Neuro-fuzzy Classifier Research Articles

A novel maternal thyroid disease prediction using multi-scale vision transformer architecture with improved linguistic hedges neural-fuzzy classifier.

Early pregnancy thyroid function assessment in mothers is covered. The benefits of using load-specific reference ranges are well-established. We pondered whether the categorization of maternal thyroid function would change if multiple blood samples obtained early in pregnancy were used. Even though binary classification is a common goal of current disease diagnosis techniques, the data sets are small, and the outcomes are not validated. Most current approaches concentrate on model optimization, focusing less on feature engineering. The suggested method can predict increased protein binding, non-thyroid syndrome (NTIS) (simultaneous non-thyroid disease), autoimmune thyroiditis (compensated hypothyroidism), and Hashimoto's thyroiditis (primary hypothyroidism). In this paper, we develop an automatic thyroid nodule classification system using a multi-scale vision transformer and image enhancement. Graph equalization is the chosen technique for image enhancement, and in our experiments, we used neural networks with four-layer network nodes. This work presents an enhanced linguistic coverage neuro-fuzzy classifier with chosen features for thyroid disease feature selection diagnosis. The training procedure is optimized, and a multi-scale vision transformer network is employed. Each hop connection in Dense Net now has trainable weight parameters, altering the architecture. Images of thyroid nodules from 508 patients make up the data set for this article. Sets of 80% training and 20% validation and 70% training and 30% validation are created from the data. Simultaneously, we take into account how the number of training iterations, network structure, activation function of network nodes, and other factors affect the classification outcomes. According to the experimental results, the best number of training iterations is 500, the logistic function is the best activation function, and the ideal network structure is 2500-40-2-1. K-fold validation and performance comparison with previous research validate the suggested methodology's enhanced effectiveness.

Human Hand Gesture Classification Based on Surface Electromyography Signals by Using a Vector–Kernel Convolutional Takagi–Sugeno–Kang Neuro-fuzzy Classifier

Human Hand Gesture Classification Based on Surface Electromyography Signals by Using a Vector–Kernel Convolutional Takagi–Sugeno–Kang Neuro-fuzzy Classifier

Hybrid Neuro-Fuzzy Classifier for Monitoring the Effectiveness of Treatment of Diseases of the Respiratory System, Taking into Account Comorbidity

The purpose of research is to develop a hybrid neuro-fuzzy classifier for remote monitoring of the severity of community-acquired pneumonia, taking into account the risk of concomitant diseases.Methods. To assess the severity of community-acquired pneumonia and determine the effectiveness of its treatment plan, a hybrid neural network is included in the hybrid neuro-fuzzy classifier, which contains three macrolayers: PNNFNN-FNN*. The number of decisive blocks of the PNN macrolayer is equal to the number of segments allocated in the space of informative features, and the output of each PNN block produces risk and non-risk assessments of communityacquired pneumonia by severity clusters. Aggregation of decisions made over N segments of the space of informative features is carried out in the FNN layer, which has the structure of a fuzzy decision-making module. The aggregation of 2L PNN-FNN outputs occurs in the FNN* macrolayer. The same macrolayer takes into account the influence of comorbidity on the severity of community-acquired pneumonia.Results. The testing of a hybrid neuro-fuzzy classifier of the severity of community-acquired pneumonia was carried out on an experimental group of patients with community-acquired pneumonia with comorbidity in the form of coronary heart disease. Indicators of the quality of classification of the severity of pneumonia taking into account the risk of comorbid disease using the example of coronary heart disease showed that the aggregation of the classifier of the severity of community-acquired pneumonia and the classifier of the risk of comorbid disease in the form of a hybrid neuro-fuzzy classifier makes it possible to improve the quality of assessing the severity of community-acquired pneumonia by more than 10% for all quality indicators.Conclusion. A hybrid neuro-fuzzy classifier, built on different pattern recognition paradigms, makes it possible to identify clusters of disease severity and improve the quality indicators for classifying the severity of community-acquired pneumonia in the presence of comorbidity by an average of 12%.

