Fuzzy Weights Research Articles

Wind Power Plant Location Selection with Fuzzy Logic and Multi-Criteria Decision-Making Methods

This study aims to examine the effectiveness of the fuzzy multi-criteria decision-making methods, especially Fuzzy Weight by Envelope and Slope (F-WENSLO) and Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) for evaluating the potential locations for wind power plants. The study's objective is to provide a solid framework for deciding which locations are most suitable for wind energy projects, taking into account various criteria and expert opinion. The study includes the evaluation of five potential places including Gürün, Kangal, Divriği, Ulaş and Zara in Sivas province of Turkey. The evaluation criteria include wind speed and direction, altitude, land use, environmental impacts, infrastructure proximity, social acceptance, economic costs, security and risk factors, climatic conditions and legal and permit requirements. Scores from experts from various fields and weights of criteria were determined. The analysis revealed that Ulaş and Kangal got the highest point for the wind power plant installation. As Ulaş gets the highest point due to favorable wind conditions, favorable altitude and advantageous land use; Kangal stood out as a strong candidate because of its acceptable wind speed, positive social acceptance and low economic costs. The study highlights the importance of integrating multiple criteria and expert assessments in the decision-making process. The findings suggest that fuzzy multi-criteria decision-making methods can be effectively supportive of wind power plant site selection. The study provides valuable information for project managers and policy makers, emphasizing the importance of criteria such as security in the choice of location, legal requirements and social acceptance. Future research may expand these findings to examine the integration of additional criteria, alternative locations, and other multi-criteria decision-making techniques.

Novel decision making approach for sustainable renewable energy resources with cloud fuzzy numbers

Decision making approaches depending on the assessments of individual decision makers produce inaccurate results due to the existence of multiple uncertainties. To model intrapersonal uncertainty, interpersonal uncertainty and randomness in decision making assessments, this research study proposes a novel approach by integrating linear and non-linear type of fuzzy numbers with cloud model theory using novel technique of computing entropy of these fuzzy numbers. A novel mathematical model known as cloud fuzzy numbers is introduced using the concepts of fuzzy numbers and cloud theory. The self-evaluated relative weights of experts are computed using a non-linear optimization method which is based on maximum deviation method and Lagrange multipliers of cloud fuzzy numbers. The new cloud fuzzy numbers are then combined with CODAS (combinative distance based assessment) approach that is based on the largest Euclidean and Taxicab distances for the selection of suitable criteria. Firstly, the linguistics evaluations are converted into the fuzzy numbers and then cloud fuzzy numbers using formulae of expectation and entropy ensuring that the obtained interval cloud values follows a normal distribution. Secondly, the cloud fuzzy weighted arithmetic averaging operator is used to aggregate cloud fuzzy numbers using the self-evaluated fuzzy weights Thirdly, the assessment score is determined to rank the alternatives by computing the distance between normalized weighted matrix and the negative ideal solution. Finally, a case study is discussed for the selection of best renewable energy resource in Turkey to elaborate the significance of the proposed research. The convergence and accuracy of the proposed model is proved with certain mathematical and theoretical results.

Using Nash Equilibrium to Reduce Number of Attacks Between the Arguments in Dung’s Argumentation Framework

For intelligent systems, argumentation is a very important factor to point out in Artificial Intelligence (AI) as it represents an essential component. It is one of the critical approaches to making a decision, whereas Dung introduces a mathematical model that classifies attacks among arguments based on ordered pairs. Furthermore, weights’ addition to the arguments is one of the most important developments. It is done by several methods of weight calculation; hence, the Fuzzy weights method is a suitable method when working with game theory (GT). To this end, this paper introduced the GT in a Nash equilibrium method to redraw the acceptable arguments. It depends on the fuzzy weight for reducing the acceptable arguments and acceptable attacks between those arguments. This paper provides an automatic model for reducing the number of arguments and attacks between them called the shorthand model based on the gaming argumentation framework SGAF by extending the gaming argumentation framework GAF. This work uses the Cash on Delivery (COD) Payment Model as a case study.

