Fuzzy Classification Research Articles

Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

As an agricultural commodity, corn functions as food, animal feed, and industrial raw material. Therefore, diseases and pests pose a major challenge to the production of corn plants. Modeling the classification of corn plant diseases and pests based on digital images is essential for developing an information technology-based early detection system. This plant’s early detection technology is beneficial for lowering farmers’ losses. The detection system based on digital images is also cost-effective. This paper aims to model the classification of corn plant diseases and pests based on digital images by implementing fuzzy discretization. Discretization is an essential technique to improve the knowledge extraction process of continuous-type data. It is also essential in some methods where continuous data must be processed or handled. Fuzzy discretization allows classes to have overlapping intervals so that they can handle information that is vague or unclear. We developed hypotheses and proved that different combinations of membership functions in fuzzy discretization affect classification performance. Empirical assessment using Monte Carlo resampling was carried out to obtain the generalizability of the performance of the best classification model of all proposed models. The best model is determined based on the number of metrics with the highest value and the highest metric on the Fscore and Kappa, a multiclass measure. The combination of digital image data preprocessing and classification methods also affects the performance of the classification model. We hope this work can provide an overview for experts in building early detection systems of corn plant diseases and pests using classification models based on fuzzy discretization.

Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min-Max Neural Network for Cervical Cancer Diagnosis.

Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions about the diagnosis of various cancers, including breast cancer, cervical cancer, etc. The Pap-smear test is the commonly used diagnostic procedure for early identification of cervical cancer, but it has a high rate of false-positive results due to human error. Therefore, computer-aided diagnostic systems based on deep learning need to be further researched to classify the pap-smear images accurately. A fuzzy min-max neural network is a neuro fuzzy architecture that has many advantages, such as training with a minimum number of passes, handling overlapping class classification, supporting online training and adaptation, etc. This paper has proposed a novel hybrid technique that combines the deep learning architectures with machine learning classifiers and fuzzy min-max neural network for feature extraction and Pap-smear image classification, respectively. The deep learning pretrained models used are Alexnet, ResNet-18, ResNet-50, and GoogleNet. Benchmark datasets used for the experimentation are Herlev and Sipakmed. The highest classification accuracy of 95.33% is obtained using Resnet-50 fine-tuned architecture followed by Alexnet on Sipakmed dataset. In addition to the improved accuracies, the proposed model has utilized the advantages of fuzzy min-max neural network classifiers mentioned in the literature.

Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery

ABSTRACT Satellite remote-sensing provides a cost- and time-effective tool for ship monitoring at sea. Most existing approaches focused on extraction of ship locations using either hull or wake. In this paper, a method of cascaded detection of ship hull and wake was proposed to locate and classify ships using high-resolution satellite imagery. Candidate hulls were fast located through phase spectrum of Fourier transform. A hull refining module was then executed to acquire accurate shapes of candidate hull. False alarms were removed through the shape features and textures of candidate hulls. The probability that a candidate hull is determined as a real one increased with the presence of wakes. After true ships were determined, ship classification was conducted using a fuzzy classifier combining both hull and wake information. The proposed method was implemented to Gaofen-1 panchromatic and multispectral (PMS) imagery and showed good performance for ship detection with recall, precision, overall accuracy, and specificity of 90.1%, 88.1%, 98.8%, and 99.3%, respectively, better than other state-of-the-art coarse-to-fine ship detection methods. Ship classification was successfully achieved for ships with detected wakes. The accuracy of correct classification was 83.8% while the proportion of false classification was 1.0%. Factors influencing the accuracy of the developed method, including texture features and classifiers combination and key parameters of the method, were also discussed.

