Fuzzy Classification Model Research Articles

Simple fuzzy classification of metal qualities of Ona River, Ibadan, Nigeria, and its implication for fish production and other uses

Elevated concentrations of metals in rivers can pose significant risks to human health. Consequently, it is essential to monitor metal levels in river water to minimize human exposure, particularly through consumption of harvested fish and other uses. This study examined the water quality of the Ona River in Ibadan, Nigeria. Seven potentially toxic elements (PTEs), namely Fe, Cu, Mn, Zn, Pb, Cd, Cr and four macronutrients: Mg, Na, K, Ca were investigated. Surface and pore water samples were collected from six sampling points during the peak of the dry and the rainy seasons. These were analyzed according to standard procedures and processed using the Simple Fuzzy Classification (SFC) model with four classes. The results revealed that the PTEs: Fe, Pb, Cd, Cr, and Mn were detected above guideline limits in several sampling points, indicating possible contamination from anthropogenic sources. Simple fuzzy classification puts the quality of water in the investigated portion of the river in the range of 67.4–88.5% in the C1 (pristine) category during the dry season and 61.9–88.6% in C1 (pristine) during rainy season. However, there were membership functions of up to 58.7 and 63.2; 61.2 and 75.4 as well as 44.3 and 43.5% in C2, C3, and C4 categories (contaminated, polluted and extremely polluted, respectively) during the first and second sampling, respectively. Due to the high concentrations of toxic and bioaccumulative metals in the river, efforts should be made to control the discharge of pollutants into the river. These will prevent human health exposure risks tendencies for various uses, including fish production.

A novel integrated soft sensing model for online cement clinker quality monitoring based on fuzzy fine-grained classification

The content of free calcium oxide (f-CaO) in cement clinker is an important index for cement quality. For the problems of multiple working conditions and unbalanced distribution of sample labels in cement clinker production, a soft sensing method of cement clinker f-CaO content based on fuzzy fine-grained classification (FF) is proposed. First, a divide-and-conquer strategy is used to divide the samples into high, medium, and low subsets according to cement clinker f-CaO and extract the fine-grained features under diverse types of multiple production conditions. Second, fuzzy classification based on the membership function is used in the FF model to solve the uncertainty of the sample categories. To ensure the rationality of the classification, the fuzzy membership rule is combined with a convolutional neural network to implement the fuzzy classification method. Finally, different feature extraction methods are proposed to be selected according to the data size of various categories of samples. After experimental validation, the evaluation metrics of RMSE decreased by 4.5 % and R2increased by 17.8 % for the direct classification model compared to the single model. The RMSE of the fuzzy classification model over the direct classification model was again reduced by 2.34 % and R2was again improved by 7.24 %, showing the effectiveness of the proposed soft measurement model.

An Approach to a Priori Assessment of Fuzzy Classification Models in Monitoring Tasks

The article addresses the problems of using automation tools to perform monitoring and management tasks applicable to assessing the quality of fuzzy classification models, where the classification procedure is implemented on the basis of knowledge (rules) in the absence of the training set. An approach is proposed to obtain a priori assessments of the classification quality based on the study of the used model sensitivity to changes in the values of internal parameters during the corresponding modeling. The interpretation of the modeling results in the form of risk assessment caused by the self-imperfection of the classification models is obtained. The article provides an example of a fuzzy classification model based on a comparison of the current state of a monitoring object described using fuzzy features with a set of predefined typical states, which form corresponding fuzzy equal (close) states (monitoring situations). The comparison is carried out using the fuzzy implication operation provided that the required reliability is met. The example of this model demonstrates how the type of implication operation, as well as the internal features of the model, affect the results of classification, and appropriate indicators are proposed, which are both an interpretation of generally accepted indicators for assessing the classification quality, and unique, inherent in the considered model. Computational experiments were carried out, which made it possible to obtain graphs of changes in classification quality assessment indicators for the considered model and its modification and visualize the influence of internal parameters of the model on the results of its application. A number of indicators are proposed that allow for an a priori assessment of the risks arising from the application of the model before its actual application.

Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

Classification of epileptic EEG signals with the utilization of Bonferroni mean based fuzzy pattern tree

The Electroencephalogram (EEG) is an essential tool used to detect and investigate multiple neurological disorders within the human brain. However, examination and visualization of any such abnormalities in the brain through inspecting these EEG signals is a time-consuming and repetitive task for a neurologist. Hence, there is a need to develop a model that can automatically classify brain disorders based on the EEG signals. In this study, a novel fuzzy classification model is proposed for classifying the EEG signals. First, the discrete wavelet transform (DWT) technique is employed to decompose the signal into time–frequency sub-bands. Then, by utilizing these sub-bands, seven meaningful statistical features are extracted effectively for additional investigation purposes. Then, the retrieved features are provided to the fuzzy pattern tree classifier to obtain meaningful inferences from the data. Here, a distinctive structure of a fuzzy pattern tree is proposed by utilizing the interrelationship handling aggregation operators. More precisely, we utilize the Bonferroni mean (BM) aggregation operator for the enlargement or expansion of the tree while considering the concatenation between the parent and the slave tree. To the best of our knowledge, this is the first work that presents such an interrelationship handling fuzzy logic-based classifier model for EEG signal classification. The proposed algorithm is evaluated using two publicly available datasets, namely the Bonn University (Bonn) epileptic datasets and the Temple University Hospital (TUH) EEG subcorpus of abnormal (TUAB) and epilepsy (TUEP) datasets. This study considers different frames of the binary problem (i.e., healthy vs. seizure detection) and multi-class problem (i.e., healthy vs. seizure free vs. seizure) over the Bonn dataset and achieves more than 99% accuracy over the binary model by testing over all possible combinations of the available experimental dataset and 97.8% accuracy in multi-class model. In the case of TUAB and TUEP categories, the model achieves an accuracy of 88% in predicting normal to abnormal activities and 86% in detecting epilepsy and non-epilepsy within the signals. The outcomes derived from the proposed model outperform some existing studies available in the literature for EEG signal classification. Also, the model is efficient as it considers the uncertainty and association among the features while framing the classifier model.

Multi-View Fuzzy Classification With Subspace Clustering and Information Granules

Multi-view learning becomes increasingly attractive and promising because multimodal or multi-view data are commonly encountered in real-world applications. In this study, we develop a novel multi-view Takagi-Sugeno-Kang (TSK) fuzzy system framework to handle classification problems for such data. We propose an anchor and graph subspace clustering strategy to discover and represent the actual latent data distribution for each view separately. In this way, the discriminate anchors (landmarks) are learned to capture the main structure of the multi-view data. This strategy also provides a computationally efficient clustering algorithm with respect to the number of instances. These resulting anchors are formed as the prototypes of information granules (IGs) for fuzzy modeling. Then we construct an information-granule-based multi-view TSK fuzzy classification model inherited from the natural interpretability of fuzzy rule-based systems. Concretely, the relationship between the multi-view input and label output spaces is depicted by IGs-oriented fuzzy rules. The experimental studies involve various commonly used benchmark datasets, which indicate that our proposed method achieves comparable or better performance compared to the state-of-the-art algorithms.

Development of tuned hybrid fuzzy and BiLSTM-based epileptic seizure classification model with stacked 1D-CNN-fisher discriminant feature selection

ABSTRACT Electroencephalogram (EEG) is a signal which consists of different sinusoidal components with a dense frequency spectrum. The existing methods are time-consumingand includes inconsistencies in judgment among seizure classification. Therefore, it is very difficult to accurately detect epileptic seizures from the recorded EEG. Therefore, a new epileptic seizure detection model is developed with hybrid deep-structured technology. The EEG signal data obtained from the standard online resources which are given into the pre-processing phase with the help of band pass filtering and smoothing techniques. Then, the 5-level Discrete Wavelet Transform (5-level DWT) is used for signal decomposition to get the decomposed signals. 1-D stacked Convolutional Neural Network (1-D stacked CNN) is utilized for the extraction of features. After, the feature selection process is executed by utilizing the Fisher Discriminant Analysis (FDA). The selected features are subjected to the classification phase by Tuned Hybrid Fuzzy Bi-directional Long Short-Term Memory (THFBi-LSTM). Here, the parameter optimization takes place with hybridized optimization algorithm of Probability-based Dingo Coyote Optimization (P-DCO). In the overall simulation estimation, the offered approach achieves a 97% accuracy rate and also a 92% F1-score rate. Thus, the experimental results are reveled that it is better than the other baseline approaches.

