This study presents the pseudo-FNN, a fuzzy neural network model that integrates the pseudo-unineuron, a novel neuron type leveraging pseudo-uninorms to enhance non-commutative operations and knowledge extraction. The pseudo-FNN employs a three-layer architecture with Gaussian fuzzy neurons, where weights are derived from kernel density estimation and rule consequents are optimized using multiple algorithms. Experimental evaluations on four datasets (Iris, Haberman, Transfusion, and Mammographic Masses) demonstrate the model’s competitive performance. The pseudo-FNN outperformed traditional fuzzy neural networks such as ANFIS and showed comparable results with optimization-enhanced FNNs. Among the optimization techniques, models using SGD, Adam, and RMSProp achieved the most consistent and high accuracies across datasets with pseudo-FNN models often aligning with these trends. Statistical analysis confirmed significant improvements over non-optimized models, and the pseudo-FNN demonstrated robustness in addressing varying classification complexities. These results highlight the effectiveness of the pseudo-unineuron in advancing fuzzy neural network architectures.

In this article, we present a novel federated learning framework to multivariate regression problems, termed evolving Gaussian federated regression (eGauss+$_{\text{FR}}$). The need for a federated approach is due to the increasing problem of distributed acquisition of the data and protection for the rights of distributing these data. Regression problems are usually nonlinear and, therefore, strongly connected to the clustering to divide the data space into smaller subspaces where a linear approximation could be applied. Here, we are faced with the main drawback of traditional clustering methods, where a predefined number of clusters are needed. In federated learning problems, where the data are commonly nonidentically distributed between different sources or clients, this represents a significant challenge. This problem can be overcome by introducing an evolving approach, which adds and removes the clusters on-the-fly. The idea in our approach is to use the incremental c-regression or c-varieties clustering methods to define the clusters, which lie close to the lines and describe them with the centers and the covariance matrices. The clustering is done for each data source or client. Due to the restriction and protection of data sharing, only the centers and the covariance matrices of all clients are then transmitted to main server and merged together, which is here done in a way as proposed in eGauss+ method. From merged clusters the auxiliary points are generated, which than serve to approximate the function by using classical fuzzy models. Our proposed method was demonstrated on simple synthetic data, while synthetic and real-world datasets were used to test time complexity and scalability with the number of clients. The results demonstrate the benefits of evolving federated method, which results in high-quality approximation of the function and can be easily extended to high-dimensional problems.

In the domain of healthcare and well-being, the fusion of machine learning and data collection through medical examinations has propelled significant advancements in diabetes monitoring. Diabetes, a prevalent and intricate health condition, has garnered increasing attention due to its substantial impact on individuals’ mental and physical well-being. The use of medical examinations for real-time diabetes assessment has become pivotal, with various physiological monitoring capabilities aiding in this endeavor. Machine learning, as a subset of artificial intelligence, has further elevated the precision and effectiveness of diabetes monitoring by extracting meaningful insights from the extensive and intricate data collected through these examinations. This paper introduces a novel and interpretable computational model known as the Evolving Fuzzy Neural Network Uni-Nullneuron-Based Approach (EFNN-UniNull). Comprising three interconnected layers, this model collaboratively produces classification outcomes while concurrently providing insightful interpretations of relationships within the age group identification of diabetes patients dataset. The fuzzification method based on grid partition helps in obtaining adequate knowledge about the problem. The model underwent a comparative analysis against evolving neuro-fuzzy systems, demonstrating results approaching 85% accuracy. Notably, the model extracted knowledge based on fuzzy rules pertinent to diabetes identification.

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.

Evolving systems are models which are able to act dynamically in adaptive open-loop manner for solving data stream modeling problems within different application areas. Their parametric adaptability for architectural constructions of models allows them to flexibly solve issues of the most varied natures. Data mining problems in the agriculture area are the target of recent researches, mainly through extracting the basic characteristics from images to transform them into feature data that can be processed by machine learning approaches. This work aims to address the problem of identifying dry beans with interpretable models and results. For this purpose, an evolving neuro-fuzzy network based on uni-nullneurons, capable of extracting knowledge about a data set through if-then rules, was used in this paper. The data set used in this work was the subject of several approaches in the literature, and the results obtained (reaching about 98.18% classification accuracy) prove that the evolving neuro-fuzzy network used in this paper can identify dry seed beans with a high degree of precision while allowing the interpretation and dissemination of knowledge about their correct identification.