A fuzzy-based framework for diagnosing esophageal mobility disorder using high-resolution manometry

In recent years, the high-resolution manometry (HRM) technique has been increasingly used to study esophageal and colonic pressurization and has become a standard routine for discovering mobility disorders. In addition to evolving guidelines for the interpretation of HRM like Chicago standard, some complexities, such as the dependency of normative reference values on the recording device and other external variables, still remain for medical professions. In this study, a decision support framework is developed to aid the diagnosis of esophageal mobility disorders based on HRM data. To abstract HRM data, Spearman correlation is employed to model the spatio-temporal dependencies of pressure values of HRM components and convolutional graph neural networks are then utilized to embed relation graphs to the features vector. In the decision-making stage, a novel Expert per Class Fuzzy Classifier (EPC-FC) is presented that employs the ensemble structure and contains expertized sub-classifiers for recognizing a specific disorder. Training sub-classifiers using the negative correlation learning method makes the EPC-FC highly generalizable. Meanwhile, separating the sub-classifiers of each class gives flexibility and interpretability to the structure.The suggested framework is evaluated on a dataset of 67 patients in 5 different classes recorded in Shariati Hospital. The average accuracy of 78.03% for a single swallow and 92.54% for subject-level is achieved for distinguishing mobility disorders. Moreover, compared with the other studies, the presented framework has an outstanding performance considering that it imposes no limits on the type of classes or HRM data. On the other hand, the EPC-FC outperforms other comparative classifiers such as SVM and AdaBoost not only in HRM diagnosis but also on other benchmark classification problems.

A DISEASE PREDICTION MODEL USING SPOTTED HYENA SEARCH OPTIMIZATION AND BI-LSTM

Early disease prediction is the best solution for any deadly disease in this fast internet world. In this direction, machine learning and deep learning techniques are applied in this fast world to predict diseases early and update the disease level through the Internet of Things techniques. For this purpose, this paper proposes a disease prediction system that uses the newly proposed fuzzy temporal correlation aware classifier and the auto-encoded bidirectional long short-term memory (FTC-Bi-LSTM) to predict the diseases such as heart, cancer, and diabetes. Moreover, this paper proposes a hybrid optimization algorithm called spotted hyena and cuckoo search optimization algorithm (SHCSA) to select the contributed features used to enrich the prediction accuracy. The standard benchmark UCI repository dataset is used to conduct the various experiments and obtain better performance in terms of precision, recall, f-measure, and prediction accuracy. The result is validated by considering the hospital patient data as input and proving the performance.

A DISEASE PREDICTION MODEL USING SPOTTED HYENA SEARCH OPTIMIZATION AND BI-LSTM

Early disease prediction is the best solution for any deadly disease in this fast internet world. In this direction, machine learning and deep learning techniques are applied in this fast world to predict diseases early and update the disease level through the Internet of Things techniques. For this purpose, this paper proposes a disease prediction system that uses the newly proposed fuzzy temporal correlation aware classifier and the auto-encoded bidirectional long short-term memory (FTC-Bi-LSTM) to predict the diseases such as heart, cancer, and diabetes. Moreover, this paper proposes a hybrid optimization algorithm called spotted hyena and cuckoo search optimization algorithm (SHCSA) to select the contributed features used to enrich the prediction accuracy. The standard benchmark UCI repository dataset is used to conduct the various experiments and obtain better performance in terms of precision, recall, f-measure, and prediction accuracy. The result is validated by considering the hospital patient data as input and proving the performance.

Classification optimization of traditional Chinese medicine materials based on additive physical properties of powder

According to the method of predicting the physical properties of oily powder based on the additive physical properties of Chinese medicinal powder, Dioscoreae Rhizoma and calcined Ostreae Concha with high sieve rate and good fluidity were mixed and crushed with Persicae Semen, Platycladi Semen, Raphani Semen, Ziziphi Spinosae Semen, and other typical oily materials with high fatty oil content in proportion to obtain 23 mixed powders. Fifteen physical properties such as bulk density, water absorption, and maximum torque force were measured, and the physical properties of typical oily powders were predicted. When the mixing and grinding ratio was in the range of 5∶1-1∶1, the r value in the correlation equation between the weighted average score of the mixed powder and the powder proportion ranged from 0.801 to 0.986, and the linearity was good, indicating that the method of predicting the physical properties of oily powder based on the additive physical properties of traditional Chinese medicine(TCM)powder was feasible. The results of cluster analysis showed that the classification boundaries of the five kinds of TCM materials were clear, and the similarity of the physical fingerprints of powdery and oily materials decreased from 80.6% to 37.2%, which solved the problem of fuzzy classification boundaries of powdery and oily materials due to the lack of representativeness of oily material model drugs. The classification of TCM materials was optimized, laying a foundation for optimizing the prediction model of the prescription of personalized water-paste pills.

