Fuzzy Rule-based System Research Articles

Refined Segmentation of K-Means Cluster Using Hidden-Markov Random Field (HMRF) for Leukemia Detection

Abstract: An important part in detecting and diagnosing leukaemia involves the microscopic examination of a patient’s blood sample. The type of malignant cells called the blasts is frequently determined by morphological alterations in White Blood Cells (WBCs). Real-time procedures are time-consuming, error-prone, and expensive. In order to overcome this problem the features such as the colour, shapes and texture can be extracted from the original image. This paper deals with the segmentation techniques. The segmentation process in automated approach is developed using k-means clustering (KMC) and Hidden-Markov Random Field (HMRF). This is also termed as Refined Segmentation (Re-Se). This analysis includes Aspirate Smear Images (ASI). First cell segmentation using a KMC is performed, then a Cell Image Representing Model (CIRM) using HMRF is build, estimating prototype variables using Expectation Maximization (EM) ratio, iterating until the best value is found, and finally achieving next phase called Refined Segmentation (Re-Se). Then, features including texture, geometry, colour, and statistical information are retrieved and used to categorize leukaemia cells from WBCs, by using Fuzzy Rule-Based Decision Support System (FRDSS). By incorporating more feature extraction techniques, the suggested method improves a blast of most immature cells identification for the Acute Myeloid Leukemia (AML) detection

Fuzzy Boundary for Beams Vibrations in Vehicles

The aim of this work is to study the transversal motion of a car's chassis, modeled by a beam and supported at its ends by two set of spring-damper element. We use a finite differences method with Robin boundary conditions at the ends to solve the mathematical modeling. The boundary conditions constants are modeled by a fuzzy parameter, which is obtained through Fuzzy Rule-Based System (FRBS) acting on the different vibrations of the model depending on the number of spirals and time of use of the spring. The contributions in this study come in the sense of considering the elastic constant of the spring as a relevant instrument acting in the system, because the analytical formula to perform these calculations does not take into consideration the time of use of it. Indeed, the mechanical components of the vehicle might have their properties altered due to fatigue process, for example. The impacts of the property evolution in the car dynamics might induce undesirable passenger's discomfort up to the vehicle performance lost. The results showed that the time of use of the spring influences the displacement of the vehicle, due to the loss of stiffness, thus affecting its stability.

Novel Method for Safeguarding Personal Health Record in Cloud Connection Using Deep Learning Models.

It is a new online service paradigm that allows consumers to exchange their health data. Health information management software allows individuals to control and share their health data with other users and healthcare experts. Patient health records (PHR) may be intelligently examined to predict patient criticality in healthcare systems. Unauthorized access, privacy, security, key management, and increased keyword query search time all occur when personal health records (PHR) are moved to a third-party semitrusted server. This paper presents security measures for cloud-based personal health records (PHR). The cost of keeping health records on a hospital server grows. This is particularly true in healthcare. As a consequence, keeping PHRs in the cloud helps healthcare institutions save money on infrastructure. The proposed security solutions include an optimized rule-based fuzzy inference system (ORFIS) to determine the patient's criticality. Patients are classified into three groups (sometimes known as protective rings) based on their severity: very critical, less critical, and normal. In trials using the UCI machine learning archive, the new ORFIS outperformed existing fuzzy inference approaches in detecting the criticality of PHR. Using a graph-based access policy and anonymous authentication with a NoSQL database in a private cloud environment improves data storage and retrieval efficiency, granularity of data access, and response time.