Coronary diseases affect a large part of the world population and have become the target of significant research in the academic field. The creation and use of intelligent models to facilitate the diagnosis of these diseases can allow treatments to be performed promptly to avoid further problems for patients. This paper applies an innovative evolving fuzzy neural network model to solve the problem of coronary heart disease diagnosis and extract valuable insights from the evaluated dataset. The null-unineurons that compose the model’s architecture can extract fuzzy rules, representing linguistic knowledge about the target problem. A dataset that condenses the most famous data sources on this problem to classify coronary heart disease was applied to state-of-the-art models of evolving fuzzy systems. The results obtained by the model applied in this study are similar to the state-of-the-art results. Furthermore, the model provides relevant interpretations about the evolution of the problem evaluation.

Interpretability in intelligent models becomes a challenge in academic research and approaches that facilitate understanding the responses obtained in models based on artificial intelligence and machine learning. This paper presents a new logical fuzzy neuron based on the concept of null-uninorm, thus called null-unineuron to compose the architecture of an evolving neuro-fuzzy model. This new structural neuron can extract advanced fuzzy rules allowing AND and OR-connections of antecedents to better interpret and understand the analyzed problem. This three-layer model uses an evolving weighted fuzzification approach based on incremental data partitioning concepts for knowledge extraction through null-unineurons whose training procedure suits the classification of binary and multiclass patterns in an online and incremental way. The weights integrated in the evolving data partitioning algorithm belong to feature importance levels and achieve an automatic shrinkage of distance calculations along unimportant input directions (features), which in turn accounts for a soft dimension reduction and the likelihood to decrease over-fitting. The new architecture proposed in this model was, subject to pattern classification tests, being more efficient compared to related (evolving) neuro-fuzzy models in the literature. Finally, experiments on various real-world data sets proved that the evolving neuro-fuzzy model proposed in this paper can act in a simplified way in the extraction of knowledge from data while providing answers with a high degree of accuracy for pattern classification problems.

Evolving fuzzy neural classifiers are incremental, adaptive models that use new samples to update the architecture and parameters of the models with new incoming data samples, typically occurring in form of data streams for classification problems. Most of the techniques assume that the target labels are permanently given as updating their structures and parameters in a fully supervised manner. This paper aims to implement ideas based on the concept of active learning in order to select the data most relevant for updating the model. This may greatly reduce annoying and costly labeling efforts for users/operators in an online system. Therefore, we propose an online active learning (oAL) methodology, which is closely linked to the internal evolving learning engine for fuzzy neurons, which is based on incremental data-cloud formation. It is thus based on the evaluation of the specificity of the current clouds, and especially by the change in their specificity with new (unsupervised) samples, in order to identify those samples carrying relevant information to the update of previously formed clouds. This is combined with the unsupervised cloud evolution criterion, which upon its fulfillment indicates a new knowledge contained in the data for which the class response needs to be known (thus should be selected for labeling feedback). In synergy to the evolving fuzzy neural classifier, it acts in an incremental single-pass manner, not using any past samples, which makes it extremely fast, as only fuzzy neurons attached to a new sample need to be checked for the degree of their specificity change. To prove the technique’s efficiency, tests with binary classification streams commonly used by the machine learning community were conducted for evaluation purposes. The number of supervised samples for model updates could be significantly reduced with a low or even negligible decrease in the classification accuracy trends, while a random selection of samples (with the same percentages as selected by our oAL approach) showed large performance downtrends. Furthermore, a very similar number of rule evolution trends could be observed with different percentages of selected samples, which indicates good robustness of our method with respect to knowledge extraction (as non-changing).