Prediction of crop disease using Rank Regressive Learning and Proaftn Fuzzy Classification models

Prediction of crop disease using Rank Regressive Learning and Proaftn Fuzzy Classification models

Enhanced tolerance-based intuitionistic fuzzy rough set theory feature selection and ResNet-18 feature extraction model for arrhythmia classification

Arrhythmia classification on Electrocardiogram (ECG) signals is an important process for the diagnosis of cardiac disease and arrhythmia disease. The existing researches in arrhythmia classification have limitations of imbalance data problem and overfitting in classification. This research applies Fuzzy C-Means (FCM) – Enhanced Tolerance-based Intuitionistic Fuzzy Rough Set Theory (ETIFRST) for feature selection in arrhythmia classification. The selected features from FCM-ETIFRST were applied to the Multi-class Support Vector Machine (MSVM) for arrhythmia classification. The ResNet18 – Convolution Neural Network (CNN) was applied for feature extraction in input signal to overcome imbalance data problem. Conventional feature extraction along with CNN features are applied for FCM-ETIFRST feature selection process. The FCM-ETIFRST method in arrhythmia classification is evaluated on MIT-BIH and CPCS 2018 dataset. The FCM-ETIFRST has 98.95% accuracy and Focal loss-CNN has 98.66% accuracy on MIT-BIH dataset. The FCM-ETIFRST method has 98.45% accuracy and Explainable Deep learning Model (XDM) method have 93.6% accuracy on CPCS 2018 dataset.

Developing a biotechnical scheme using fuzzy logic model for classification of severity of pyelonephritis

Developing a biotechnical scheme using fuzzy logic model for classification of severity of pyelonephritis

An optimized fuzzy deep learning model for data classification based on NSGA-II

As a powerful paradigm, deep learning (DL) models have been used in many applications for classification tasks in images, text, and audio. Through DL models, we can learn task-driven features from big data. However, DL models are fully deterministic and cannot handle uncertain and imprecise data. DL models are often sensitive to noise in data and do not operate well in areas where data are vague. Moreover, when there is a large feature set or high-dimensional data with irrelevant and redundant features, the DL models’ performance decreases in classification due to training with irrelevant features. To gain reliable results in such high-dimensional problems, DL models require a large amount of data which usually grows exponentially concerning the number of features. Data uncertainty problems and irrelevant features in DL models cause low performance in classification tasks. This paper proposes an optimized fuzzy deep learning (OFDL) model for data classification based on Non-Dominated Sorting Genetic Algorithm II (NSGA-II). OFDL utilizes optimization in the composition of DL and fuzzy learning via the NSGA-II in multi-modal learning. To achieve effective classification, OFDL first considers intelligent feature selection by finding the best trade-offs between two conflicting objective functions, minimizing the number of features, and maximizing the accuracy (maximizing weights of selected features). Next, to reach optimized backpropagation and fuzzy membership functions, OFDL utilizes Pareto optimal solutions for multi-objective optimization using NSGA-II based on their objective functions. Furthermore, the fusion layer in OFDL fused optimized views of DL and fuzzy learning that provides a high-level representation of inputs and optimum features for classification tasks where the data contain high uncertainties and noises. This functionality gives valuable attributes during classification since identifying and selecting appropriate features ensures prompt and correct class. Also, it provides deep insight into tackling the effect of ambiguous data and each feature’s uncertainty in the classification tasks. The examination of OFDL reveals good performance in terms of F-measure, accuracy, recall, precision, and True Positive Rate (TPR) compared to fuzzy classifiers. Furthermore, OFDL has higher accuracy in classification tasks than earlier fuzzy DNN models.

