluation of low impact development using scenario analysis and fuzzy decision approach

The comprehensive benefit evaluation of LID based on multi-criteria decision-making methods faces technical issues such as the uncertainties and vagueness in hybrid information sources, which can affect the overall evaluation results and ranking of alternatives. This study introduces a multi-indicator fuzzy comprehensive benefit evaluation approach for the selection of LID measures, aiming to provide a robust and holistic framework for evaluating their benefits at the community level. The proposed methodology integrates quantitative environmental and economic indicators with qualitative social benefit indicators, combining the use of the Storm Water Management Model (SWMM) and ArcGIS for scenario-based analysis, and the use of hesitant fuzzy language sets and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for decision-making. The framework’s novelty lies in the integration of the hesitant fuzzy weighted average algorithm to handle subjective uncertainties in expert judgment and the incorporation of multi-return period scenarios to enhance the robustness of the evaluation. The comprehensive benefits of 26 LID configurations were conducted in Chenglong Road Subdistrict under five rainfall return period scenarios of 5, 10, 20, 50, and 100 years. The results show that LID measures, particularly combinations of sunken green spaces and permeable paving, offer significant reductions in runoff and peak flow, along with improved flood mitigation across multiple return periods. Additionally, this study identifies practical LID implementation priorities for local decision-makers. The relative closeness is influenced by the indicators and non-calibrated parameters. However, it overall does not affect the main trends and key insights derived. The robustness of the proposed approach is reinforced by four key aspects: the impact of the Thiessen polygon method in ArcGIS, the influence of composite runoff coefficient and iterative optimization in SWMM, the effect of hesitant fuzzy linguistic sets and TOPSIS on weight calculation, and the contribution of simulations under different return periods to stability analysis.

An information gap decision theory and improved gradient-based optimizer for robust optimization of renewable energy systems in distribution network

In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand. A novel multi-objective improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock’s direct rotational technique to overcome premature convergence is proposed to determine the problem optimal decision variables. The deterministic optimization framework without uncertainty minimizes active energy loss, unmet customer energy, and renewable generation costs. The study also examines the impact of dispersed and hybrid renewable resources on solving the problem. In the robust optimization framework considering the deterministic obtained results, the focus is on determining the maximum uncertainty radius (MUR) of renewable resource generation and network demand based on the uncertainty risk. The MURs and system robustness are optimally determined using information gap decision theory (IGDT) and the MOIGBO, considering various uncertainty budgets under worst-case scenarios. The deterministic results indicate that the MOIGBO effectively balances the objectives and identifies the final solution within the Pareto front, according to fuzzy decision-making. The results also reveal that the dispersed case yields better objective values than the hybrid case. Furthermore, the MOIGBO outperforms MOGBO and multi-objective particle swarm optimization (MOPSO) in improving distribution network operations. The robust results show that maximum system robustness is achieved at 30% uncertainty risk due to forecasting errors, with MUR values of 0.54% for resource production and 12.56% for load demand.

A noninvasive blood glucose detection method with strong time adaptability based on fuzzy operator decision fusion and dynamic spectroscopy characteristics of PPG signals.

PPG signals are a new means of non-invasive detection of blood glucose, but there are still shortcomings of poor time adaptability and low prediction accuracy of blood glucose quantitative models. Few studies discuss prediction accuracy in the case of a large time interval span between modeling and prediction. This paper proposes an automatic optimal threshold baseline removal algorithm based on variational mode decomposition (AOT-VMD), which can adaptively eliminate high-frequency noise and baseline interference for each decomposed IMF modal component and reduce the baseline difference of PPG signals from different days. Furthermore, a fuzzy integral multi-model decision fusion algorithm based on error weight is proposed. The fuzzy integral operator is introduced to make the features with large contributions in each sub-model maintain a high-weight value in the overall prediction mechanism, which improves the prediction accuracy of blood glucose. In this paper, a self-developed portable PPG glucose meter is used to collect PPG signals, and the true blood glucose values for 8 consecutive days are collected by CGM. The proposed algorithm is used to build a model with the first day's data and predict the blood glucose values for the remaining 7 days. The experimental results show that the AOT-VMD preprocessing algorithm and the quantitative regression algorithm of the fuzzy integral multiple model decision fusion algorithm proposed in this paper perform well in measurement accuracy and time adaptability compared with the traditional methods. In addition, the proposed method requires less invasive calibration samples in the modeling stage, achieving high-precision prediction for a long period. 100% of the samples are located in areas A and B of the Clarke area in this experiment, and the algorithm has strong time generalization ability. This innovative method can promote the development of a home blood glucose noninvasive detector.