A Multivariate Fuzzy Weighted K-Modes Algorithm with Probabilistic Distance for Categorical Data

Data clustering is a data mining approach that assigns similar data to the same group. Traditionally, cluster similarity considers all attributes equally, but in real-world applications, some attributes may be more important than others. Therefore, this study proposes an algorithm that utilizes multivariate fuzzy weighting to demonstrate the varying importance of each attribute, using a Gini impurity measure for weight assignment. Additionally, the proposed algorithm implements probabilistic distance to reduce sensitivity to noise. Probabilistic distance offers more detailed information and better interpretation than Hamming distance, which ignores attribute positions. Probabilistic distance utilizes information about the attribute’s position within and between clusters. This enhances clustering performance by creating clusters with more similar attributes. Therefore, the proposed Multivariate Fuzzy Weighted K-Modes with Probabilistic Distance for Categorical Data (MFWKM-PD) algorithm, based on the multivariate fuzzy K-modes algorithm, not only considers detailed membership calculations but also considers the varying contributions of attributes and their positions in distance calculation. This study evaluated the proposed MFWKM-PD using several benchmark datasets. The experiments validated that the proposed MFWKM-PD shows promising results compared to other algorithms in terms of accuracy, NMI, and ARI.

Assessing airline efficiency with a network DEA model: A Z-number approach with shared resources, undesirable outputs, and negative data

This study measures the efficiency of airlines using a novel fuzzy common weight additive network data envelopment analysis (NDEA) with shared resources, negative data, and undesirable outputs. First, an appropriate two-stage network is designed for each airline so that stages 1 and 2 are called the Production and Service stages, respectively. The proposed model adopts a top-down approach and calculates the efficiency of the system first and then estimates the efficiency of stages 1 and 2. To evaluate and predict the airlines’ efficiency considering fuzzy data and the reliability of the information, the values of input/intermediate/output variables are predicted as the Z-number and the appropriate Z-number version of NDEA (ZNDEA) models is proposed. To develop the proposed ZNDEA models and find common weights for the variables, three multi-objective ZNDEA models for the system, stage 1 and stage 2 are presented. The multi-objective common weight ZNDEA models are solved using the min-max Chebyshev goal programming technique and the final efficiencies are calculated. To illustrate the capability of the proposed approach, real-life data from Iranian airlines in 2022 are collected, and the efficiencies are analyzed.

Cumulative prospect theory under different types of input data for public health resilience assessment during natural disasters

Despite the significance of health and the threats natural disasters pose to public health and the well-being of the affected communities, comprehensive research that explores critical factors and indicators that measure health implications brought about by disasters is underdeveloped in the literature. The primary objective of this study is twofold: (1) to develop a list of indicators that assess health-related impacts during disasters through a literature survey, and (2) to create a “public health resilience index” that measures the resilience of affected communities with a case study in Southern Cebu, Philippines. A hybrid model based on the fuzzy stepwise weight assessment ratio analysis and the extended cumulative prospect theory was deployed to advance these objectives. The proposed model considers subjective judgment and cognitive biases while exhibiting high scalability and flexibility and accommodating diverse data input formats. Results revealed that the promptness of response, the allocated budget for humanitarian aid and services, the availability of drinking water per household, the number of personnel assigned for humanitarian aid, and the location of water distribution points are highly prioritized during natural disasters. On the other hand, among the 21 municipalities under consideration, 10 demonstrated strong performance, while the other 10 exhibited poor performance, leaving one municipality in the middle of both extremes. Sensitivity and comparative analyses show that the proposed hybrid model is not sensitive to model parameter changes. The findings of this study would aid decision-makers in developing initiatives aimed at optimizing resources and efforts, mitigating risks, and enhancing the overall health resilience of local communities in the onslaught of natural disasters.

PSO-DFNN: A particle swarm optimization enabled deep fuzzy neural network for predicting the pellet strength

In addressing the complexity, limited information, and dynamic spatiotemporal characteristics encountered in predicting pellet strength with traditional methods, this study proposes a novel prediction model for the strength of fusible pellets, developed on a Particle Swarm Optimization Deep Fuzzy Neural Network (PSO-DFNN). Initially, the model is constructed by observing and extracting fractal features of the microstructure of pellet ore. Subsequently, the fuzzy system is utilized to partition the spatiotemporal data and generate multi-layer fuzzy rules, thus constructing a deep fuzzy neural network. Lastly, the Particle Swarm Optimization algorithm is employed to optimize the fuzzy membership rule weights, achieving precise prediction of pellet strength. The results indicate a Mean Absolute Error (MAE) of 3.7218 and a Symmetric Mean Absolute Percentage Error (SMAPE) of 3.72 % when predicting pellet strength during the pellet roasting drying stage. The PSO-DFNN model exhibits high prediction accuracy, meeting the needs for pellet strength prediction and providing a more reliable basis for decision-making in the production process.