Fuzzy Deep Forest With Deep Contours Feature for Leaf Cultivar Classification

Deep learning is a compelling technique for feature extraction due to its adaptive capacity of processing and providing deeper image information. However, for the task of leaf cultivar classification, the deep learning-based classifier model is unable to extract contour features of leaf images deeply due to the lack of large specialized datasets and expert knowledge annotations. Also, the scale/size of the current leaf cultivar dataset does not meet the needs of deep neural networks (DNNs). In particular, the high model complexity of DNNs implies that deep-learning-based neural networks seem to must require a large dataset to achieve good performance, but facing the fact that the leaf cultivar dataset often is small, even some classes in this kind of datasets contain less than ten images/examples. To overcome these problems and inspired by the resounding success of fuzzy logic, we propose a novel fuzzy ensemble model for leaf cultivar classification. To extract the contours of leaves, we first propose generative adversarial networks-based methods. Second, to improve the ability of feature representation, we present a data augmentation method to transform our contour features. Third, to get the essential features of leaves, we design a novel generation of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy random forest</i> . Finally, to achieve accurate classification, we design a novel deep learning strategy, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deep fuzzy representation learning</i> , integrating and cascading a lot of our fuzzy random forests. Experimental results show that our model outperforms other existing state-of-the-arts on three real-world datasets, and performs much better than the original deep forest and DNN-based algorithms particularly.

Comparison of the probabilistic model of a fuzzy Bayesian classifier and the Gaussian mixture problem

The article shows that the problem of fuzzy classification and the problem of separation of Gaussian mixtures have different theoretical solution models based on the results of the classical probability theory. A probabilistic model of the clustering and classification process based on the Bayesian approach is the joint distribution of multidimensional data points across clusters and cluster prototypes. The model is built for datasets with a normal distribution. Unknown membership values for each data point are assumed to be a random variable with some empirical distribution. The cluster prototype is also assumed to be a normally distributed random variable. The model is compared with the Gaussian mixture separation model and the main difference between them is highlighted: in the Bayesian fuzzy classification model, the product of all component probabilities is used, which means taking into account the possibility of the xn value falling into different clusters. The computational algorithm of this model may have a wider application compared to the traditional fuzzy c-means approach, due to the lack of restrictions on clustering parameters. Some numerical results are presented that confirm the model’s operability for an artificial data set both with parameters within the traditional limitations of the c-means method and for an extended range of parameter values.

Decision-Refillable-Based Two-Material-View Fuzzy Classification for Personal Thermal Comfort

The personal thermal comfort model is used to design and control the thermal environment and predict the thermal comfort responses of individuals rather than reflect the average response of the population. Previous individual thermal comfort models were mainly focused on a single material environment. However, the channels for individual thermal comfort were various in real life. Therefore, a new personal thermal comfort evaluation method is constructed by means of a reliable decision-based fuzzy classification model from two views. In this study, a two-view thermal comfort fuzzy classification model was constructed using the interpretable zero-order Takagi–Sugeno–Kang (TSK) fuzzy classifier as the basic training subblock, and it is the first time an optimized machine learning algorithm to study the interpretable thermal comfort model is used. The relevant information (including basic information, sampling conditions, physiological parameters, physical environment, environmental perception, and self-assessment parameters) was obtained from 157 subjects in experimental chambers with two different materials. This proposed method has the following features: (1) The training samples in the input layer contain the feature data under experimental conditions with two different materials. The training models constructed from the training samples under these two conditions complement and restrict each other and improve the accuracy of the whole model training. (2) In the rule layer of the training unit, interpretable fuzzy rules are designed to solve the existing layers with the design of short rules. The output of the intermediate layer of the fuzzy classifier and the fuzzy rules are difficult to explain, which is problematic. (3) Better decision-making knowledge information is obtained in both the rule layer of the single-view training model and in the two-view fusion model. In addition, the feature mapping space is generated according to the degree of contribution of the decision-making information from the two single training views, which not only preserves the feature information of the source training samples to a large extent but also improves the training accuracy of the model and enhances the generalization performance of the training model. Experimental results indicated that TMV-TSK-FC has better classification performance and generalization performance than several related state-of-the-art non-fuzzy classifiers applied in this study. Significantly, compared with the single view fuzzy classifier, the training accuracies and testing accuracies of TMV-TSK-FC are improved by 3–11% and 2–9%, respectively. In addition, the experimental results also showed good semantic interpretability of TMV-TSK-FC.