A method for evaluating the learning effectiveness of MOOC English online education based on fuzzy clustering decision tree

A method for evaluating the learning effectiveness of MOOC English online education based on fuzzy clustering decision tree

Computerized Decision Support System and Fuzzy Logic Rules for Early Diagnosis of Pesticide-Induced Diseases.

Many reflexologists employ outdated concepts that do not align with modern anatomy, physiology, and biophysics. Those concepts undermine physicians' confidence in their diagnosis. This study aims to improve the quality of medical care for workers in the agro-industrial complex who are exposed to pesticides by a fuzzy mathematical model using acupuncture points reflexes. Data obtained from reflex diagnostic methods are utilized in hybrid fuzzy decision rules to build a predictive classification model that integrates medical diagnosis with artificial intelligence. Pesticide exposure leads to cardiovascular and nervous system bronchopulmonary diseases, as well as kidney and liver tissue pathology. The developed model generates decision rules for early prediction of nervous system disorders, particularly when the primary risk factor is exposure to agricultural pesticides containing nitrates. In modern medical practice, there is a growing interest in ancient methods of reflex diagnostics and therapies based on maintaining the energy balance of an organism's meridian structures. However, the lack of a solid theoretical foundation explaining the mechanisms of interaction between internal and surface meridian structures poses a significant obstacle to wider adoption of reflex diagnostic techniques. This limitation severely hampers the potential of acupuncture. Moreover, many reflexologists in practice tend to overstate the benefits of acupuncture, which may lead to errors, that undermine the appropriate approach to diagnosis and treatment. The proposed model proves valuable for the healthcare of agro-industrial complex workers, as its decision-making process achieves an accuracy rate of over 85% in forecasting nervous system disorders.

AI-based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities with Spherical Fuzzy Decision Algorithm

AI-based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities with Spherical Fuzzy Decision Algorithm

Multiobjective energy management of multi-source offshore parks assisted with hybrid battery and hydrogen/fuel-cell energy storage systems

With the recent advancements in the development of hybrid offshore parks and the expected large-scale implementation of them in the near future, it becomes paramount to investigate proper energy management strategies to improve the integrability of these parks into the power systems. This paper addresses a multiobjective energy management approach using a hybrid energy storage system comprising batteries and hydrogen/fuel-cell systems applied to multi-source wind-wave and wind-solar offshore parks to maximize the delivered energy while minimizing the variations of the power output. To find the solution of the optimization problem defined for energy management, a strategy is proposed based on the examination of a set of weighting factors to form the Pareto front while the problem associated with each of them is assessed in a mixed-integer linear programming framework. Subsequently, fuzzy decision making is applied to select the final solution among the ones existing in the Pareto front. The studies are implemented in different locations considering scenarios for electrical system limitation and the place of the storage units. According to the results, applying the proposed multiobjective framework successfully addresses the enhancement of energy delivery and the decrease in power output fluctuations in the hybrid offshore parks across all scenarios of electrical system limitation and combinational storage locations. Based on the results, in addition to the increase in delivered energy, a decrease in power variations by around 40 % up to over 80 % is observed in the studied cases.

Medical Waste Management Based on an Interval-Valued Fermatean Fuzzy Decision-Making Method

