Fuzzy Classification Approach Research Articles

Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems

In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach.

Fuzzy classification with distance-based depth prototypes: High-dimensional unsupervised and/or supervised problems

Supervised and unsupervised classification is crucial in many areas where different types of data sets are common, such as biology, medicine, or industry, among others. A key consideration is that some units are more typical of the group they belong to than others. For this reason, fuzzy classification approaches are necessary. In this paper, a fuzzy supervised classification method, which is based on the construction of prototypes, is proposed. The method obtains the prototypes from an objective function that includes label information and a distance-based depth function. It works with any distance and it can deal with data sets of a wide nature variety. It can further be applied to data sets where the use of Euclidean distance is not suitable and to high-dimensional data (data sets in which the number of features p is larger than the number of observations n, often written as p>>n). In addition, the model can also cope with unsupervised classification, thus becoming an interesting alternative to other fuzzy clustering methods. With synthetic data sets along with high-dimensional real biomedical and industrial data sets, we demonstrate the good performance of the supervised and unsupervised fuzzy proposed procedures.

Discussion: A Fuzzy Classification Process for Swelling Soils [Transp. Infrastruct. Geotechnol. 10(3), 474–487]

This communication article presents a discussion of various fundamental points pertaining to the Toksöz Hozatlıoğlu and Yılmaz (2023) (the Authors) investigation [published in Transp. Infrastruct. Geotechnol. 10(3), 474–487], specifically scrutinizing the practical ramifications of employing the liquid limit (LL) parameter (by itself) to infer soil expansivity. Based on previous experimental investigations and their own independent analyses presented in this article, the Discussers demonstrate that without proper consideration of soil mineralogical activity, the sole reliance on the LL parameter (as adopted in the Authors’ investigation) is often not a reliable basis for performing soil expansivity assessments. Accordingly, the LL-based fuzzy classification approach reported by the Authors, despite addressing potential uncertainties arising from LL determinations by the Casagrande percussion-cup method, would, in itself, not lead to significantly more reliable soil expansivity assessments. As a way forward, a practical and more realistic classification framework for expansive soils based on equilibrium sediment volume measurements is presented and compared to the LL-based scheme adopted in the Authors’ investigation. Through this endeavor, it is demonstrated that the sole reliance on the LL parameter generally produces overly conservative soil expansivity rankings.

Application of interval type-2 fuzzy logic and type-1 fuzzy logic-based approaches to social networks for spam detection with combined feature capabilities.

Social networks are large platforms that allow their users to interact with each other on the Internet. Today, the widespread use of social networks has made them vulnerable to malicious use through different methods such as fake accounts and spam. As a result, many social network users are exposed to the harmful effects of spam accounts created by malicious people. Although Twitter, one of the most popular social networking platforms, uses spam filters to protect its users from the harmful effects of spam, these filters are insufficient to detect spam accounts that exhibit new methods and behaviours. That's why on social networking platforms like Twitter, it has become a necessity to use robust and more dynamic methods to detect spam accounts. Fuzzy logic (FL) based approaches, as they are the models such that generate results by interpreting the data obtained based on heuristics viewpoint according to past experiences, they can provide robust and dynamic solutions in spam detection, as in many application areas. For this purpose, a data set was created by collecting data on the twitter platform for spam detection. In the study, fuzzy logic-based classification approaches are suggested for spam detection. In the first stage of the proposed method, a data set with extracted attributes was obtained by applying normalization and crowdsourcing approaches to the raw data obtained from Twitter. In the next stage, as a process of the data preprocessing step, six attributes in the binary form in the data set were subjected to a rating-based transformation and combined with the other real-valued attribute to create a database to be used in spam detection. Classification process inputs were obtained by applying the fisher-score method, one of the commonly used filter-based methods, to the data set obtained in the second stage. In the last stage, the data were classified based on FL based approaches according to the obtained inputs. As FL approaches, four different Mamdani and Sugeno fuzzy inference systems based on interval type-1 and Interval Type-2 were used. Finally, in the classification phase, four different machine learning (ML) approaches including support vector machine (SVM), Bayesian point machine (BPM), logistic regression (LR) and average perceptron (Avr Prc) methods were used to test the effectiveness of these approaches in detecting spam. Experimental results were obtained by applying different FL and ML based approaches on the data set created in the study. As a result of the experiments, the Interval Type-2 Mamdani fuzzy inference system (IT2M-FIS) provided the highest performance with an accuracy of 0.955, a recall of 0.967, an F-score 0.962 and an area under the curve (AUC) of 0.971. However, it has been observed that FL-based spam models have a higher performance than ML-based spam models in terms of metrics including accuracy, recall, F-score and AUC values.