A pragmatic approach of Parkinson disease detection using hybrid case-based reasoning neuro-fuzzy classification system over Mobile edge computing

Parkinson’s disease is neurological degenerative disorder cause by deficient dopamine production which in turn harms the motor functionality and speech. With latest IoT advancement in the health care era, we propose intelligent and smart Parkinson’s disease detection system based on voice signal analysis. Addition to PDs detection, we propose remote health monitoring feature that keep on monitoring and diagnosing PD person activity. To perform all tasks efficiently we divide our propose model in three phases: monitoring, diagnosing and analysis. During monitoring phase, PDs person voice signal is monitored and captured via IoT sensor enabled Smartphone device. This voices signal is further processed for PD detection over MEC server during diagnosing phase. We use Tunable Q factor wavelet transform (TQWT) for extracting feature from voice sample, these extracted feature are reduced FRS methods. For feature reduction PCA and LDA are used. Theses processed feature are then applied to hybrid case-based reasoning neuro-fuzzy (ANFIS) classification system to detect Parkinson’s disease. On the detection of PDs abnormality, the proposed healthcare monitoring system immediately generates notification to the patient simultaneously send detection report to centralized healthcare cloud system. This PDs detection report is further analyzed and stored at cloud server during analysis phase where report is analyzed by professional health expert and send the appropriate treatment and medication to PD infected person or care taker. For experimentation and performance evaluation benchmark baseline UCI dataset of PDs are used. We analyzed our proposed hybrid ANFIS-CBR classifier with existing classifiers over the accuracy, sensitivity and specificity parameter. Based on the result analysis, it is observed that proposed hybrid classifier maximum accuracy, sensitivity, and specificity of 98.23%, 99.1%, and 95.3% in comparison to other classifier.

Improving Accuracy of Integrated Neuro-Fuzzy Classifier with FCM based Clustering for Diagnosis of Psychiatric Disorder

Parkinson’s disease (PD) is a progressive neurodegenerative disorder. Autism spectrum disorder (ASD) is a neurodevelopment disorder. Clinical decision-making process is complex. Due to complex nature of disease sign and its symptoms clinical decision making may lead to misclassification. To deal with such complex medical problems methods or approaches of soft computing play an important role. This paper will focus on presenting an integrated Neuro-fuzzy model. This integrated model has the learning strength of neural network and knowledge representation ability of fuzzy logic. Modified Adaptive Neuro –Fuzzy inference system (M-ANFIS) is used here for classification and predication. Here Fuzzy C-mean (FCM) Clustering is used first to make classes of data before presenting in to ANFIS. This FCM based class will reduce the classifier computational overhead. Precision error and recall, F-measure and accuracy matrices are used to compare the experimental results with other classic methods.

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.

An online learning algorithm for a neuro-fuzzy classifier with mixed-attribute data

General fuzzy min–max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification. However, one of the downsides of its original learning algorithms is the inability to handle and learn directly from mixed-attribute data without using encoding techniques. While categorical feature encoding methods can be used with the GFMMNN learning algorithms, they exhibit many shortcomings. Other improved approaches proposed in the literature are not suitable for online learning algorithms working in the dynamically changing environments without ability to retrain or access full historical data, which are usually required for many real world applications. This paper proposes an extended online learning algorithm for the GFMMNN. The proposed method can handle the datasets with both continuous and categorical features. It uses the change in the entropy values of categorical features of the samples contained in a hyperbox to determine if the current hyperbox can be expanded to include the categorical values of a new training instance. An extended architecture of the original GFMMNN and its new membership function are introduced for mixed-attribute data. Important mathematical properties of the proposed learning algorithms are also presented and proved in this paper. The extensive experiments confirmed superior and stable classification performance of the proposed approach in comparison to other relevant learning algorithms for the GFMM model.

Neuro-Fuzzy Classification of Technical Conditions of Objects of Complex Structure

The application of neural-fuzzy technologies for solving the problem of classification of states of objects of complex structure is considered. A quantitative assessment of the technical condition of the facility is proposed on the basis of a complex indicator — the technical condition index (ITS). The methodology of classification of technical conditions of objects of complex structure is based on the adaptive neural mesh interference system (ANFIS) and the neural mesh classifier (NNA), which significantly accelerated and improved the accuracy of calculations for effective management of these objects.

Artificial Neural Networks for Brain Cancer Classification

The detection and categorization of brain tumours is now possible because to new technology. The MRI images of individuals who have been diagnosed with Astrocytoma are analyzed by the system, which makes use of an artificial neural network, in order to identify tumour blocks or lesions also to classify the type of tumour. These approaches are described below. The Grey Level Co-occurrence Matrix (GLCM) was the tool that made it feasible to characterize the different textures of tumours. A contrast is drawn between each of these traits and those that are already included in the Knowledge Base. Finally, after years of research and development, a Neuro Fuzzy Classifier has been created that is capable of correctly identifying the various subtypes of brain cancer. An initial phase of testing focused on learning and training, followed by a second phase focused on recognizing and assessing, have been carried out on the system in its entirety. The Radiology Department at Tata Memorial Hospital (TMH) was kind enough towards supply the training data for the system, which consisted of MRI scans of patients who had already been diagnosed with brain cancer. The method was also validated by using unlabeled MRI image data taken from patients with brain tumours treated at TMH. The classification of these samples and the handling of outliers were both determined to be successfully handled by the system.