This paper proposes to use an artificial intelligence (AI) model based on neuro-fuzzy techniques to aid in the automatic evaluation of notes originated from the student's interaction with their performed activities in online distance learning (ODL) forums. The evolution of non-classroom teaching allows new business opportunities and studies to emerge for the population. Some people who do not have enough time to attend traditional teaching choose distance learning to boost their tasks and money. The increasing demand for ODL courses creates challenges that are mainly aimed at automating the tasks commonly required for students' evaluation routines during their activities. For distance learning to maintain an acceptable level of costs, routine activities must be automated to reduce values related to more straightforward operations execution. In this paper, we will use a real dataset on evaluations of frequent interactions of academic in forums, allowing the obtained data to be submitted to a fuzzy neural network able to estimate the value of the student's score value according to the activities carried out by them, beyond the extraction of knowledge through fuzzy rules. Model outputs confirm that the approach may be feasible to automate the presence and participation process in ODL forums through a specialist system based on fuzzy rules. The tests performed with a resulting low RMSE of 1.37 suggest that our neuro-fuzzy-based AI approach performs better than traditional state-of-the-art regressive models.

This paper proposes a hybrid architecture based on neural networks, fuzzy systems, and n-uninorms for solving pattern classification problems, termed as ENFS-Uni0 (short for evolving neuro-fuzzy system based on uni-nullneurons). The model can produce knowledge in an on-line (single-pass) and evolving learning context in a particular form of neuro-fuzzy rules representing the dependencies among input features through IF-THEN type relations. The rules antecedents are thereby realized through uni-nullneurons, which are constructed from n-uninorms, leading to the possibility to express both, AND- and OR-connections (and a mixture of these) among the single antecedent parts of a rule (and thus achieving an advanced interpretability aspect of the rules). The neurons’ evolution is done through an extended version of an autonomous data partition method (ADPA). On-line interpretation of the timely evolution of rules is addressed by (i) a concept for tracking the degree of changes of the rules over data stream samples, which may indicate experts/operators how much dynamics is in the process and may be used as a structural active learning component to request operator’s feedback in the case of significant changes and (ii) a concept for updating feature weights incrementally. These weights express the (possibly changing) impact degrees of features on the classification problem: features with low weights can be seen as unimportant and masked out when showing rules to an expert (→ rule length reduction). The rules’ consequents are represented by certainty vectors and are recursively updated by an indicator-based recursive weighted least squares (I-RWLS) approach (one RWLS estimator per class) where the weights are given through the neuron activation levels in order to gain stable local learning. The model proposed in this paper was successfully compared to related hybrid and evolving approaches in the literature for classifying binary and multi-class patterns. The results obtained by the proposed model show an outperformance of the related works in terms of higher accuracy trend lines over time, while offering a high degree of interpretability through coherent neuro-fuzzy rules to solve the classification problems.

This paper proposes a Bayesian hybrid approach based on neural networks and fuzzy systems to construct fuzzy rules to assist experts in detecting features and relations regarding the presence of autism in human beings. The model proposed in this paper works with a database generated through mobile devices that deals with diagnoses of autistic characteristics in human beings who answer a series of questions in a mobile application. The Bayesian model works with the construction of Gaussian fuzzy neurons in the first and logical neurons in the second layer of the model to form a fuzzy inference system connected to an artificial neural network that activates a robust output neuron. The new fuzzy neural network model was compared with traditional state-of-the-art machine learning models based on high-dimensional based on real-world data sets comprising the autism occurrence in children, adults, and adolescents. The results (97.73- Children/94.32-Adolescent/97.28-Adult) demonstrate the efficiency of our new method in determining children, adolescents, and adults with autistic traits (being among the top performers among all ML models tested), can generate knowledge about the dataset through fuzzy rules.

Power-quality disturbances lead to several drawbacks such as limitation of the production capacity, increased line and equipment currents, and consequent ohmic losses; higher operating temperatures, premature faults, reduction of life expectancy of machines, malfunction of equipment, and unplanned outages. Real-time detection and classification of disturbances are deemed essential to industry standards. We propose an Evolving Gaussian Fuzzy Classification (EGFC) framework for semi-supervised disturbance detection and classification combined with a hybrid Hodrick-Prescott and Discrete-Fourier-Transform attribute-extraction method applied over a landmark window of voltage waveforms. Disturbances such as spikes, notching, harmonics, and oscillatory transient are considered. Different from other monitoring systems, which require offline training of models based on a limited amount of data and occurrences, the proposed online data-stream-based EGFC method is able to learn disturbance patterns autonomously from never-ending data streams by adapting the parameters and structure of a fuzzy rule base on the fly. Moreover, the fuzzy model obtained is linguistically interpretable, which improves model acceptability. We show encouraging classification results.