Due to its infectious and hazardous nature, medical waste poses risks to people and the environment. For patients to receive medical attention and recover in a safe environment, waste must be disposed of correctly. Improper medical waste disposal poses a severe risk to society, which can accelerate the development of various pandemics and epidemics. In this case, medical waste disposal should be handled appropriately. This study presents an integrated multi-criteria decision-making method consisting of entropy, the Pivot Pairwise Relative Criteria Importance Assessment, and Measurement of Alternatives and Ranking according to Compromise Solution methods based on an interval-valued Fermatean fuzzy set. This method can guarantee high safety and security for health practitioners and society through effective modeling and ranking of risks associated with medical waste disposal. Five alternatives and eight criteria were determined. According to the results, incineration is the most suitable disposal process for medical waste. The performance was then assessed and validated using a sensitivity analysis. A sensitivity analysis has been conducted across the range of values for the $\alpha$ parameter. It was examined whether the rankings of the alternatives changed when the $\alpha$ values in the integrated weight determination model for sensitivity analysis were altered. When the different $\alpha$ values were reviewed with the selected $\alpha$ value in the application example, it was seen that incineration was the first alternative. In addition, the study's findings and their consequences for lawmakers, businesspeople, technologists, and practitioners are examined. In the future, these stakeholders can concentrate on these deficiencies and provide long-term remedies.

Markov chain and RATGOS-driven fuzzy decision-making for prioritizing cybersecurity measures in microgrid systems

AbstractNecessary precautions must be taken to increase cybersecurity to improve the performance of microgrid systems. However, because these actions create costs, it is not possible to improve all of these variables at the same time. There is a significant need for a new study that will conduct a priority analysis of the factors affecting the increase of cyber security. Accordingly, the purpose of this study is to identify the most significant factors to increase the cybersecurity of microgrids. For this purpose, a novel machine learning methodology adopted fuzzy decision-making model has been generated that has three different stages. Firstly, the weights of the experts are computed by the help of dimension reduction with machine learning. At the second stage, the criteria for cybersecurity in microgrids are weighted via Markov chain with Spherical fuzzy sets. The final stage examines the performance of group of seven (G7) economies with respect to the cybersecurity performance in microgrid projects. The use of Markov chain in criterion weights is the biggest contribution of this study to the literature. The Markov chain examines possible states by considering the next transition probabilities of the states. Based on this transition matrix, the limit state can be obtained and the general situation in the problem can be obtained. With this analysis, it is aimed to obtain the importance of the general criteria in the problem, taking into account the transitivity of the criteria in the problems. The findings indicate that the most important issue in cyber security in microgrids is the quality of the structure of the network used. Similarly, the emergency action plan and redundancy is the second most critical factor in this regard. The ranking results give information that Germany and France are the most successful countries with respect to the cybersecurity increase performance of microgrids.

A fuzzy optimization-oriented decision support model to examine key industry 4.0 strategies for building resilience against disruptions in a healthcare supply chain

A fuzzy optimization-oriented decision support model to examine key industry 4.0 strategies for building resilience against disruptions in a healthcare supply chain

A Fuzzy Decision Support System for Real Estate Valuations

The field of real estate valuations is multivariate in nature. Each property has different intrinsic attributes that have a bearing on its final value: location, use, purpose, access, the services available to it, etc. The appraiser analyzes all these factors and the current status of other similar properties on the market (comparable assets or units of comparison) subjectively, with no applicable rules or metrics, to obtain the value of the property in question. To model this context of subjectivity, this paper proposes the use of a fuzzy system. The inputs to the fuzzy system designed are the variables considered by the appraiser, and the output is the adjustment coefficient to be applied to the price of each comparable asset to obtain the price of the property to be appraised. To design this model, data have been extracted from actual appraisals conducted by three professional appraisers in the urban center of Santa Cruz de Tenerife (Canary Islands, Spain). The fuzzy system is a decision-helping tool in the real estate sector: appraisers can use it to select the most suitable comparables and to automatically obtain the adjustment coefficients, freeing them from the arduous task of calculating them manually based on the multiple parameters to consider. Finally, an evaluation is presented that demonstrates its applicability.

Assessing barriers of automation and robotics adoption in the Indian construction industry: a fuzzy DEMATEL approach