Comparative Analysis of Clustering Methods for Fuzzy Classifiers Simplification

One problem of fuzzy systems for classification tasks is the exponential growth of rule generation, which translates into excessively long processing times. Different proposals in the literature address this problem through exhaustive rule reduction techniques, which achieve competitive results compared to conventional classifiers by reducing their computational complexity as well. This paper proposes a methodology that is comprised of two stages: 1) a clustering technique that helps identify the structure of an initial low-accurate classifier and 2) a differential evolution parameter identification stage that takes that low-accurate classifier and refines it to obtain a high-accurate classifier. In this way, a comparative analysis among several clustering methods is performed, allowing the users to select a reduced rule set and avoid the use of traditional rule search algorithms. The results show that the Gaussian Mixture Model is the most suitable clustering technique to identify the structure of fuzzy classifiers, since it provides the corresponding fuzzy model with highly competitive classification performance compared to other state-of-the-art methods.

A Proposal of Robust Condition Monitoring Scheme for Industrial Systems

The Industry 4.0 paradigm aims to obtain high levels of productivity and efficiency, more competitive final products and compliance with the demanding regulations related to industrial safety. To achieve these objectives, the industrial systems must be equipped with condition monitoring systems for early detection, isolation, and location of faults. The paper presents a proposal for a condition monitoring system characterized by its robustness in presence of noise and missing variables in the measurements. The proposal combines the use of simple and effective imputation algorithms with a fuzzy classification kernel algorithm based on the use of the non-standard Pythagorean fuzzy sets. The proposed scheme was validated using the known DAMADICS test problem with excellent results.

Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy

Nowadays, the call for transparency in Artificial Intelligence models is growing due to the need to understand how decisions derived from the methods are made when they ultimately affect human life and health. Fuzzy Rule-Based Classification Systems have been used successfully as they are models that are easily understood by models themselves. However, complex search spaces hinder the learning process, and in most cases, lead to problems of complexity (coverage and specificity). This problem directly affects the intention to use them to enable the user to analyze and understand the model. Because of this, we propose a fuzzy associative classification method to learn classifiers with an improved trade-off between accuracy and complexity. This method learns the most appropriate granularity of each variable to generate a set of simple fuzzy association rules with a reduced number of associations that consider positive and negative dependencies to be able to classify an instance depending on the presence or absence of certain items. The proposal also chooses the most interesting rules based on several interesting measures and finally performs a genetic rule selection and adjustment to reach the most suitable context of the selected rule set. The quality of our proposal has been analyzed using 23 real-world datasets, comparing them with other proposals by applying statistical analysis. Moreover, the study carried out on a real biomedical research problem of childhood obesity shows the improved trade-off between the accuracy and complexity of the models generated by our proposal.

Inculcating Context for Emoji Powered Bengali Hate Speech Detection using Extended Fuzzy SVM and Text Embedding Models

The massive growth of social webs offer opportunities to communicate with diverse languages, unstructured text, informal posts, misspelled contents and emojis. Social media users feel comfortable to express their emotions specially emotions with high intensity (hate speech) in their mother tongue. Hate speech in any form targets groups and individuals that may trigger antisocial activities, hate crimes, and terrorist acts. Bengali social media users use Bengali for posting implicit or indirect hate text. Existing Bengali hate speech detection research considers explicit hate speech detection but in actual hate is expressed more in implicit way. In order to detect both implicit and explicit hate speech from low resource content, social webs need highly efficient automated tools. Researchers applied discriminative learning approaches (i.e. SVM, MLP, CNN) to distinguish hate text with only clear-cut outcomes in detecting direct hate speech. The proposed novel Bengali hate speech detection model considers two parallel approaches: (i) It applies extended fuzzy SVM classifier for class imbalanced dataset (FSVMCIL) and multilingual BERT (mBERT) text embedding model to detect first hate label; (ii) Morphological analysis method to detect implicit and explicit hate content with the hate similarity (HS) scheme for second hate label. Linking both labeling methods, this research extracts contextual Bengali hate speech from informal text. This novel HS method considers Word2Vec word embedding model and Bengali hate lexicon. It also considers emoji to text conversion for efficient contextual analysis. This study also conducts extensive experiments for various categories with the Bengali hate speech dataset. It also evaluates the proposed model performance considering weighted F1 score, precision, recall and accuracy parameters. Results reveal significant improvement in Bengali hate speech detection with 2.35% increase in F1- score and 9.11 % increase in accuracy.