EFNC-Exp: An evolving fuzzy neural classifier integrating expert rules and uncertainty

Data stream classification processes with neuro-fuzzy approaches may involve situations where uncertainties arise, which may directly interfere with the quality of the results of the evolving models. Another factor that can help improve the performance of neuro-fuzzy evolving models is using a priori knowledge about a topic and incorporating it into the model's training procedure. The definition of fuzzy rules with a high degree of representativeness for certain classes can help models increase the significance of the representation of these labels and thus boost their predictive performance for these classes. This article proposes the integration of uncertainty in experts' feedback on the class labels and the integration of expert rules into the classifier architecture and the evolving, adaptive learning engine. This uncertainty integration occurs by combining it in defining neurons' weights in the first layer of the model and incorporating these weight values in the Gaussian neurons in the model's first layer; furthermore, uncertainty is integrated into an incremental feature weighting concept (inducing a weighted version of it) for the curse of dimensionality reduction. The proof of the new concepts will be carried out through tests on binary pattern classification problems in real-world data streams and a comparison between our approach and several related state-of-the-art works in evolving (neuro-) fuzzy modeling. The results obtained by the model showed that, by explicitly respecting the uncertainty of the class labels in the process of updating the evolving neuro-fuzzy classifier, the accuracy trend lines showed a robust behavior as the degree of distortion existing in the class labels of the samples due to uncertainty could be partially compensated.

3WDNFS – Three-way decision neuro-fuzzy system for classification

The three-way decision approach is an emerging paradigm in the design of tools for data mining and machine learning. It switches from a two-way classification (“negative” and “positive” class) to three decisions: “negative”, “positive”, and non-commitment class. It means that when for some data it is not possible to elaborate a reliable answer they are assigned with a non-commitment class. In the paper we apply this paradigm for a cascade of neuro-fuzzy classifiers. If the first neuro-fuzzy system assigns a data item with a non-commitment class, the next neuro-fuzzy system is run for this data item. For easy items the first system is enough, but for harder ones two or more systems have to be run. Neuro-fuzzy systems elaborate interpretable fuzzy models. The models are composed of fuzzy rules that can be interpreted linguistically by humans. Application of neuro-fuzzy systems results in a cascade of interpretable models. The paper describes algorithms for training a cascade of neuro-fuzzy classifiers and for elaboration of answers. The paper presents results of numerical experiments that show that this technique can elaborate results with lower generalisation error than two-way classifiers. The implementation of the proposed system is available from a github repository.

Intrusion aware data transmission framework in wireless sensor network

Intrusion attack is considered as the major concerns to be focussed in wireless sensor network which should be seriously viewed for identification of secure and trustworthy information processing. The various characteristics involved in Intrusion attacks should be adapted precisely since it impacts on result of the intrusion detection in terms of accuracy. PCA-based centralized approach (PCACID) and Knowledge based Intrusion Detection Strategy (KBIDS) is suggested in this research for achieving the accurateintrusion detection. Though KBIDS is involved in achieving accurate detection, the demerit is that time complexity and computational overhead are progressively more which in turn influences on the entire network performance. Traffic Variation based Intrusion Detection System (TV-IDS) plays a major role in mitigating these issues. In addition to it, Fuzzy based mean shift clustering is also suggested for incorporating clustering feature process which influences precise clustering result with the advantage of less time complexity. The decision classifier takes its role after the assessment of data points bias variations. This variation factor helps in recognizing smaller traffic variation and not determined as irregular data. The classification is achieved by hybrid genetic neuro fuzzy classifier. The updating of ANFIS weight values is accomplished concurrently with optimal selection by means of genetic algorithm. The optimal route path is chosen by greatly utilizing the artificial bee colony algorithm. The various fitness parameters involved in this research are energy level of nodes, bandwidth, etc., for efficient data transmission successfully. MATLAB simulation platform is greatly utilized for assessment of overall results for validating that proposed TV-IDS achieves improved outcomes comparatively.

Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images

Lung disease is a most common disease all over the world. A numerous feature extraction with classification models were discussed previously about the lung disease, but those methods having high over fitting problem, consequently, decrease the accuracy of detection. To overwhelm this issue, a Deep Convolutional Spiking Neural Network optimized with Arithmetic Optimization Algorithm is proposed in this manuscript for Lung Disease Detection using Chest X-ray Images as COVID-19, normal and viral pneumonia. Initially, NIH chest X-ray image dataset is taken from Kaggle repository for detecting lung disease. Then, the chest X-ray images are pre-processed using the Anisotropic Diffusion Filter Based Unsharp Masking and crispening scheme for removing noise and enhancing the image quality. These pre-processed outputs are fed to feature extraction. In feature extraction process, the empirical wavelet transform method is used. These extracted features are given into Deep Convolutional Spiking Neural Network classifier (DCSNN) for detecting lung diseases. Here, the weight with bias parameter of DCSNN is enhanced based upon Arithmetic Optimization Algorithm (AOA), which improves detection accuracy. The simulation is executed in MATLAB. The proposed LDC-DCSNN-AOA technique attains higher accuracy, higher Precision, higher F-Score analyzed with the existing techniques, like Lung disease detection using Support Vector Machines optimized with Social Mimic Optimization (LDC-SVM-SMO), Lung disease detection using eXtreme Gradient Boosting optimized by particle swarm optimization (LDC-XGBoost-PSO), Lung disease detection using neuro-fuzzy classifier optimized with multi-objective genetic algorithm (LDC-NFC-MOGA), Lung disease detection using convolutional neural network optimized with Bayesian optimization LDC –CNN-BOA respectively.

Optimizing deep neuro-fuzzy classifier with a novel evolutionary arithmetic optimization algorithm

Deep Neuro-Fuzzy System has been successfully employed in various applications. But, the model faces two issues: (i) dataset with many features exponentially increases the fuzzy rule-base, (ii) parameters in the fuzzy rule-base are optimized using the gradient descent approach, which has the drawback of local minima. Therefore, this study aims on improving the model’s accuracy by proposing Arithmetic Optimization Algorithm. The outcomes using the Arithmetic Optimization Algorithm for feature selection have not only reduced the burden of implementing a huge dataset, but the Arithmetic Optimization-based deep neuro-fuzzy system has outperformed with 95.14% accuracy compared to the standard method with 94.52%.

Parkinson’s disease identification in homo sapiens based on hybrid ResNet-SVM and resnet-fuzzy svm models

The identification of Parkinson’s Disease (PD) is a necessary concern for reducing the occurrences of nervous disorders and brain death. The prediction of PD based on symptoms is depending on the body conditions of patients as the symptoms differ for every individual. Doctors preferably use ionized radiation-free MRI scans since they offer more precise images of soft tissues in the brain. In the recent years, deep learning is the prominently used method for performing image analysis and classification. However, the systems developed using deep learning are not able to predict the PD accurately. In order to bridge the gaps present in the existing systems, we propose a hybrid model based on neuro-fuzzy classification to detect PD more accurately. For enhancing the accuracy of PD identification, we used the ResNet-18 deep learning architecture for the classification of MRI images. In addition to this, a hybrid framework is also proposed in this paper where the softmax layer of ResNet-18 is modified using non-linear SVM and Fuzzy SVM (fSVM) classifiers. The convolution and max-pooling layers of ResNet-18 are able to learn more objective features for classification. The proposed hybrid model of ResNet-fSVM is evaluated on the neuro-MRI images from the PPMI dataset and achieved 4.4% higher accuracy than the ResNet-18 model and 2.8% higher accuracy than hybrid ResNet-SVM model. The age group based results obtained in this work has proved that the accuracy of the proposed ResNet-fSVM hybrid model is better when it is compared with ResNet-18 and hybrid ResNet-SVM models. This system effectively detects Early-onset PD through its efficiency in classification.

Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier

Rotating equipment is considered as a key component in several industrial sectors. In fact, the continuous operation of many industrial machines such as sub-sea pumps and gas turbines relies on the correct performance of their rotating equipment. In order to reduce the probability of malfunctions in this equipment, condition monitoring, and fault diagnosis systems are essential. In this work, a novel approach is proposed to perform fault diagnosis in rotating equipment based on permutation entropy, signal processing, and artificial intelligence. To that aim, vibration signals are employed for an indication of bearing performance. In order to facilitate fault diagnosis, fault detection and isolation are performed in two separate steps. As first, once a vibration signal is received, the faulty state of the bearing is determined by permutation entropy. In case a faulty state is detected, the fault type is determined using an approach based on signal processing and artificial intelligence. Wavelet packet transform and envelope analysis of the vibration signals are utilized to extract the frequency components of the fault. The proposed approach allows for the automatic selection of a frequency band that includes the characteristic resonance frequency of the fault, which is subject to change in different operational conditions. The method works by extracting the proper features of the signals that are used to decide about the faulty bearing’s condition by a multi-output adaptive neuro-fuzzy inference system classifier. The effectiveness of the approach is assessed by the Case Western Reserve University dataset: the analysis demonstrates the proposed method’s capabilities in accurately diagnosing faults in rotating equipment as compared to existing approaches.

Retraction Note to: Early diagnose breast cancer with PCA-LDA based FER and neuro-fuzzy classification system

Retraction Note to: Early diagnose breast cancer with PCA-LDA based FER and neuro-fuzzy classification system

Intelligent digitalization of cardiovascular risks

The aim of the study was to develop a mathematical model of the risks of the cardiovascular system based on the selected factors affecting cardiovascular diseases (CVD) and to test the developed mathematical model on a sample of clinical examples. CVD risk factors was grouped by types: biological indicators (anthropometric, biochemical, morphological, physiological), disease indicators, social indicators. An assessment of the degree of risk for each of the indicators was carried out by calculating the degree of risk using the membership formula, then evaluating the hazard class (according to the degree of risk) using a logical-linguistic model and a training algorithm for the neural fuzzy classifier of the network. The correctness of the risk determination by the developed model was confirmed by the analyzed 60 verified cases of acute cerebrovascular accident (18 men and 42 women). The analysis of the test results of the constructed neuro-fuzzy classifier allows us to conclude that it works satisfactorily even when using incomplete information, which makes it possible to use it for prompt decision-making. The results of testing on clinical examples, with an acceptable level of significance of a type I error of 0.05, showed that the risk was determined correctly. The factors influencing the risk of CVD are identified and designated as the corresponding linguistic variables. A logical-linguistic model was built, from which a transition was made to a hybrid neuro-fuzzy classifier, which allows assessing the influence of the identified factors on the level of risk of CVD. As a result of approbation of the model of intellectual digitalization of risks of the cardiovascular system on real clinical examples, it was confirmed that the risk was determined correctly, which means that it is possible to assert about the prospects for introducing this model into clinical practice and guaranteeing medical specialist more accurate diagnosis and optimization of their activities.

Early Detection of Autism Spectrum Disorders (ASD) with the Help of Data Mining Tools.

Autism is a disorder of neurobiological origin that originates a different course in the development of verbal and nonverbal communication, social interactions, the flexibility of behavior, and interests. The results obtained offer relevant information to reflect on the practices currently used in assessing the development of children and the detection of ASD and suggest the need to strengthen the training of health professionals in aspects such as psychology and developmental disorders. This study, based on genuine and current facts, used data from 292 children with an autism spectrum disorder. The input dataset has 20 characteristics, and the output dataset has one attribute. The output property indicates whether or not a certain person has autism. The research study first and foremost performed data pretreatment activities such as filling in missing data gaps in the data collection, digitizing categorical data, and normalizing. The features were then clustered using k-means and x-means clustering methods, then artificial neural networks and a linguistic strong neurofuzzy classifier were used to classify them. The outcomes of each strategy were examined, and their respective performances were compared.

Diagnosing Breast Cancer Based on the Adaptive Neuro-Fuzzy Inference System.

In this work, a novel hybrid neuro-fuzzy classifier (HNFC) technique is proposed for producing more accuracy in input data classification. The inputs are fuzzified using a generalized membership function. The fuzzification matrix helps to create connectivity between input pattern and degree of membership to various classes in the dataset. According to that, the classification process is performed for the input data. This novel method is applied for ten number of benchmark datasets. During preprocessing, the missing data is replaced with the mean value. Then, the statistical correlation is applied for selecting the important features from the dataset. After applying a data transformation technique, the values normalized. Initially, fuzzy logic has been applied for the input dataset; then, the neural network is applied to measure the performance. The result of the proposed method is evaluated with supervised classification techniques such as radial basis function neural network (RBFNN) and adaptive neuro-fuzzy inference system (ANFIS). Classifier performance is evaluated by measures like accuracy and error rate. From the investigation, the proposed approach provided 86.2% of classification accuracy for the breast cancer dataset compared to other two approaches.