Fuzzy Rule-based Approach Research Articles

Retraction Note: Intelligent fuzzy rule-based approach with outlier detection for secured routing in WSN

Retraction Note: Intelligent fuzzy rule-based approach with outlier detection for secured routing in WSN

Global warming communicative actions of publics in Türkiye: Utilizing fuzzy rule based system

The effectiveness of government policies and environmental initiatives to mitigate global warming relies heavily on public support, which is closely tied to public perception and awareness. Despite the scientific evidence communicated, the public remains reluctant to take preventive measures against global warming. The aim of the paper is to investigate the communicative actions of publics proposed as in the situational theory of problem solving to understand publics' communicative actions towards global warming. The paper utilizes a fuzzy rule-based system approach to analyze the communicative actions of publics to reveal non-linear relationships; whereas previous studies mostly used linear statistical analysis. The paper provides a deeper understanding into the interplay between problem recognition, constraint recognition, and involvement in shaping information behavior. The results show that the communicative actions of the publics are at a low-to-moderate level. The paper's interesting finding is the nonlinear effects of constraint recognition on communicative action about global warming. Contrary to the current literature, it was found out that the dominant factor that may convince public to start taking action towards global warming seems to be recognizing being constrained at a moderate level. Based on the results, it is suggested for policy makers and communication strategists to mitigate the negative outcomes of global warming by integrating environmental issues into education at all levels and collaborating with non-governmental organizations for national awareness campaigns which focus on increasing public problem recognition and involvement.

Fuzzy Contrast Enhancement Techniques Using Rule-Based System and Intensification Operator

Contrast enhancement is a crucial step in digital image processing to ensure the production of high-quality images. This paper presents two distinct fuzzy techniques designed to address the nuanced concept of contrast. that utilize rule-based system and intensification operator, respectively, to handle the inherent vagueness associated with the concept of contrast. The fuzzy rule-based system calculates the intensity value of the output image based on the contrast measure (standard deviation) of the input image, thereby enabling effective contrast enhancement for low contrast images. Meanwhile, the fuzzy intensification operator aims to accentuate the differences in intensity levels within the image, thereby enhancing the contrast between various features and details. The experimental results show better image contrast is obtained using both methods. However, fuzzy rule-based approach shows more images with better quality compared to the intensification operator approach. The performance of these techniques is evaluated both visually and quantitatively.

An Investigation of the Current Situation of Floodplain Mapping in British Columbia: A Fuzzy Rule-Based Approach

An Investigation of the Current Situation of Floodplain Mapping in British Columbia: A Fuzzy Rule-Based Approach

A new fuzzy rule-based optimization approach for predicting the user behaviour classification in M-commerce

<p class="MsoNormal" style="text-align: justify;"><span>A novel approach for classification of user behaviour prediction using proposed embracing the optimized fuzzy techniques to predicting the user data in M-commerce. Using this technique, network users can be monitored and their behavior categorized according to their activity. Unauthorized use of the website, network security breach attempts, firewalls, unauthorized access to the service and frequency of attempts. The proposed method has been adapted with the user classification to predict the predefine segregation of information to extract from user logs. Pattern recognition is a method for information discovery that results in current information patterns. Continuing items are a required task in various knowledge mining operations in pursuit of fascinating types from the data banks, including association rules, connections, sequences, episodes, classifications, bunches and much more. The functionality findings achieved in relation to precision and recall show that our technique can contribute to predicting more accurately than the different approaches. This paper focuses on to enhance the far better forecast for the mobile phone users through locating more reliable frequent patterns coming from the consumer deal data bank through looking at the body weight value of each thing collection and also examining the consumer activities on all time intervals.</span></p>

A model proposal for measuring service quality of eduaction with fuzzy rule-based approach and fuzzy ranking and an application

In today’s understanding, the universities are considered as service providers besides their institutional functions. Because the universities shape the future of the country via the services they provide, it is a necessity that their service quality must be assessed by using scientific analyses, and their service quality must be improved based on such scientific findings. The Generation Z, whose members are currently receiving university education carries unique features that distinguish them from the previous generations. When this fact is considered, it is understood that the constant research and monitoring of the learning environment of the Generation Z is important. In this study, as a result of a detailed literature search, a scale consisting of 7 dimensions and 36 indicators was developed in order to measure the higher education service quality of the Z generation. The validity and reliability tests of this scale are completed via the convergent and divergent validity analyses, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). Because the answers provided to the surveys reflect the personal evaluation of the participants, the Fuzzy Logic is employed, and the study is conducted by using the fuzzy modelling and fuzzy ranking. As a result of this study, the General Satisfaction Index is created, and improving recommendations are carried out based on the scores.