Purpose Despite global advancements, the Indian construction industry lags in adopting technologies like robots, artificial intelligence, drones, unmanned ground vehicles and Internet of Things sensors due to various barriers. This reliance on traditional practices leads to persistent issues such as labour shortages, low productivity, safety risks, cost overruns and delays. Identifying and addressing these barriers is crucial for adopting advanced technologies. Hence, this study aims to identify and assess the influential barriers to construction automation and robotics (AaR) in India using a systematic approach. Design/methodology/approach An extensive literature review identified key barriers, including technological, financial, regulatory, environmental and organisational. A questionnaire survey was conducted among industry professionals, and the Fuzzy Decision-Making Trial and Evaluation Laboratory technique was used to evaluate the interrelationships and relative significance of these barriers. Findings The results of the study reveal the most critical cause barriers as “high initial cost”, “rigidity in organisational processes and procedures”, “interoperability”, “lack of competency” and “lack of standardisation”. These barriers subsequently influence the top two effect barriers such as “market uncertainty” and “ethical concerns”. Practical implications This research offers a systematic and quantitative assessment, enabling stakeholders to make informed decisions and develop strategies to overcome barriers to AaR adoption, unlocking the transformative potential of AaR in India’s construction sector. Originality/value Despite potential benefits, the adoption of AaR in Indian construction remains limited due to various barriers. This study provides novel insights with the first comprehensive assessment of these barriers and their interrelationships.

Lateral collision avoidance stability control study of intelligent vehicles on low adherence roads

To enhance lateral collision avoidance safety for intelligent vehicles on various road surfaces, this paper proposes a collision avoidance algorithm specifically designed for low-adhesion roads. The study begins by a fuzzy control lane change decision model that considers the relative state information between the ego vehicle and the front vehicle. Next, the environment risk potential field is modeled, with multiple lane change paths of varying parameters considered as candidate trajectories. Critical risks during vehicle steering are analyzed, leading to the design of a dynamic instability boundary risk point potential field. Trajectory optimization is then performed using the Particle Swarm Optimization (PSO) algorithm. Finally, a fuzzy PID controller is developed to track the optimal trajectory. Simulation results demonstrate that, compared to the traditional artificial potential field method, the proposed collision avoidance algorithm provides better lateral stability in the planned paths. Additionally, the fuzzy PID controller designed in this study outperforms the Quantitative Feedback Theory (QFT) controller in terms of tracking accuracy.

Application of Fuzzy Decision Theory in Multi Objective Logistics Distribution Center Site Selection

Application of Fuzzy Decision Theory in Multi Objective Logistics Distribution Center Site Selection

Comparative Study of Naive Bayes Data Mining, Decision Tree C4.5 And Fuzzy Decision Tree (ID3) In Analyzing the Financing of Fitness Center Memberships: Case Study PT Fitindo Sehat Sempurna

Abstract - The business world is always experiencing rapid changes, so it requires companies to be able to respond to these changes quickly and precisely. PT. Fitindo Sehat Sempurna as a company engaged in fitness services wants to do data mining to predict the smooth financing of its members and strive to increase sustainable sales turnover. One of the classification algorithms that is often used and gets a lot of attention from researchers in predicting problematic financing is Naive Bayes, Decision Tree C4.5 and then tries to compare it with the Fuzzy Decision Tree (ID3). Of all the trials of the three methods, the best results were obtained, namely Naive Bayes with the highest calculation result of accuracy of 85,00% and AUC = 0,960 as the best results. This test uses 80 training data and 20 testing data.Keywords: Financing, Membership, ID3, Fuzzy, Decision Tree, C4.5, Naive Bayes. Abstrak - Dunia bisnis selalu mengalami perubahan yang cepat, sehingga menuntut perusahaan untuk dapat merespon perubahan tersebut dengan cepat dan tepat. PT. Fitindo Sehat Sempurna sebagai perusahaan yang bergerak di bidang jasa kebugaran ingin melakukan data mining untuk memprediksi kelancaran pembiayaan para anggotanya dan berupaya untuk meningkatkan omset penjualan yang berkelanjutan. Salah satu algoritma klasifikasi yang sering digunakan dan mendapat banyak perhatian dari para peneliti dalam memprediksi pembiayaan bermasalah adalah Naive Bayes, Decision Tree C4.5 dan kemudian mencoba membandingkannya dengan Fuzzy Decision Tree (ID3). Dari semua uji coba ketiga metode tersebut, didapatkan hasil terbaik yaitu Naive Bayes dengan hasil perhitungan akurasi tertinggi yaitu 85,00% dan AUC = 0,960 sebagai hasil terbaik. Pengujian ini menggunakan 80 data training dan 20 data testing.Kata kunci: Pembiayaan, Keanggotaan, ID3, Fuzzy, Decision Tree, C4.5, Naive Bayes

RETRACTION: The Analysis of Optimized Path Selection for Management Mode of Coastal Regional Circular Economy Based on Fuzzy Decision Algorithm

Retraction: W. Wei, D. Xiao, and W. Liu, “,” Expert Systems 41, no. (2024): . https://doi.org/10.1111/exsy.12985. The above article, published online on 04 March 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. In addition, the investigation found significant flaws and logical inconsistencies in the article. Therefore, the article must be retracted. The authors did not respond to the notice regarding the retraction.