Steam Package Boiler Expert System for Control and Maintenance of Fertilizer Plants using Rule-Base Fuzzy Logic

Generally, expert systems have been found very useful branch of artificial intelligence that makes extensive use of specialized knowledge to solve complex problems and even in fertilizer plants it has been deployed in handling operations in critical sections, such as material handling systems, online leak detection systems, granulation, air compressor among others. This paper presents research work for steam package boiler expert system for control and maintenance of fertilizer plants using rule-base fuzzy logic hybrid system, which has not been benefited much from expert system. The system handles cause of boiler failures in terms of controlling and maintaining the functional chemical components of the boiler drum and feed water parameters. validation on the system consistency, correctness, and its precision with six (6) steam package boiler parameters test value cases was conducted involving fourteen (14) fertilizer plant boiler domain partitioners. The boiler drum and feed water qualities with less or higher test value worst-cases validates the boiler system, showing each of the parameter’s bar turns red, as displayed on the boiler’s panel, while on test value best-cases, validates the system, displaying green on the boiler’s panel as users entered the right value of parameters per the design specification. We discovered that from 1 to 10 minutes run time for auto system run gives 10.8% errors as compared from 1 to 10 minutes time interval on manual system run that gives 80.2% error, this results to less effort in user interface application usage on auto operations better than manual. The expert system prevents damaged and malfunctioning as control the alkalinity, prevent scaling, both mechanical & chemical corrosion, forming, correct pH values and then the conductivity which deals with the feed boiler water and monitored the level in the boiler drum using the industry measurement parameters (pressure, temperature, level, and flow).

A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images.

In this paper, we present rule-based fuzzy inference systems that consist of a series of mathematical representations based on fuzzy concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms. In particular, in most practical applications (i.e., biomedical image analysis), the emphasis is placed primarily on fuzzy filtering algorithms, with the main advantages of restoration of corrupted medical images and the interpretation capability, along with the capability of edge preservation and relevant image information for accurate diagnosis of diseases.

A Robust Backstepping Sliding Mode Controller with Chattering-Free Strategy for a Swarm of Drones

This paper presents a robust controller for a swarm of drones that are subjected to external disturbances. Integrating a fuzzy-based system in the sliding mode control technique, a chattering-free controller is proposed for the trajectory tracking of a drone. Furthermore, the formation controller for a group of drones is developed using the Backstepping control design technique. The integration of a fuzzy rule-based system in the sliding mode tracking controller enables overcoming the inherent chattering problem due to discontinuous switching function in the sliding mode control technique and provides smooth and chattering-free control signals. The asymptotic stability of the overall system is shown using the Lyapunov stability theory. Finally, the control design performance is evaluated using numerical simulations.

Healthcare service quality evaluation: An integrated decision-making methodology and a case study

Developments and changes in health services give importance to the concept of competition and encourage to increase quality among the healthcare centers. During these processes, the most required expectations from the system are achieving the desired quality and maintenance of the system environments. In this sense, measurement and continuous improvement of service quality are crucial for health centers to provide better service to patients. For this aim, this paper presents an integrated assessment methodology based on the distance-based Pythagorean Fuzzy Multiple Criteria Decision Making (MCDM) method, TOPSIS, and Fuzzy Inference System (FIS) for the measurement of health care service quality. To show the efficiency of the proposed method, a case study is carried out by comparing the ten clinics of a private hospital. Five dimensions of service quality (SERVQUAL) have been used as the main criteria for evaluating the service quality levels of the clinics, and multiple sub-criteria are determined for each dimension for a detailed analysis. Pythagorean Fuzzy TOPSIS is executed to the determined decision matrices to obtain inputs of the fuzzy rule-based system. Then, the clinic's service quality levels are calculated by implementing the fuzzy inference system. Moreover, sensitivity analyses based on the changes in the decision makers' weights are also applied to check the flexibility of the results. Finally, implementations with respect to theoretical, managerial, and policy aspects are discussed based on the obtained results.