An Efficient JSH-FCM-Based Thyroid Disease Detection Using Ash-Ann with Stage Classification via a Fuzzy Rule-Based Approach

One of the most misunderstood and undiagnosed diseases is termed Thyroid Disease (TD), which is a subset of endocrinology. It emerges at the edge of the thyroid gland due to the abnormal development of thyroid tissue. Owing to the lack of awareness and early diagnosis, TD is a critical problem in underdeveloped countries. For TD diagnosis, various theoretical works have been introduced; still, in the early diagnosis of TD, accurate prediction of the thyroid data is a significant problem. Thus, by utilizing Altered SigHyper activation-centric Artificial Neural Network (ANN) (ASH-ANN) with various stage classifications, an effectual Jaccard Similarity and He-initialization induced Fuzzy C-Means (FCM) (JSH-FCM) clustering-centric TD detection system is proposed by means of a fuzzy rule-centric methodology. Initially, for accurate detection, the thyroid dataset is gathered and the data is pre-processed. Next, by JSH-FCM clustering, the age-centric clustering is carried out. After that, by utilizing Pearson Correlation-amalgamated Principal Component Analysis ((PC)2A), Feature Extraction (FE) and feature selection is conducted. Moreover, to detect the TD kind, an ASH-ANN classifier is wielded. Finally, for differentiating the stages of TD, the fuzzy rule is employed. The experimental outcomes depict that the proposed system achieved superior performance with an accuracy of 97.32% when weighed against the prevailing system; in addition, the stages of TD are differentiated precisely.

Evaluation of fuzzy membership functions for linguistic rule-based classifier focused on explainability, interpretability and reliability

Objective:Decision support systems focus on their interpretability with a different caution. The majority of approaches utilize reliable optimization techniques to achieve high recognition accuracy. However, accuracy improvement is done at the cost of the readability of the decision support system. This paper proposes a novel technique of linguistic rule extraction focused on: explainability, interpretability, and reliability, called EasIeR. Methods:Proposed solution performs a direct analysis of the learning set without omitting real data imperfections. The proposed algorithm processes the imprecision of data with linguistic values described by fuzzy membership functions and the uncertainty of knowledge employing a fuzzy belief function. It describes the learning set with the most appropriate fuzzy if-then rules. For this purpose, a new method of evaluation of membership functions in a set of rules and their iterative refinement is proposed. Results:EasIeR achieves the best results of trade-off criteria between the generalization quality and the interpretability of the knowledge base when compared with state-of-art fuzzy rule-based classification approaches. Achieving the simplest rule base and competitive generalization quality is verified by cross-validation experiments with benchmark data sets. Significance:The new method of rule evaluation allows for better determination of their quality in decision support, and the selection algorithm quickly provides a clear and easy to interpret knowledge base. This rule evaluation technique will be proficient for all fuzzy rule-based classification systems.

Applied Picture Fuzzy Sets for Group Decision-Support in the Evaluation of Pedagogic Systems

Evaluation of E-Learning resources plays a significant role in the context of pedagogic systems. Resource evaluation is important in both conventional ‘talk-and-chalk’ teaching and in blended learning. In on-line (e-learning) teaching [an enforced feature of pedagogic systems in tertiary education during the Covid-19 pandemic] the effective evaluation of teaching resources has obtained importance given the lack of ‘face-to-face’ student-teached interaction. Moreover, the enforced use of e-learning has demonstrated the effectiveness of on-line pedagogic systems, which has been argued in blended learning pedagogic systems. Additionally, in e-learning, the lack of ‘face-to-face’ meetings [between teaching staff and students and in staff meetings] makes feedback (positive and negative) important for all actors in the pedagogic system. In this paper we present a novel approach to enable effective evaluation of teaching resources, which provides effective group decision-support designed to evaluate e-learning resources, enhancing students’ satisfaction. The proposed approach employs Picture Fuzzy Sets to quantify survey responses from actors, including: agree, disagree, neutral, and refuse to answer. In our approach, the system can manage the evaluation of e-learning resources based on both explicit and tacit knowledge using a picture fuzzy rule-based approach in which linguistic semantic terms are used to express rules and preferences. The proposed system has been tested using e-learning case studies with the goal of enhancing the learning experience and increasing students' satisfaction. Experimental results demonstrate that our proposed approach achieves a significant improvement in performance in the evaluation of e-learning resources.