Fuzzy Rule-Based Classification Method for Incremental Rule Learning

Granularrules have been extensively used for classification in fuzzy datasets to promote the advancement of artificial intelligence. However, due to the diversity of data types, how to improve the readability of the extracted granular rules while ensuring efficiency is always a challenge. Since granular reduct in granular computing (GrC) can simplify real complex problem and dataset, this article carries out granular rule learning from the perspective of granular reduct by taking formal concept analysis (FCA)-based GrC method as a framework. Specifically, for achieving classification task, we first propose a method to update the granular reduct, and then explore the updating mechanism of fuzzy granular rule in a reduced dataset. Second, a novel fuzzy rule-based classification model named FRCM is presented for fuzzy granular rule learning. In order to verify the effectiveness of the proposed model, some numerical experiments for incremental learning and fuzzy rule mining are conducted to demonstrate that FRCM can achieve the state-of-the-art classification performance.

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.

Integrating human cognition in cyber-physical systems: A multidimensional fuzzy pattern model with application to thermal spraying

The development of next-generation, intelligent manufacturing relies on the realization of human-cyber-physical systems. This key area of transdisciplinary research seeks to interrogate ways of supporting and integrating human cognition and expertise in complex cyber-physical systems, using modelling methods from artificial intelligence. This paper uses the exemplar of wear classification in thermal spraying to outline how relevant cognitive processes can be elicited and combined with technical variables in a singular, efficient and cognitively plausible modelling framework. To this end, eye tracking and high-resolution voltage measurements were performed in a pilot study, and a representative data set was generated for small data problems. Two multidimensional fuzzy pattern classification models were derived. Results show that both human and technical models are significant and complement one another. While the models still require optimizing, they clearly show that the integrative approach is practicable and fruitful. Our findings call for further transdisciplinary research of the development of cognitive assistance as well as predictive maintenance and quality assurance.

A novel ensemble fuzzy classification model in SARS-CoV-2 B-cell epitope identification for development of protein-based vaccine

B-cell epitope prediction research has received growing interest since the development of the first method. B-cell epitope identification with the aid of an accurate prediction method is one of the most important steps in epitope-based vaccine development, immunodiagnostic testing, antibody production, disease diagnosis, and treatment. Nevertheless, using experimental methods in epitope mapping is very time-consuming, costly, and labor-intensive. Therefore, although successful predictions with in silico methods are very important in epitope prediction, there are limited studies in this area. The aim of this study is to propose a new approach for successfully predicting B-cell epitopes for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, the SARS-CoV B-cell epitope prediction performances of different fuzzy learning classification models genetic cooperative competitive learning (GCCL), fuzzy genetics-based machine learning (GBML), Chi’s method (CHI), Ishibuchi’s method with weight factor (W), structural learning algorithm on vague environment (SLAVE) and the state-of-the-art ensemble fuzzy classification model were compared. The obtained results showed that the proposed ensemble approach has the lowest error in SARS-CoV B-cell epitope estimation compared to the base fuzzy learners (average error rates; ensemble fuzzy=8.33, GCCL=30.42, GBML=23.82, CHI=29.17, W=46.25, and SLAVE=20.42). SARS-CoV and SARS-CoV-2 have high genome similarities. Therefore, the most successful method determined for SARS-CoV B-cell epitope prediction was used in SARS-CoV-2 cell epitope prediction. Finally, the eventual B-cell epitope prediction results obtained for SARS-CoV-2 with the ensemble fuzzy classification model were compared with the epitope sequences predicted by the BepiPred server and immunoinformatics studies in the literature for the same protein sequences according to VaxiJen 2.0 scores. We hope that the developed epitope prediction method will help design effective vaccines and drugs against future outbreaks of the coronavirus family, especially SARS-CoV-2 and its possible mutations.

Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works.

Developing a biotechnical scheme using fuzzy logic model for classification of severity of pyelonephritis

Developing a biotechnical scheme using fuzzy logic model for classification of severity of pyelonephritis