Water Quality Level for Shrimp Pond at Probolinggo Area Based on Fuzzy Classification System

Since several years ago, vaname shrimp (Litopenaeus vannamei) has been extensively cultivated in Indonesia because it has good business opportunities. In aquaculture activities, water quality is an important factor that dramatically impacts the survival and quality of shrimp in the pond. Therefore, information of water quality must be known by the farmer for obtaining a satisfactory harvest. This study aims to develop a water quality monitoring system based on information of temperature, pH, salinity, and dissolved oxygen. The data from sensors are sent to the cloud utilizing Internet of Things (IoT) technology and then classified by a fuzzy logic system. In order to help farmers easily know the water quality of their shrimp pond, four sensor data including the result of classification from fuzzy logic are sent to the phone. After a trial of the system, 100% of the data are successfully sent to the cloud (google spreadsheet). The system also successfully classified the level of water quality as the expectation of the farmer. With this system, it is hoped that it can assist farmers in monitoring the water quality of shrimp pond to improve the quality and quantity of shrimp.

Analytic deliberation approach for collaborative determination of impact significance of projects in dispute, a case study in Mexico

The determination of impact significance is a critical step in Environmental Impact Assessment (EIA). Yet, the notion of impact significance is inherently subjective, immersed in uncertainty, and influenced by competing interests. This article addresses the difficult task of collaboratively determining the significance of the environmental impacts of projects in dispute. We introduce an approach centered on analytic deliberation that entails three steps: analysis, translation, and deliberation. Analysis combines and synthesizes impact significance into a single index using a fuzzy pattern classification algorithm, which also integrates the inherent uncertainty of impact assessment. Translation transforms the index into understandable and accessible spatial representations through geovisualization. Deliberation promotes safe spaces for collaboration in which stakeholders examine evidence openly and reflect on the conflicting values underlying their perspectives. The outcome of the approach is a compromise on the most meaningful representation of impact significance, if one exists, as the basis for agreement on the appropriate prevention, mitigation, and compensation measures of a project. Hence, it helps to reach agreement on the impact prevention, mitigation, and compensation strategy prior to the consultation process required by law in most countries. We illustrate our approach through an example from Mexico.

Empirical enhancement of EEG classification models for epileptic seizures using a fuzzy statistical perspective

The signals produced by an electrocardiogram (ECG) are made up of intricate pattern se-quences that have a periodic structure. These pattern sequences contain an initial P-wave, which denotes the beginning of an ECG wave, a QRS sequence, which denotes the intensi-ty of the pulse, and a T segment, which denotes the conclusion of the wave. Characteris-tics such as PR interval, QRS interval, QT interval, ST interval, R to R interval, etc. are employed to recognize chronic, ischemic, and other cardiac illnesses. These wave patterns need the simultaneous execution of many high-complexity signal processing operations to be classified into cardiac disorders. Signal pre-processing, feature extraction, feature se-lection, classification into epileptic and non-epileptic seizures, and post-processing are some of these operations. For each of these operations, researchers create a wide range of algorithms. These algorithms' performance differs significantly in terms of the quantity of leads utilized for ECG collection, filtering effectiveness, feature extraction & selection ef-fectiveness, and classifier effectiveness. Thus, researchers and system designers have be-come unclear when choosing the optimum algorithm set for an application. This text of-fers a thorough analysis & design of fuzzy CNN model of a broad range of epileptic & non-epileptic seizure classification techniques to lessen ambiguity. Convolutional neural network (CNN) based models outperform other models in terms of general-purpose per-formance, whereas application-specific deployments need the employment of customized fuzzy CNN models. A significant area of research & development is presented by the ob-servation that fuzzy logic techniques are not observed while constructing ECG classifica-tion models. Based on these findings, a new fuzzy logic-based classification method is provided in this text that employs quantization techniques to transform input ECG signals into fuzzy values. With these parameters and a specially created CNN model, it was pos-sible to achieve an accuracy of 99.5% for diverse ECG datasets. This accuracy was com-pared to a number of cutting-edge models, and it was observed that the suggested model is quite good at categorizing ECG signals. The suggested technique was observed to be quicker than traditional procedures due to the usage of a fuzzy logic model, enhancing its scalability for a broad range of clinical applications.