Application of Improved Class of Weighted Averaging Operators in Intuitionistic Fuzzy Ann Guided MAGDM E-Commerce Problems

Objectives: To propose a novel and improvised Artificial Neural Network (ANN) technique to solve Multiple Attribute Group Decision Making (MAGDM) problems and an improved class of Aggregation operators for combining Intuitionistic Fuzzy Set (IFS) matrices for the ANN algorithm. Methods: A novel class of improved aggregation operators namely the Improved Intuitionistic Fuzzy Weighted Arithmetic Averaging (IM-IFWAA) operator and the Improved Intuitionistic Fuzzy Ordered Weighted Averaging (IM-IFOWA) operator are proposed in this work. The proposed improved class of aggregation operators will aggregate the IFS matrix data sets appearing in the form of matrices and then the revised input vectors which is then fed into the ANN algorithm following Delta, Perceptron, and Hebb Learning Rule for the next phase. Findings: Aggregating the Intuitionistic Fuzzy set information or data in today’s digital world is a tedious task in the field of MAGDM problem-solving. In this work, two new classes of operators for aggregating the data sets are proposed namely the IM-IFWAA operator and the IM-IFOWA operator which will improve the performance of the ANN. Necessary theorems for the proposed operators are proved to provide consistency of the same. The input created using the aforementioned operators is then fed into the novel ANN algorithm for further computations. Varieties of learning rules are then engaged for ranking of the best alternative for the decision problem. The developed theory is supported by a numerical example which is computed using all the proposed techniques. Novelty: Most of the research done on Intuitionistic Fuzzy Artificial Neural Network models are based on learning rules or using some other calculations. The proposed methods of using novel and improvised aggregation operators for ANN are used to find the inputs for ANN where varieties of learning rules for ANN are employed for effective decision analysis. Keywords: ANN, Learning Rules, MAGDM, Intuitionistic Fuzzy Sets, Weighted Aggregation Operator

Enhanced three-dimensional trajectory tracking control for AUVs in variable operating conditions using FMPC-FTTSMC

In addressing the variable operating conditions encountered in complex underwater tasks, an enhanced three-dimensional tracking control method is proposed for autonomous underwater vehicle (AUV) based on fuzzy model predictive control-finite time terminal sliding mode control (FMPC-FTTSMC). Firstly, to enhance adaptability to tasks and environments, fuzzy model predictive control method is designed to achieve precise three-dimensional trajectory tracking. Additionally, a fuzzy weight allocator is employed to enable AUV to autonomously adjust optimization strategies based on real-time states such as task type, speed, and distance from the seabed, thus better coping with changing conditions and improving the universality of the control method. Secondly, a dynamic controller is designed using the finite-time terminal sliding mode control method, combined with a finite-time radial basis function neural network (FTRBFNN) disturbance observer to accurately estimate disturbances. Finally, simulation results demonstrate that the proposed method effectively reduces optimization solving time compared to traditional model predictive control, achieves trajectory tracking control under various conditions, meets preset state constraints, and demonstrates excellent tracking accuracy and robustness.