Cancer treatment is extremely aggressive and, in addition to causing considerable discomfort, can lead to death. Therefore, identifying aspects related to treatment assertiveness may be efficient for reducing the mortality rate of cancer patients. This paper seeks to identify the prognosis of cancer treatment survival through hybrid techniques based on the autonomous fuzzification process and artificial neural networks. The public dataset on cancer mortality is the source for conducting treatment assertiveness rating tests. The hybrid model had its results compared to other models present in the pattern classification literature with superior accuracy and identification of people likely to survive treatment (90.46%), and the fuzzy rules obtained with the execution of the model corroborate the high assertiveness of the model, even surpassing state of the art for the theme.

This paper proposes the use of intelligent hybrid systems based on the concepts of artificial neural networks and fuzzy systems to assist in the identification of children who have a problem that may impair their motor or cognitive development. Research on children seeking to identify diseases that are listed in the International Classification of Functioning, Disability, and Health (ICF-CY) are the targets of this research. To assist in the disease identification studies, a database was provided for researchers from all over the world to develop techniques to aid in the creation of specialist systems based on fuzzy rules to assist in the detection of healthy children with psychological or motor dysfunction. This paper used a smart model capable of generating fuzzy rules to construct a predictor model that helps to diagnose some of these problems in children or adolescents. The results obtained were promising, obtaining better accuracy indexes than the initial studies, confirming that the approach is feasible to identify children with ICF-CY.

Smart models are responsible for solving complex problems within the routines of people and companies. The complexity of their structures can be defined by the methodologies used in the definition of parameters or architecture of their models. In three-layer fuzzy neural networks, the number of neurons is determined by fuzzification techniques capable of creating elements based on the data submitted to the training of the intelligent structures. The choice of the fuzzification method can make the model architecture complex and with many parameters. To create a more compact fuzzy neural network architecture, this paper presents a new fuzzy approach for fuzzy neural networks based on Bayesian clustering. This technique expands the training capabilities of the fuzzy c-means algorithm by adding to its methods the nature of the particle filter inference technique to estimate the model parameters including the number of clusters, making the approach more straightforward and less dependent on settings for the construction of the neural network fuzzy. The other layers of the model use fuzzy logical neurons capable of creating IF / THEN rules and use the extreme learning machine to update parameters. The proposed new model was submitted to linear and nonlinear regression databases to confirm that the proposed model can act as a universal approximation of functions. The results presented were promising about the ability to identify patterns within bases in complex problems.

Artificial hydrocarbon networks (AHN) is a supervised machine learning method inspired on chemical carbon networks that simulate heuristic chemical rules involved within organic molecules to represent the structure and behavior of data. However, training AHN depends on a relevant number of parameters. In that sense, the original training algorithm presents some issues to find suitable parameters in a reasonable amount of time. Thus, this paper proposes a new training algorithm for AHN based on the concept of extreme learning machines, to update weight parameters related to the molecular functions. To evaluate the effectiveness of the proposed algorithm, binary classification and regression tests are performed over real public datasets from a central data repository specialized in machine learning problems. The results obtained validated that the updating of the weight parameters using the new training algorithm in the molecular structures is efficient and maintains the expected results of model accuracy. In addition, this work increased up to 24.88% the speed of the training phase in contrast to the original algorithm.

Mobile systems were built to aid in the prediction of children with autism traits. This type of system uses artificial intelligence capabilities and machine learning techniques to assign probabilities to people who undergo the test in the application. According to the information provided by the authors of the mobile application, it is intended to use fuzzy neural networks to aid in prediction whether or not the person has traits of autism. Therefore, this paper proposes the insertion of an interpretive technique based on an extreme learning machine to deal with questions provided by users seeking to obtain more immediate responses, based on binary classification labels. The tests performed with the base achieved high levels of accuracy for the proposed model and base, making it a viable alternative for the efficient prediction of children with autism.