A fuzzy rule-based approach via MATLAB for the CDR instrument for staging the severity of dementia

Background: The CDR scale is a standard qualitative staging instrument that has been widely applied for assessing the severity of dementia which is based on information elicited through a semi-structured interview standardized in an assessment protocol. Despite clinical skills to elicit appropriate information are required, subjectivity still lies in the administration of the protocol and scoring process of the CDR. In this paper we propose a fuzzy rule-based CDR instrument to stage dementia based on the usual CDR, aiming to cover the subjectivities of the scoring process in the usual CDR which are directly related to the scoring system. This is effectively achieved by the F-CDR, our proposed expert system, which allows assigning scores continuously throughout the interval [0,3].Results: In order to test the performance of our fuzzy model, we compare the outputs FCDR obtained from of F-CDR approach to the outputs U-CDR obtained by a usual application of the CDR via the same inputs for both. The dataset provided by ADNI, composed of more than eleven thousand CDR tests, including the inputs and outputs (U-CDR), is the source for comparisons.Methods: The fuzzy rule-based model for the CDR that we propose in this paper is a fuzzy inference system (FIS) constructed in MATLAB with the aid of the Fuzzy Logic Designer app. The FIS was constructed based on the CDR and the specialist’s indications and tested on real data provided by ADNI.Conclusion: The high accuracy of matches between U-CDR and F-CDR via the same inputs over random samples selected from the ADNI dataset suggests that the fuzzy approach to the CDR instrument here proposed is suitable to extend the scoring process of the usual CDR since the fuzzy approach allows the possibility of scoring continuously in the interval [0,3].

A fuzzy rule-based multi-criterion approach for a cooperative green supplier selection problem.

Multi-criterion decision-making models are widely used in supplier selection problems. This study contributes to a green supplier selection problem considering the green manufacturing, green transportation, and green procurement. This study contributes to reverse logistics, eco-design, reusing, recycling, and remanufacturing with their high impact on the industries. In addition to the logistics costs and transportation costs, the carbon emissions are considered. With regard to the game theory, this paper uses a cooperative green supplier selection model. If transportation requirements of two or more companies are combined, it will help manufacturers to have less {CO}_{2} emissions with lower cost. After creating the optimization model to consider the uncertainty, this cooperative game theory model is established in a fuzzy environment. In this regard, a fuzzy rule-based (FRB) system is deployed and the set of fuzzy IF–THEN rules is considered. The proposed FRB model is contributed for the first time in the area of green supplier selection problem. Finally, some sensitivity analyses are conducted in a numerical example to evaluate the proposed model. With regard to the findings, although the cost of CO2 emission of horizontal cooperation is increased, the cost saving of companies is increased. It means our total cost is optimal in a logistic network using the cooperative game theory. The results also indicate that horizontal cooperation in logistic network causes less cost and benefits for each company.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-17015-2.

A Decentralized Fuzzy Rule-Based Approach for Computing Topological Relations between Spatial Dynamic Continuous Phenomena with Vague Boundaries Using Sensor Data.

Sensor networks (SN) are increasingly used for the observation and monitoring of spatiotemporal phenomena and their dynamics such as pollution, noise and forest fires. In multisensory systems, a sensor node may be equipped with different sensing units to observe and detect several spatiotemporal phenomena at the same time. Simultaneous detection of different phenomena can be used to infer their spatial interactions over space and time. For this purpose, decentralized spatial computing approaches have shown their potential for effective reasoning on spatial phenomena within a sensor network. However, in most cases, spatial extents of continuous dynamic phenomena are uncertain, and their relations and interactions cannot be inferred by the existing approaches at the sensor node level. To address this limitation, in this paper, we propose and develop a decentralized fuzzy rule-based spatial reasoning approach to depict the spatial relations that hold between two evolving spatial phenomena with fuzzy boundaries. The proposed method benefits from a more adapted fuzzy-crisp representation of dynamic phenomena observed by SN where each vague phenomenon is composed of five distinguished zones including the kernel, conjecture and exterior zone and their boundaries. For each detected phenomenon, a sensor node will report one of these zones based on its location. Aggregation of the information reported from the sensor nodes allows reasoning on spatial relations between the observed phenomena and their evolution. Such spatial information provides users with more valuable near real-time information on the state of different phenomena that can be used for informed decision-making.

Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-Based Approach

Here, we propose an unsupervised fuzzy rule-based dimensionality reduction method primarily for data visualization. It considers the following important issues relevant to dimensionality reduction-based data visualization: (i) preservation of neighborhood relationships, (ii) handling data on a non-linear manifold, (iii) the capability of predicting projections for new test data points, (iv) interpretability of the system, and (v) the ability to reject test points if required. For this, we use a first-order Takagi-Sugeno type model. We generate rule antecedents using clusters in the input data. In this context, we also propose a new variant of the Geodesic c-means clustering algorithm. We estimate the rule parameters by minimizing an error function that preserves the inter-point geodesic distances (distances over the manifold) as Euclidean distances on the projected space. We apply the proposed method on three synthetic and three real-world data sets and visually compare the results with four other standard data visualization methods. The obtained results show that the proposed method behaves desirably and performs better than or comparable to the methods compared with. The proposed method is found to be robust to the initial conditions. The predictability of the proposed method for test points is validated by experiments. We also assess the ability of our method to reject output points when it should. Then, we extend this concept to provide a general framework for learning an unsupervised fuzzy model for data projection with different objective functions. To the best of our knowledge, this is the first attempt to manifold learning using unsupervised fuzzy modeling.

An intelligent hybrid classification algorithm integrating fuzzy rule-based extraction and harmony search optimization: Medical diagnosis applications

Uncertainty is a critical factor in medical datasets needed to be overcome for increasing diagnosis efficiency. This paper proposes an intelligent classification algorithm comprising a fuzzy rule-based approach, a harmony search (HS) algorithm, and a heuristic algorithm to classify medical datasets intelligently. Two fuzzy approaches, as well as orthogonal and triangular fuzzy sets, are first utilized to define the attributes of data. Then, an HS algorithm is integrated with a heuristic to generate fuzzy rules to select the best rules in the fuzzy rule-based systems. Moreover, to improve the performance of the proposed classification approach, a three-phase parameter tuning approach is applied. First, the Taguchi method (phase I) is employed to tune the parameters of the HS algorithm using a fixed number of training data and find the central points of the parameters’ values. Then, a nested cross-validation (CV) approach consisting of an outer CV (phase II) and an inner CV (phase III) is utilized. Using the Taguchi approach gives the advantage of not considering a wide range of parameters by the nested CV which produces better results on the medical dataset. Nine well-known medical datasets are used to evaluate the efficiency of the proposed hybrid algorithm. To this aim, the results obtained by the algorithm are compared with the ones of several related works in the literature where several statistical tests and graphical approaches are used for comparisons. The results show that the proposed methods are robust in data analysis and classification of clinical datasets.

Granular Classification for Imbalanced Datasets: A Minkowski Distance-Based Method

The problem of classification for imbalanced datasets is frequently encountered in practical applications. The data to be classified in this problem are skewed, i.e., the samples of one class (the minority class) are much less than those of other classes (the majority class). When dealing with imbalanced datasets, most classifiers encounter a common limitation, that is, they often obtain better classification performances on the majority classes than those on the minority class. To alleviate the limitation, in this study, a fuzzy rule-based modeling approach using information granules is proposed. Information granules, as some entities derived and abstracted from data, can be used to describe and capture the characteristics (distribution and structure) of data from both majority and minority classes. Since the geometric characteristics of information granules depend on the distance measures used in the granulation process, the main idea of this study is to construct information granules on each class of imbalanced data using Minkowski distance measures and then to establish the classification models by using “If-Then” rules. The experimental results involving synthetic and publicly available datasets reflect that the proposed Minkowski distance-based method can produce information granules with a series of geometric shapes and construct granular models with satisfying classification performance for imbalanced datasets.