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Abstract Accurate location of unknown radio transmitter (URT) is the key to secure wireless communication. Since the fingerprint positioning methods based on received signal strength difference (RSSD) can adapt to the diversity of transmitting power and frequency, RSSD has become a popular scheme for locating the unknown radio transmitter. However, the RSSD is obtained by subtracting the RSS from two different access points (APs), so the interference of noise on the RSS is inherited and amplified by the RSSD. Besides, the need for more APs to ensure positioning accuracy leads to an increase in hardware costs. In this paper, a RSSD-based fuzzy weight grey correlation degree positioning algorithm, called FUZZY-GREY, is proposed to reduce the interference of noise, save AP hardware cost and improve the positioning accuracy. Firstly, online RSSD vector is improved by using fuzzy weight to reduce the noise interference. Secondly, the RSSD-based grey correlation coefficient is designed to calculate the correlation degree of the corresponding RSSD and ensure data integrity. Finally, a RSSD-based grey correlation degree scheme combining with fuzzy weight is proposed to select optimal reference points (RPs). Simulation and experimental results show that the proposed algorithm has better positioning performance than weighted k-nearest neighbor (WKNN), maximum correlation coefficient estimation (MCORE), Naive Bayes and support vector machine (SVM) in the case of different selected K numbers, grid distances, noise levels and AP numbers.

Flood Susceptibility Mapping Using Information Fusion Paradigm Integrated with Decision Trees

Accurate estimation of flood-damaged zones in a watershed is prominent in guiding a framework for developing sustainable strategies. For these purposes, several flood conditioning factor values at flooded and non-flooded points are extracted, and those points are analyzed using decision tree algorithms and eight novel information fusion techniques to get more reliable flood susceptibility mapping. The belief function values of flood susceptibility values at leaf nodes of the tree are fused by several techniques named Dempster-Shafer (DS), Fuzzy Gamma Overlay (FGO), Hesitant Fuzzy Weighted Averaging (HFWA), Hesitant Fuzzy Weighted Geometric (HFWG), Hesitant Fuzzy Weighted Ordered Averaging (HFWOA), HFWOG, Closeness coefficient (Cc) using Euclidean and Manhattan distances. The flood susceptibility values are extracted from the generated maps and are validated by receiver operating characteristics (ROC) curve parameters, and the seed cell area index (SCAI) of classified flood levels. The area under ROC (AUROC) values of training process are 0.997 for DS, HFWA, HFWOA, and Cc-Euclidean, 0.996 for Cc-Manhattan, 0.995 for FGO and 0.994 for HFWG and HFWOG. The AUROC values of the testing process are 0.951 for DS, HFWA, HFWOA, Cc-Euclidean, and Cc-Manhattan, 0.945 for FGO, 0.943 for HFWG, and 0.941 for HFWOG. True Skill Statistics values are 0.962 and 0.870 for training and testing processes. Although these techniques present excellent performance, the SCAI values versus flood susceptibility classes are fitted to assess the prediction capabilities of the techniques further. HFWA and HFWOG have the first- and second-best performances on the estimations. Hence, information fusion paradigm can be employed to combine flood conditioning factors based on a robust classification method to get reliable predictions of flood potential levels and utilize them for land use and construction planning and management.

Promoting inclusivity in education amid the post-COVID-19 challenges: An interval-valued fuzzy model for pedagogy method selection

The advent of the COVID-19 pandemic has brought about unprecedented disruptions in the field of education, necessitating a reevaluation of pedagogical approaches in the post-COVID era. This research paper introduces a novel interval-valued fuzzy simple additive weighting (SAW), weighted product model (WPM), and weighted aggregates sum product assessment (WASPAS) multi-criteria decision-making (MCDM) framework to address the challenge of promoting inclusivity in education amidst the post-COVID era. Leveraging the uncertainty inherent in the post-pandemic educational landscape, the proposed method offers a comprehensive pedagogy selection approach incorporating interval-valued fuzzy sets to account for imprecise and ambiguous data. By integrating the principles of inclusivity and diversity, the method evaluates various pedagogical approaches and their effectiveness in fostering an inclusive learning environment for diverse student populations. The study showcases the application of pedagogy selection in real-world educational scenarios, demonstrating its potential to inform policy decisions and enable educational institutions to adapt and cater to the evolving needs of learners in the aftermath of the COVID-19 pandemic.