Transfer learning of Impact Echo signal classification from laboratory to the field

Impact Echo (IE) is a popular nondestructive testing tool for condition assessment of concrete bridge decks. Traditionally, IE signals require to be manually interpreted and classified into discrete classes – good, fair and poor - representing the different conditions at test points. This study aims to develop an automated fuzzy classification approach for IE signals trained on a set of eight reinforced concrete specimens constructed at the FHWA Advanced Sensing Technology (FAST) NDE laboratory with artificial defects that are known apriori. We choose fuzzy classification to obtain condition “goodness” represented on a continuum rather than on discrete classes (good, fair and poor). We use fuzzy classification models informed by physics-based features and automatically extracted features to determine the departure of a bridge deck condition from ‘good’. Next, using transfer learning, we suitably tune the classification model for optimum performance on data from FHWA’s InfoBridge database that has a limited amount of IE data from real bridge decks. We evaluate the transferred model performance against expert-assisted manual signal classification. This study will demonstrate the utility of laboratory studies for developing models applicable to real field data, where little or no ground truth data are available.

Selection of Compatible Coal Seam for Methane Drainage Operation Based on Uncertain Geological Conditions: A Hybrid Fuzzy Approach

Geological studies are very important at different steps of mining activities. Different uncertain geological criteria and factors show a significant impact during the underground mining operations and mineral extraction process. The current study reports on the evaluation and classification of coal seams for methane drainage-ability (MDA) through a fuzzy hybrid approach. This problem was investigated due to the importance of uncertain geological factors in the process of MDA and the necessity to evaluate safety operations in underground coal mines. The important criteria involved are depth, thickness, and uniformity of coal seam, joints and cleats conditions, roof quality, coal seam gas content, underground water condition, and permeability of coal seam. The two-stage fuzzy classification approach was used to analyze the effectiveness of the uncertain geological criteria. Also, fuzzy cognitive map (FCM) method was used to calculate weights for geological criteria. The used FCM method is based on the Hebbian algorithm and metaheuristic methods. The Hebbian learning algorithm is made from a hybrid learning algorithm of nonlinear heuristics and differential evolution. In addition, fuzzy intervals of criteria were calculated based on technical reports and other scientific studies. Then, the rank of each coal seam calculates by fuzzy T-norms. The proposed system was employed to classify the coal seams for MDA in Parvadeh coalfield, Iran. The results showed that the C1, C2, B2, B1, and D coal seams were classified in the “very good,” “good,” “moderate,” “poor,” and “very poor” categories, respectively. The proposed fuzzy hybrid approach provides a new logical tool in the selection of coal seam for methane drainage operation and can reduce the risk of methane drainage projects in underground coal mines.

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.

A new approach for fuzzy classification by a multiple-attribute decision-making model

A new approach for fuzzy classification by a multiple-attribute decision-making model

Retrieval of Chlorophyll-a concentration and associated product uncertainty in optically diverse lakes and reservoirs