Risk Assessment Model for Pluvial Flood Prediction Using Fuzzy-Based Classification Technique

Both developed and developing countries are promoting risk management and refining the ability to alleviate the effects of disaster both man-made and natural, which have become a threat to human life and the world’s economy. The variability in climate change, rapid urbanization and fast-growing socio-economic development has naturally increased the risk associated with flooding. A recent report showed that flood have affected more individuals than any other category of disaster in the 21st century with the highest percentage of 43% of all disaster events in 2019 and Africa been the second vulnerable continent after Asia. So, it is highly important to devise a scientific method for flood risk reduction since it cannot be eradicated. Machine learning can improve the risk management. The paper proposes a pluvial flood detection and prediction system based on machine learning techniques. The proposed model will employ a fuzzy rule-based classification approach for pluvial flood risk assessment. Keywords : Machine Learning, Pluvial Flood, Risk, Fuzzy Rule-Based, Prediction DOI: 10.7176/CEIS/12-1-07 Publication date: January 31 st 2021

A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients.

Coronavirus disease-19 (COVID-19) is a very dangerous infectious disease for the entire world in the current scenario. Coronavirus spreads from one person to another person very rapidly. It spreads exponentially throughout the globe. Everyone should be cautious to avoid the spreading of this novel disease. In this paper, a fuzzy rule-based approach using priority-based method is proposed for the management of hospital beds for COVID-19 infected patients in the worst-case scenario where the number of hospital beds is very less as compared to the number of COVID-19 infected patients. This approach mainly attempts to minimize the number of hospital beds as well as emergency beds requirement for the treatment of COVID-19 infected patients to handle such a critical situation. In this work, higher priority has given to severe COVID-19 infected patients as compared to mild COVID-19 infected patients to handle this critical situation so that the survival probability of the COVID-19 infected patients can be increased. The proposed method is compared with first-come first-serve (FCFS)-based method to analyze the practical problems that arise during the assignment of hospital beds and emergency beds for the treatment of COVID-19 patients. The simulation of this work is carried out using MATLAB R2015b.

Fuzzy rule base approach to evaluate performance of hydrodynamic cavitation for borewell water softening

An attempt has been carried out to remove hardness of borewell water by hydrodynamic cavitation. The performance of this approach is dependent on multiple parameters like pressure, orifice diameter, velocity, pipe material, etc. Under such circumstances it is difficult to decide most significant parameter governing efficiency of hydrodynamic cavitation. Observations were recorded at various pressures ranging from 1 bar to 10 bar. To achieve cavitation, venture meter and orifice with varying diameter were used. With varying pipe diameter and flow the efficiency of hardness removal was analyzed.In the present article assessment of most significant combination to remove hardness using hydrodynamic cavitation approach is carried out using Fuzzy Rule Base approach. Total 60 rules were formulated. A Mamdani type; Min-Max; Fuzzy method and triangular membership functions were adopted in fuzzification.

Cooperative sustainable supply chain for deteriorating item and imperfect production under different carbon emission regulations

Operational adjustments in a supply chain and efficient inventory management, especially when demand is uncertain, can turn out to be an effective way in emission reduction. Different emission regulation strategies influence the firms to redesign their supply chain for sustainable management. This paper develops a sustainable supply chain inventory management (SSCIM) model for a single-manufacturer and a single-buyer’s integrated approach by considering item deterioration and imperfect production simultaneously. Rework facility of produced imperfect items is available. Carbon emissions through production, transportation, warehousing and disposal of the deteriorated items are considered. Our objective here is to implement different regulatory policies (mainly carbon tax, carbon cap-and-offset and cap-and-trade) of emission reduction which mitigate the effect of carbon emission by reducing its volume. We also focus on finding a trade-off between overall generated cost and carbon emission. Hence, the mathematical model of proposed SSCIM is formulated in bi-objective framework for the purpose to minimize the operational cost and carbon emission. Furthermore, fuzzy rule base approach, which is based on some existing survey records over the past years, is employed to predict the customers’ demand. The derived mathematical model and solution methodology are discussed and thereby illustrated with the help of numerical examples. Sensitivity analysis accounting to changes in the parameter values is also examined to highlight the managerial insights.

A Fuzzy Rule-Based Approach for Automatic Irrigation System through Controlled Soil Moisture Measurement

A Fuzzy Rule-Based Approach for Automatic Irrigation System through Controlled Soil Moisture Measurement