Age invariant face recognition using discriminative tensor subspace learning with fuzzy based classification

AbstractAging is a complex process that affects both the shape and texture of a face. In the aging domain, various methods have been proposed for age estimation or simulation of the face based on an image at a given age. Face recognition methods that are designed for automatic face recognition are still a challenging issue. We present a novel age invariant face recognition method using tensor subspace learning with fuzzy synthetic classification. Local Binary Pattern (LBP) processed face images are used to learn the tensor sub‐space, which converts high dimensional feature space to age‐invariants while retaining key elements of the local geometrical structure of the face. Tensor sub‐space is better in terms of sub‐space learning and feature extraction than Principal Component Analysis (PCA)/Linear Discriminant Analysis (LDA) that been used in many techniques in the literature. Tensor Normalized Face images are generated which exhibit maximum inter‐class distances and minimum intra‐class distances. Further local patches are used for synthesizing global Fuzzy membership scores to classify the test face images. Experiments performed on standard face‐aging datasets, namely FG‐NET, AGEDB, and MORPH‐Album‐II, and received accuracy of 99.15%, 99.20%, and 99.8%, respectively. Experimental results outperform the current state‐of‐the‐art techniques, and results show the promise of the proposed system for personal identification despite the aging process. It also proved that the local descriptor gives better performance over the global descriptor like PCA for the aging process. The method also demonstrated improved performance as compared with compute‐intensive methods that required training on deep networks.

Credit risk evaluation using clustering based fuzzy classification method

Credit scoring is a crucial indicator for banks to determine the financial position and the eligibility of a client for credit. In order to assign statistical odds or probabilities to predict the risk of nonpayment in relation to many other factors, the scoring criterion becomes an important issue. The focus of this study is to propose a clustering based fuzzy classification (CBFC) method for credit risk assessment. We aim to illustrate the beneficial use of machine learning (ML) methods whose prediction power is increased by adopting fuzzy theory to calculate the default risk with a better selection of the features contributing to it. An important feature of the CBFC method is that membership values obtained as a result of the fuzzy k-means clustering algorithm are used for the purpose of better capturing the structure of an existing system.An extensive comparison is performed to show how CBFC performs compared to the traditional ones for the datasets having different characteristics in terms of the variable types. Five different real-life datasets are studied to expose the contribution of fuzzy approach on improving the ML use. Our findings show that the proposed CBFC models can produce the promising classification results in credit risk evaluation which aid the practitioners and decision makers for issuance of credit purposes.

Z-number-valued rule-based classification system

The fuzzy rule-based classification system (FRBCS) is a popular tool for classification problems due to its interpretability and comprehensibility. As an extension of fuzzy numbers, the concept of Z-number is a more appropriate formal structure to describe uncertain and partially reliable information. A Z-number is an ordered pair of fuzzy numbers, where the second fuzzy number describes the reliability of the first one. As a result of its representation capability, it can receive better classification results. However, there is still a gap in the application of Z-numbers to classification problems due to their high computation complexity. To take advantage of the Z-number, we design a simple way to make Z-numbers apply to classification problems. Use the second fuzzy number to adjust the first fuzzy number to fit the training data. Then we create a kind of Z-number-valued if–then rule by extending the fuzzy if–then rule. In addition, a Z-number-valued rule-based classification system (ZRBCS) is developed, including two main processes: rule generation and new pattern classification. The developed system can cover more information than the classic fuzzy rule-based system, which can improve classification effects. The proposed ZRBCS is compared with classical FRBCS with/without certain degrees and three classical classification algorithms. According to statistical tests, ZRBCS is superior to FRBCS and two other algorithms.