Evaluating renewable energy projects using fuzzy bipolar soft aggregation and entropy weights

Evaluating renewable energy projects using fuzzy bipolar soft aggregation and entropy weights

Fuzzy Neighborhood-Based Manifold Learning and Feature Weight Matrix for Multilabel Feature Selection

In recent years, it has been difficult to determine neighborhood radii with almost all feature selection methodologies, and the existing multilabel feature selection approaches neglect these correlations among labels, which results in poor classification efficiency. To address these flaws, a fuzzy neighborhood-based manifold learning model and a feature weight matrix are developed to study multilabel feature selection in this article. First, the difference coefficient of the given samples and the new classification margin of each sample are defined to construct an adaptive fuzzy neighborhood radius. While considering the influences of features and labels on the similarity relationships of samples, a novel fuzzy neighborhood similarity relationship is developed by combining it with the label similarity approach that uses the Jaccard similarity coefficient. Second, the least-squares regression model is introduced to design a loss function for learning the mapping relationship between the feature and label spaces. To constrain the objective function, a regularization term based on the fuzzy neighborhood similarity relationship matrix is developed to establish a manifold learning model. To reduce the complexity of the loss function and to reduce the induced structural risk, the final objective function is constructed by incorporating two graph Laplacian matrices. Third, positive and negative confidence values are presented to incorporate the frequency parameter and to form the dependencies between labels into a new label correlation matrix. A new feature weight matrix integrated with the dependencies between labels is defined to learn the correlations between features and labels. Finally, an embedded feature selection method with an objective function is designed, where the objective function is optimized by the gradient descent scheme. Experiments conducted on 12 multilabel datasets reveal that the proposed approach yields better classification results than do other algorithms.

Enhancing Contractor Selection through Fuzzy TOPSIS and Fuzzy SAW Techniques

Contractors play an integral role in construction projects, and their qualifications directly impact various aspects of a project’s success. The unbiased selection of contractors is a challenge in the construction industry worldwide, particularly in public projects where impartiality in the final selection is essential. Numerous factors must be considered when evaluating contractors, making the selection process challenging for the human brain. This paper introduces and compares two methods for assessing contractor prequalification by applying the fuzzy theory. The idea is to facilitate using human judgments in a mathematical system for decision-making with regard to selecting contractors. The method is based on identifying a fuzzy weight for the selection criteria using the Buckley method. Fuzzy TOPSIS and Fuzzy SAW methods are then used for the qualification ranking of the contractors. The proposed models are assessed using a case study. A sensitivity analysis was also conducted to compare the two models. The introduced method is expected to improve the quality of the qualification-based selection of contractors and prevent possible losses from hiring unsuitable contractors.

Vulnerability assessment of landslides along the Yunnan section of the Northern Tropic of Cancer based on fuzzy evidence weight model

AbstractFrequent geological disasters pose significant threats to both human lives and property. High-risk landslides, with features such as rapid occurrence, wide distribution, and strong destructiveness, are of particular concern. To investigate the distribution characteristics and threat range of landslides, the Yunnan section of the Northern Tropic of Cancer was selected as the study area. Based on the fuzzy evidence weight model and using a grid size of 1000 m × 1000 m as the study unit, a vulnerability assessment of landslides along the Northern Tropic of Cancer in Yunnan was conducted. This assessment involved constructing a set of 13 evaluation indicator factors from three aspects: geological environment, geographical environment, and human engineering activities. The assessment results were subsequently subjected to precision verification. The results are as follows: (1) The overall vulnerability of landslides in the Yunnan section of the Northern Tropic of Cancer is moderate, covering an area of 25,968.79 km2. This area constitutes the largest proportion, accounting for 46.30% of the entire study area and is primarily distributed in counties such as Yanshan, Funing, and Shuangjiang. The highly vulnerable area covers 15,942.27 km2, representing 28.43% of the total study area. This region is mainly distributed in counties such as Gejiu, Yuanjiang, and Mojiang. (2) The main triggering factors for landslides are twofold: first, large-scale slope instability caused by excavation in human engineering activities, leading to extensive unstable slopes affected by long-term weathering and gravitational effects. Second, soil erosion resulting from land cultivation leads to the collapse and sliding of weathered and fragmented rocks under the influence of heavy rainfall and water erosion. During routine geological disaster patrols, special attention should be given to these factors, with a focus on implementing engineering measures in areas with more fragile geological environments. (3) Precision verification of the assessment results was conducted using both receiver operating characteristic (ROC) curves and Success rate curves. The area under the curve (AUC) values for the success rate curve and ROC curve were 0.85 and 0.74, respectively. The verification results showed high accuracy and feasibility of the evaluation method.