Automatic detection and severity analysis of grape black measles disease based on deep learning and fuzzy logic

• A new disease evaluation method fusing deep learning and fuzzy logic is proposed. • DeepLabV3+ with ResNet50 backbone is utilized for images segmentation. • A fuzzy reference system is introduced for grape disease grades classification. • Two complementary indices are given to determine the disease grades. Grape black measles disease may be one of the best known, longest studied and most destructive of all plant diseases, which ultimately reduces productivity and quality of products. Timely, effective and accurate evaluation of grape black measles disease is acknowledged as a crucial step in field management. In this paper, an effective automatic detection and severity analysis method is proposed for grape black measles disease based on deep learning and fuzzy logic. In the first phase, the state-of-the-art ResNet50-based DeepLabV3+ semantic segmentation model is trained for pixel-level predictions in images of individual leaves exhibiting pathological lesions caused by fungus. In this way, the extracted features including ROI and POI are obtained. Second, for each feature, the fuzzy rule-based system is developed for predicting the harm degree of disease. Also, appropriate membership functions for the inputs and output are considered for fuzzification and defuzzification purposes in the fuzzy logic system. Finally, grape leaves are divided into grades of Healthy, Mild, Medium and Severe. Experimental results report an overall classification accuracy of 97.75% on the hold-out test dataset. It is concluded that the DeepLabV3+ model-based fuzzy reference system can be used effectively to classify grape leaves with different disease risks, based on combination of image analysis and statistical calculation.

FUZZY MODELING OF THE EFFECT OF IRRIGATION DEPTHS ON BEET CULTIVARS

The objective of this study was to develop a Fuzzy Rule-Based System (FRBS) for the mathematically modeling of the irrigation level effect on beet cultivars ( Beta vulgaris L.). From an agronomic experiment carried out in a protected environment (greenhouse), it was defined as input variables, for each cultivar, the irrigation levels (depths of 25, 40, 55, 70, 85, and 100% of ETc), in which ETc is the crop evapotranspiration [mm d−1], while the output variables were productivity, root diameter, and plant height. Was adopted the inference method of Mamdani and centroid method for fuzzification. The fuzzy models performed better than polynomial regression models, when compared with the coefficients of determination (R2), mean absolute error (MAE) and root mean square error (RMSE). In addition, the excellent performance of the model is highlighted due to the curve containing all the points of the treatments means. As agronomic results, concluded that the Early Wonder cultivar has higher yields when submitted to water deficit.

Enhanced Edge Detection Technique in Digital Images Using Optimised Fuzzy Operation

In image processing, edge detection is a critical issue. Edge detection is a key approach for evaluating the edge of various objects in a digital image. These edges are found using the gradients, which are present in the image. The intensity and value of pixels determine the gradients. In digital images, edge detection lowers the quantity of data and filters out irrelevant data while maintaining the image's key structural features. In this paper, a new edge detection approach based on a fuzzy rule-based system is proposed. In digital image processing, the proposed method typically depends on fuzzy logic systems. The main goal of this system is to show how fuzzy logic may be used in image processing. This paper provides a fuzzy logic-based edge detection technique that uses a sharpening Gabor filter to regulate edge quality and a Gaussian filter to reduce noise caused by sharpening. This is determined by utilizing applications such as “Peak Signal to Noise Ratio (PSNR) F-Measure, and Hausdorff distance (HoD) to prove that fuzzy logic outperforms the proposed system. The findings for edge detection approaches are included in high quality. The proposed approach outperforms most commonly used traditional edge detection methods. The proposed method also reduces the number of noisy features and may be used for a wide range of images.

Fuzzy Rule-Based Prediction of Gold Prices using News Affect

Because of gold’s value, systems for predicting its price have attracted extensive interest in the scientific and industrial communities. Diverse artificial intelligence methods outperform traditional statistical methods in predicting short- and long-term gold price. However, previous research has neglected the transparency of these systems, nor have these systems incorporated the potentially important effect of media sentiment on investment decisions. Therefore, we here propose a fuzzy rule-based prediction system with a component that processes various aspects of news stories. This system is trained on historical data to provide investors with one- and five-days-ahead gold price predictions while achieving a highly interpretable trading strategy in terms of rule complexity. We demonstrate that the proposed system is effective in terms of both prediction accuracy and interpretability compared with state-of-the-art models, such as extreme learning machines and neural networks with deep learning. Our findings suggest that the component of news affect is particularly important for one-day-ahead predictions. We also show that the proposed system performs well in terms of average annual return while providing an interpretable set of linguistic trading rules. This has important implications for investors.