On the performance and interpretability of Mamdani and Takagi-Sugeno-Kang based neuro-fuzzy systems for medical diagnosis

PurposeNeuro-fuzzy systems aim to combine the benefits of artificial neural networks and fuzzy inference systems: a neural network can learn patterns from data and achieves high performance, whereas a fuzzy system matches inputs and outputs using linguistic and interpretable rules. The combination of these two techniques yields models that can both perform well and provide interpretability in a fuzzy linguistic manner. DesignIn this paper, the performance and interpretability of five neuro-fuzzy classifiers were evaluated (three Takagi-Sugeno-Kang (TSK) classifiers: adaptive neuro-fuzzy inference system (ANFIS), dynamic evolving neuro-fuzzy system (DENFIS), self-organizing fuzzy neural network (SOFNN), and two Mamdani classifiers: hybrid fuzzy inference system (HyFIS) and neuro-fuzzy classifier (NEFCLASS)). All the empirical evaluations were over four benchmark medical datasets (Wisconsin breast cancer dataset, SPECTF heart dataset, Parkinsons dataset, and diabetic retinopathy Debrecen dataset), and used five performance criteria (accuracy, precision, recall, f score, and training time) and two interpretability criteria (number of rules and number of membership functions). FindingsResults showed that the TSK-based self-organizing fuzzy neural network classifier, in general, outperformed the others. In terms of interpretability, DENFIS and NEFCLASS were the best Takagi-Sugeno-Kang and Mamdani classifiers respectively. The findings also suggested that three classifiers: DENFIS, SOFNN, and NEFCLASS achieved a good performance-interpretability tradeoff. OriginalityTo the best of our knowledge, no study has compared the neuro-fuzzy techniques presented in this paper in terms of performance and interpretability in the medical domain.

Assessing water resources systems’ dynamic resilience under hazardous events via a genetic fuzzy rule-based system

AbstractIn this paper, the use of a novel genetic fuzzy rule-based system (FRBS) is proposed for assessing the resilience of a water resources system to hazards. The proposed software framework generates a set of highly interpretable rules that transparently represent the causal relationships of hazardous events, their timings, and intensities that can lead to the system's failure. This is achieved automatically through an evolutionary learning procedure that is applied to the data acquired from system dynamics (SD) and hazard simulations. The proposed framework for generating an explainable predictive model of water resources system resilience is applied to the Pirot water resources system in the Republic of Serbia. The results indicate that our approach extracted high-level knowledge from the large datasets derived from multi-model simulations. The rule-based knowledge structure facilitates its common-sense interpretation. The presented approach is suitable for identifying scenario components that lead to increased system vulnerability, which are very hard to detect from massive raw data. The fuzzy model also proves to be a satisfying fuzzy classifier, exhibiting precisions of 0.97 and 0.96 in the prediction of low resilience and high rapidity, respectively.

IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks

Cardiovascular diseases currently present a key health concern, contributing to an increase in death rates worldwide. In this phase of increasing mortality rates, healthcare represents a major field of research, and the knowledge acquired from this analysis of health information will assist in the early identification of disease. The retrieval of medical information is becoming increasingly important to make an early diagnosis and provide timely treatment. Medical image segmentation and classification is an emerging field of research in medical image processing. In this research, the data collected from an Internet of Things (IoT)-based device, the health records of patients, and echocardiogram images are considered. The images are pre-processed and segmented, and then further processed using deep learning techniques for classification as well as forecasting the risk of heart disease. Segmentation is attained via fuzzy C-means clustering (FCM) and classification using a pretrained recurrent neural network (PRCNN). Based on the findings, the proposed approach achieves 99.5% accuracy, which is higher than the current state-of-the-art techniques.