Risk Evaluation and Attack Detection in Heterogeneous IoMT Devices Using Hybrid Fuzzy Logic Analytical Approach.

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, and vulnerabilities. Hence, to achieve the goal of having risk-free IoMT devices, the authors used a hybrid approach using fuzzy logic and the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate risks, providing effective and useful results for developers and researchers. The presented approach specifies qualitative descriptors such as the frequency of occurrence, consequence severity, weight factor, and risk level. A case study with risk events in three different IoMT devices was carried out to illustrate the proposed method. We performed a Bluetooth Low Energy (BLE) attack on an oximeter, smartwatch, and smart peak flow meter to discover their vulnerabilities. Using the FAHP method, we calculated fuzzy weights and risk levels, which helped us to prioritize criteria and alternatives in decision-making. Smartwatches were found to have a risk level of 8.57 for injection attacks, which is of extreme importance and needs immediate attention. Conversely, jamming attacks registered the lowest risk level of 1, with 9 being the maximum risk level and 1 the minimum. Based on this risk assessment, appropriate security measures can be implemented to address the severity of potential threats. The findings will assist healthcare industry decision-makers in evaluating the relative importance of risk factors, aiding informed decisions through weight comparison.

A novel fuzzy scenario-based stochastic general best-worst method

The best-worst method (BWM) is a popular multi-criteria decision-making (MCDM) method known for the low number of pairwise comparisons and high consistency. General BWM (GBWM) is a new version of BWM that considers the interdependencies between interwind factors in MCDM problems. This study proposes a fuzzy stochastic GBWM for weighting intertwined factors using a scenario-based approach in complex intertwined or hierarchical networks under uncertainty. Fuzzy stochastic GBWM provides decision-makers with a wide range of weights, from fuzzy-stochastic weights to stochastic weights (defuzzified weights), fuzzy weights, and deterministic weights to use with different assumptions in the decision-making process. We demonstrate the efficacy and applicability of the proposed method with a well-known car-buying problem in the literature and a real-world problem in the transportation industry.

Analyzing fuzzy semantics of reviews for multi-criteria recommendations

Hotel reviews play a vital role in tourism recommender system. They should be analyzed effectively to enhance the accuracy of recommendations which can be generated either from crisp ratings on a fixed scale or real sentiments of reviews. But crisp ratings cannot represent the actual feelings of reviewers. Existing tourism recommender systems mostly recommend hotels on the basis of vague and sparse ratings resulting in inaccurate recommendations or preferences for online users. This paper presents a semantic approach to analyze the online reviews being crawled from tripadvisor.in. It discovers the underlying fuzzy semantics of reviews with respect to the multiple criteria of hotels rather than using the crisp ratings. The crawled reviews are preprocessed via data cleaning such as stopword and punctuation removal, tokenization, lemmatization, pos tagging to understand the semantics efficiently. Nouns representing frequent features of hotels are extracted from pre-processed reviews which are further used to identify opinion phrases. Fuzzy weights are derived from normalized frequency of frequent nouns and combined with sentiment score of all the synonyms of adjectives in the identified opinion phrases. This results in fuzzy semantics which form an ideal representation of reviews for a multi-criteria tourism recommender system. The proposed work is implemented in python by crawling the recent reviews of Jaipur hotels from TripAdvisor and analyzing their semantics. The resultant fuzzy semantics form a manually tagged dataset of reviews tagged with sentiments of identified aspects, respectively. Experimental results show improved sentiment score while considering all the synonyms of adjectives. The results are further used to fine-tune BERT models to form encodings for a query-based recommender system. The proposed approach can help tourism and hospitality service providers to take advantage of such sentiment analysis to examine the negative comments or unpleasant experiences of tourists and making appropriate improvements. Moreover, it will help online users to get better recommendations while planning their trips.

Application of ensemble fuzzy weights of evidence-support vector machine (Fuzzy WofE-SVM) for urban flood modeling and coupled risk (CR) index for ward prioritization in NCT Delhi, India

Application of ensemble fuzzy weights of evidence-support vector machine (Fuzzy WofE-SVM) for urban flood modeling and coupled risk (CR) index for ward prioritization in NCT Delhi, India