Satellite product uncertainty estimates are critical for the further development and evaluation of remote sensing algorithms, as well as for the user community (e.g., modelers, climate scientists, and decision-makers). Optical remote sensing of water quality is affected by significant uncertainties stemming from correction for atmospheric effects as well as a lack of algorithms that can be universally applied to waterbodies spanning several orders of magnitude in non-covarying substance concentrations. We developed a method to produce estimates of Chlorophyll-a (Chla) satellite product uncertainty on a pixel-by-pixel basis within an Optical Water Type (OWT) classification scheme. This scheme helps to dynamically select the most appropriate algorithms for each satellite pixel, whereas the associated uncertainty informs downstream use of the data (e.g., for trend detection or modeling) as well as the future direction of algorithm research. Observations of Chla were related to 13 previously established OWT classes based on their corresponding water-leaving reflectance (Rw), each class corresponding to specific bio-optical characteristics. Uncertainty models corresponding to specific algorithm - OWT combinations for Chla were then expressed as a function of OWT class membership score. Embedding these uncertainty models into a fuzzy OWT classification approach for satellite imagery allows Chla and associated product uncertainty to be estimated without a priori knowledge of the biogeochemical characteristics of a water body. Following blending of Chla algorithm results according to per-pixel fuzzy OWT membership, Chla retrieval shows a generally robust response over a wide range of class memberships, indicating a wide application range (ranging from 0.01 to 362.5 mg/m3). Low OWT membership scores and high product uncertainty identify conditions where optical water types need further exploration, and where biogeochemical satellite retrieval algorithms require further improvement. The procedure is demonstrated here for the Medium Resolution Imaging Spectrometer (MERIS) but could be repeated for other sensors, atmospheric correction methods and optical water quality variables.

Detection of lung tumor using dual tree complex wavelet transform and co‐active adaptive neuro fuzzy inference system classification approach

AbstractThe automatic detection and location of the tumor regions in lung images is more important to provide timely medical treatments to patients in order to save their lives. In this article, machine learning‐based lung tumor detection, classification and segmentation algorithm is proposed. The tumor classification phase first smooth the source lung computed tomography image using adaptive median filter and then discrete time complex wavelet transform (DT‐CWT) is applied on this smoothed lung image to decompose the entire image into a number of sub‐bands. Along with the decomposed sub‐bands, DWT, pattern, and co‐occurrence features are computed and classified using co‐active adaptive neuro fuzzy inference system (CANFIS). The tumor segmentation phase uses morphological functions on this classified abnormal lung image to locate the tumor regions. The multi‐evaluation parameters are used to evaluate the proposed method. This method is compared with the other state‐of‐the‐art methods on the same lung image from open‐access dataset.

Harnessing fuzzy neural network for gear fault diagnosis with limited data labels

Diagnosis and prognosis of gear systems play an important role in modern manufacturing. While first-principle-based inverse analysis is subject to various limitations, data-driven approaches such as many machine learning techniques have shown great promise in recent years. Nevertheless, major challenges remain. Machine learning generally requires large amount of high-quality training data which may not be available for many industrial systems. In particular, while gear faults are continuous in nature and exhibit many different scenarios, in practical situations owing to the high cost in data acquisition especially for fault scenarios, only a small number of discrete classes of faults, i.e., fault types and severities, can be recorded and employed in training. As such, the neural networks trained will need to deal with unseen faults when they are actually implemented. To tackle this challenge, in this research, we develop a fuzzy classification approach capable of handling fault scenarios that are not included in the training dataset. Through the integration of a fuzzification procedure, this fuzzy neural network (FNN) can produce classification outcome with probability and confidence level. An unseen fault scenario will be classified into the nearest fault class with probability, effectively yielding the diagnosis result under limited data. While fault features in gear vibration signals are hidden and have complex nonlinear relations with respect to fault scenarios, it is found that the kernel principal component analysis (KPCA) can enable the FNN to facilitate the correlation of fault features. Systematic case studies using experimental data acquired from a lab-scale gear system are carried out to validate the new approach.

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 novel Covid-19 and pneumonia classification method based on F-transform

Nowadays, Covid-19 is the most important disease that affects daily life globally. Therefore, many methods are offered to fight against Covid-19. In this paper, a novel fuzzy tree classification approach was introduced for Covid-19 detection. Since Covid-19 disease is similar to pneumonia, three classes of data sets such as Covid-19, pneumonia, and normal chest x-ray images were employed in this study. A novel machine learning model, which is called the exemplar model, is presented by using this dataset. Firstly, fuzzy tree transformation is applied to each used chest image, and 15 images (3-level F-tree is constructed in this work) are obtained from a chest image. Then exemplar division is applied to these images. A multi-kernel local binary pattern (MKLBP) is applied to each exemplar and image to generate features. Most valuable features are selected using the iterative neighborhood component (INCA) feature selector. INCA selects the most distinctive 616 features, and these features are forwarded to 16 conventional classifiers in five groups. These groups are decision tree (DT), linear discriminant (LD), support vector machine (SVM), ensemble, and k-nearest neighbor (k-NN). The best-resulted classifier is Cubic SVM, and it achieved 97.01% classification accuracy for this dataset.