Integrating IVIFS and Butcher’s RK Method for Multiple Attribute Group Decision Making: Mobile Brand Selection

Problems in MAGDM (Multiple Attribute Group Decision Making) are examined using IVIFS (Interval-Valued Intuitionistic Fuzzy Sets). To aggregate the Interval-Valued Intuitionistic Fuzzy Decision Matrices (IVIFDM), subjective geometric & hybrid geometric operators are employed. Decision-maker weights are determined using Runge-Kutta methods, which are applied to the decision-making process. A new Extended Hamming Distance formula is used to rank the alternatives. A numerical instancehad been given to demonstrate effectiveness of proposed method.

Fuzzy-UNet: Semantic-Fuzzy Integration for Fabric Defect Identification

Abstract The detection of fabric surface defects is a critical component for quality assurance and operational efficiency within the textile industry. A significant challenge in this field is the effective incorporation of domain expertise to enhance the precision of defect recognition. This paper introduces Fuzzy-UNet, an innovative fabric defect detection system that integrates a semantic segmentation network with a fuzzy decision-making model, addressing the challenge of domain knowledge integration. By combining a cascaded data model with a knowledge-based model, Fuzzy-UNet harnesses the power of deep learning and artificial experience to refine the identification process. The data-driven model of our system is an advanced UNet architecture, specifically tailored for detecting subtle defects in fabrics with non-standard aspect ratios. The system’s novel fuzzy decision model utilizes spatiotemporal data from multiple cameras, which is essential for enhancing the accuracy of the detection process. Our comprehensive experiments demonstrate the robustness of Fuzzy-UNet, with a significant increase in accuracy to 96.10% and a marked reduction in the False Positive Rate (FPR). Fuzzy-UNet's superior performance over existing methods makes it a leading solution for industrial fabric defect detection.

Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation

High noise resistance and high boundary fitting accuracy have always been the goals of image classification. However, the two mutually constrain each other, making it extremely difficult to reach equilibrium. To deal with this problem, the unsupervised image classification algorithm based on fully fuzzy Voronoi tessellation is proposed. It extends Voronoi tessellation from hard to fuzzy, and proposes a hierarchical fuzzy membership model, i.e., pixels fuzzily belong to Voronoi polygons and polygons fuzzily belong to clusters. The objective function is established based on the hierarchical fuzzy membership model by fully considering the transitivity of fuzziness between different levels. Then, the optimal classification result can be obtained by the fuzzy comprehensive decision theory under the best parameter solution. The first level retains the flexibility of pixels while modeling spatial constraints. The second level determines which class the polygon belongs to under the constraint of the first level. It provides an effective way of balancing noise resistance and boundary fitting. In addition, the Voronoi tessellation is explicitly expressed in the objective function in the form of the mathematical model, which allows it to obtain the optimal value through analytical solutions instead of the previous random sampling method. It greatly increases the convergence speed of the algorithm. Experiments have been performed on simulated and several remote sensing images with seven comparing algorithms to demonstrate the effectiveness of the proposed algorithm.

Task-to-Thread Mapping in OpenMP Using Fuzzy Decision Making

The performance of shared-resource multi-core hardware platforms in complex cyber-physical systems (CPSs), e.g., automotive industry, can be improved using task-based parallelism through OpenMP. However, most CPS require certain level of predictability, which challenges the efficient implementation of the task-to-thread mapping process. This exploratory work build on the fact that existing mapping methods mostly use elementary or heuristic algorithms, and the idea that artificial intelligence (AI) algorithms can be used to enhance the efficiency of such processes. Accordingly, this paper (1) evaluates the suitability of AI-based techniques in improving the performance of task-to-thread mapping in the OpenMP framework, and (2) proposes a hypothesis to perform an intelligent mapping using fuzzy logic for multi-queue schedulers to improve the predictability of the system.