On computing the similarity of trapezoidal fuzzy sets using an Automated Area Method

This paper provides a new Automated Area Method (AAM) for computing the Jaccard similarity of non-normal trapezoidal fuzzy sets (with triangles and rectangles as special cases), needed, e.g. in XAI for rule-based fuzzy systems, Computing With Words using the Perceptual Computer, Perceptual reasoning, etc. The most intensive part of the Jaccard similarity computation is computing the area under the minimum of two fuzzy sets. The AAM does this very efficiently by initially using figures for the 40 possible cases of the minimum of two non-normal trapezoidal type-1 FSs, and by then providing area formulas for each of them using simple geometry. Flowcharted algorithms are then provided for automatically determining which of the 40 cases one is in, which do not need any figures, and only need the coordinates of the vertices of the two non-normal trapezoidal type-1 FSs. Examples are provided that illustrate these new results, both for type-1 and interval type-2 FSs. The AAM can also be used to compute other similarity measures and to compute the subsethood between fuzzy sets.

Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations

This paper uses Monte Carlo simulations to estimate the parameters of rule-based fuzzy inference systems (FISs) designed for mineral prospectivity modeling. The targeted process for the case study is gold mineralization in the Rajapalot project area in northern Finland. Mamdani-type FISs are developed and implemented for the predictive modeling of favorable structural settings and favorable chemical traps causing gold enrichment in host rocks from ore-bearing hydrothermal fluids. The parameters of the fuzzification functions control the output fuzzy membership values. Traditionally these parameters are chosen subjectively based on the expert's domain knowledge. This study uses drill core data statistics to define the distribution of the parameters. Subsequently, Monte Carlo simulations are used to simulate the corresponding fuzzy membership values and optimize the FISs.•Capturing the complexities of the multi-processes geodynamic systems and the possible interplay mineralization-related geological aspects using ‘If-Then’ rule-based fuzzy inference systems.•Implementation of Monte Carlo simulations for quantification of uncertainties related to a Mamdani-type FIS-based prospectivity modeling.•Reporting prospectivity modeling results at different confidence levels for informed decision making on selection of exploration targets.

Achieve balanced stiffness and toughness properties of nanocomposites based on poly(lactic acid)/polyolefin by using fuzzy rule-based system

Achieve balanced stiffness and toughness properties of nanocomposites based on poly(lactic acid)/polyolefin by using fuzzy rule-based system

Attributes regrouping by genetic algorithm in fuzzy inference systems

Fuzzy Rule-Based Classification Systems (FRBCS) are a very popular and useful tool due to their interpretable classification models based on linguistic variables. However, a high number of attributes leads to an explosion of the number of classification rules. In this paper, we are interested in ensemble methods applied to FRBCS. An attributes regrouping method based on association rules and called SIFRA was proposed to solve this problem. It determines the relevant groups of related attributes that will be treated separately by FRBCSs. We propose a Genetic Algorithm-based method to select final groups of related attributes determined by SIFRA. In our approach, the genetic algorithm is used to filter the groups of related attributes and conserve the best ones, while maintaining a good classification rate and a reduced number of rules. We lead experiments to compare our contribution with previous methods in the same context; the experimental results are satisfactory.