UAV‐based training for fully fuzzy classification of Sentinel‐2 fluvial scenes

AbstractAn estimated 76% of global stream area is occupied by channels with widths above 30 m. Sentinel‐2 imagery with resolutions of 10 m could supply information about the composition of river corridors at national and global scales. Fuzzy classification models that infer sub‐pixel composition could further be used to compensate for small channel widths imaged at 10 m of spatial resolution. A major challenge to this approach is the acquisition of suitable training data useable in machine learning models that can predict land‐cover type information from image radiance values. In this contribution, we present a method which combines unmanned aerial vehicles (UAVs) and Sentinel‐2 imagery in order to develop a fuzzy classification approach capable of large‐scale investigations. Our approach uses hyperspatial UAV imagery in order to derive high‐resolution class information that can be used to train fuzzy classification models for Sentinel‐2 data where all bands are super‐resolved to a spatial resolution of 10 m. We use a multi‐temporal UAV dataset covering an area of 5.25 km2. Using a novel convolutional neural network (CNN) classifier, we predict sub‐pixel membership for Sentinel‐2 pixels in the fluvial corridor as divided into classes of water, vegetation and dry sediment. Our CNN model can predict fuzzy class memberships with median errors from −5% to +3% and mean absolute errors from 10% to 20%. We also show that our CNN fuzzy predictor can be used to predict crisp classes with accuracies from 95.5% to 99.9%. Finally, we use an example to show how a fuzzy CNN model trained with localized UAV data can be applied to longer channel reaches and detect new vegetation growth. We therefore argue that the novel use of UAVs as field validation tools for freely available satellite data can bridge the scale gap between local and regional fluvial studies. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

Foreign Body Shape Classification Using Intuitionistic Fuzzy Rule Based Approach on Pediatric Radiography Images

Segmentation is the significant key stage in image analysis towards partitioning an image into different regions which have homogeneous features such as color, shape, and texture which is very important in classifying different region shapes in an image. In general, images are considered fuzzy due to the uncertainty present in terms of vagueness. The regions contain imprecise gray levels and uncertain data values which makes the task of defining the membership function difficult due to lack of precise knowledge. The intuitionistic fuzzy rule-based shape classification approach is used to classify the different shapes, such as circular, polygon, sharp, and irregular of the aspired foreign body on pediatric radiography images. Experimental results show the effectiveness of the proposed method in contrast to conventional fuzzy rule base algorithm.

Integrated healthcare monitoring solutions for soldier using the internet of things with distributed computing

This paper has proposed an integrated healthcare monitoring solution for the soldiers deployed in adverse environmental conditions, using the internet of things (IoT) with distributed computing. For these soldiers, the health parameters of every individual need to be monitored on a real-time basis and subsequent analysis of the dataset to be made for initiating appropriate medical support with the lowest possible delay. In this paper, a three-layer service-oriented IoT architecture has been proposed where the computational functionalities are distributed among all the layers. The proposed distributed computing mechanism has implemented two levels of filtration of redundant information that belongs to safe soldiers. The first level of filtering is done at the end-node using the Fuzzy classification approach and the second level of filtering is done at the intermediate node using the time-series pattern analysis approach. This layer-wise filtration process results in a reduction in data flooding and computational burden on the cloud due to which system response time improves to suit emergency applications. A prototype has been developed to validate the effectiveness of the proposed solution.

Design of fuzzy inference system for apple ripeness estimation using gradient method

In this study, a fuzzy classification approach based on colour features has been investigated to estimate the ripeness of apple fruits according to three maturity stages; unripe, turning-ripe and ripe. The K nearest neighbour algorithm was applied in order to segment the fruit image into four regions namely background, green area, yellow area and red area. The last three regions represent the colour features and were subsequently given as inputs to the fuzzy classifier. Gradient method has been used for tuning the fuzzy classifier in order to obtain the best performance. Image database used for simulation has been collected and exploited for the training and testing phases using cross-validation. Simulation results indicate that the best classifier parameters can be obtained. The efficiency of the proposed system compared with the non-use of the gradient method has been proved by the confusion matrix and the most known classification evaluation metrics. Moreover, the trained fuzzy classifier demonstrates its outperformance in terms of accuracy and execution time compared with other existing methods.

Advanced Fuzzy-Logic-Based Context-Driven Control for HVAC Management Systems in Buildings

Control in HVAC (heating, ventilation and air-conditioning) systems of buildings is not trivial, and its design is considered challenging due to the complexity in the analysis of the dynamics of its nonlinear characteristics for the identification of its mathematical model. HVAC systems are complex since they consist of several elements, such as heat pumps, chillers, valves, heating/cooling coils, boilers, air-handling units, fans, liquid/air distribution systems, and thermal storage systems. This article proposes the application of LAMDA (learning algorithm for multivariable data analysis) for advanced control in HVAC systems for buildings. LAMDA addresses the control problem using a fuzzy classification approach without requiring a mathematical model of the plant/system. The method determines the degree of adequacy of a system for every class and subsequently determines its similarity degree, and it is used to identify the functional state or class of the system. Then, based on a novel inference method that has been added to LAMDA, a control action is computed that brings the system to a zero-error state. The LAMDA controller performance is analyzed via evaluation on a regulation problem of an HVAC system of a building, and it is compared with other similar approaches. According to the results, our method performs impressively in these systems, thereby leading to a trustable model for the implementation of improved building management systems. The LAMDA control performs very well for disturbances by proposing control actions that are not abrupt, and it outperforms the compared approaches.

Identification of Paddy Stubble Burnt Activities Using Temporal Class-Based Sensor-Independent Indices Database: Modified Possibilistic Fuzzy Classification Approach

In this study, temporal multispectral Sentinel-2A/B data have been used for identification of burnt paddy field using temporal Class-Based Sensor-Independent Indices Database using modified possibilitic fuzzy classification approach. The practice of stubble burning was found in Patiala site of Punjab State, and it was analysed and detected in temporal dataset of Sentinel 2A/B from 30 Sep 2018 to 19 Nov 2018. The result showed that stubble burning was started in a more frequent way from 20 Oct 2018 to 19 Nov 2018. The maximum stubble burnt was identified between 30 Oct 2018 and 19 Nov 2018. Stubble burnt paddy field area can be used in further estimation of pollution inlet to the atmosphere.

A weighted fuzzy classification approach to identify and manipulate coincidental correct test cases for fault localization

Identifying the location of faults effectively and accurately is highly important in the debugging process of software engineering. Coverage-based Fault Localization (CBFL) has been widely studied that can alleviate the effort of developers to find the faults position using the execution information of test cases. Coincidental Correct (CC) test cases are the specific test cases that execute the faulty statements but with a correct output, which have been illustrated with a negative effect on the accuracy of CBFL. In this paper, we propose a weighted fuzzy classification approach to identify CC test cases and three fuzzy strategies are suggested to manipulate CC test cases for CBFL. Firstly, we present a simple but efficient approach to identify some CC test cases for single fault programs, which provide labeled samples that enable the application of supervised classification algorithms for CC identification. Then, a Fuzzy Weighted K-Nearest Neighbor (FW-KNN) algorithm is proposed to classify potential CC from the passed test cases, in which a ‘weighted’ similarity measure and a “weighted” CC probability computation are presented. Finally, three fuzzy CC test cases manipulation strategies are presented to mitigate the impact of CC test cases in CBFL. Various empirical studies are conducted on 190 faulty versions of 12 programs to investigate the impact of “weighted” and “fuzzy” methods for CC identification by the comparison of the effectiveness and efficiency between FW-KNN and three popular cluster and classification techniques. The results indicate that the proposed FW-KNN has higher accuracy and lower time cost. The Precision, Recall and False Positive Rate of FW-KNN is 96.47%, 83.40% and 2.85%, respectively. Besides, by utilizing code block coverage, the time cost can be reduced by 72.97% in average compared to statement coverage. The experimental results also indicate that the fault localization accuracy of CBFL can be improved by the proposed CC test cases manipulation strategies.