Recovering Missing Data via Top-k Repeated Patterns for Fuzzy-Based Abnormal Node Detection in Sensor Networks

The stream data acquired by heterogeneous Internet of Things (IoT) sensors are seldom perfect. Most of the collected data streams include either missing or abnormal values caused by various factors such as failure, malfunction, or integrity attacks. Such unreliable data affect the real-time monitoring and compromise the quality of data analysis. By simply analyzing the sensor data via anomaly detection, applications may still be unreliable over the incomplete sensor data streams. Therefore, a reliable method for recovering the missing data and detecting the abnormal ones is indispensable in the IoT environment. This paper presents FuzHD++, a new method to recover missing sensor data and detect abnormal nodes jointly rather than independently. Both elements, data recovery and abnormal node detection, rely on the observed temporal and spatial correlation of sensor data to effectively achieve reliable recovery estimation and detection performance. In the data recovery process, the system adopts a matrix profile to extract the top-<i>k</i> repeated patterns from different sensor nodes. Furthermore, it utilizes the <i>k</i>-nearest neighbor estimator to recover the missing data based on the extracted pattern information of multiple neighbor nodes. During the abnormal node detection process, the system adopts a refined fuzzy rule-based detection method. The refined fuzzy rule-based inference system integrates the expert rules and the rules obtained from sensor data analysis to treat the ambiguity in the decision-making process. We validated the performance of FuzHD++ by comparing it with existing methods using two real-world datasets. Our results showed that the proposed missing sensor data recovery method achieves more than 20% improved root mean square error results than most existing methods. Furthermore, FuzHD++ achieved an average accuracy of 92% for analyzing the sensor readings and detecting the abnormal ones. According to the results, the proposed mechanisms based on the observed temporal and spatial correlation analysis improve the robustness of IoT against data loss and integrity attacks.

Combining Scientific Facts and Significance Criteria to Predict the Result of an Environmental Impact Assessment Review

The purpose of determining impact significance is to place value on impacts. Environmental impact assessment review is a process that judges whether impact significance is acceptable or not in accordance with the scientific facts regarding environmental, ecological and socio-economical impacts described in environmental impact statements or environmental impact assessment reports. The first aim of this paper is to summarize the criteria of significance evaluation from the past review results and accordingly utilize fuzzy rule-based system to incorporate these criteria into scientific facts. The second aim is to employ data mining technique to construct an environmental impact statement or environmental impact assessment report prediction model for reviewing results which can assist developers to prepare and revise better environmental management plans in advance. The validity of the previous prediction model proposed by authors in 2009 is 92.7%. The enhanced validity in this study can attain 100.0% after taking significance criteria into account. In the case study, some indicators are over-standard in first and second-order significance but all indicators ultimately fall into the category of conditional approval in third-order significance.

A Fuzzy Rule-Based System for Classification of Diabetes.

Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

Efficient inference models for classification problems with a high number of fuzzy rules

In data science there are problems that are not visible until you work with a sufficiently large number of data. This is the case, for example, with the design of the inference engine in fuzzy rule-based classification systems. The most common way to implement the winning rule inference method is to use sequential processing that reviews each of the rules in the rule set, to determine the best one and return the associated class. This implementation produces fast response times when the set of rules is small and is applied to a small set of examples. In this paper we explore new versions to implement this inference method, avoiding analyzing all the rules and focusing the analysis on the neighborhood of rules around the example. We study experimentally the conditions where each of them should be applied. Finally, we propose an implementation that combines all the studied versions offering good accuracy results and a significant reduction in the response time.

Design Of Fuzzy Rule-Based System Using Genetic Algorithms

The aim of this paper is to obtain a compact and optimal fuzzy rule-based model from observation data by utilizing the Genetic algorithm technique. The approach is optimized by applying Genetic Algorithms, owing to its capability of searching irregular and high dimensional solution spaces. Genetic Algorithms has been applied to learn consequent part of fuzzy rules and models with fixed number of rules. In the work we propose a Genetic algorithm approach to a non-linear air conditioning system for the construction of optimal fuzzy rules in two steps. First, fuzzy clustering is applied to obtain an initial rule based model having pre-calculated number of rules with antecedents only. In the second step, the regions of rule-consequents are obtained by a binary coded Genetic Algorithm which leads to the extraction of an optimal rule based model.