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The architecture of AND/OR fuzzy neural networks exhibits outstanding learning abilities and significant interpretation capabilities. However, AND/OR networks suffer from structure-related problems namely low efficiency and slow convergence of learning due to several reasons such as high dimensionality and gradient-based learning algorithms which lead to a visible computing overhead. In this paper, we present a two-phase fuzzy logic-oriented network design that is composed of AND/OR neurons. This design takes advantages of Randomized Neural Network (RNN) to achieve higher convergence while exhibiting good nonlinear approximation capabilities. A gradient–based learning algorithm is implemented in the second phase of the design to further reduce values of performance index. The quality of the proposed design and resulting architecture is quantified through the use of numeric data along with fuzzy sets (information granules). Experimental results meet the research's objectives and the proposed design methodology opens up new future directions for proceeding with more improvements. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently, numerous scholars actively explore improvements to algorithmic structures, operators, search patterns, etc., to enhance their optimization performance. Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years. This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). We begin with the conceptual outlines of reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. The RL-assisted procedure is divided according to the implemented functions including solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Additionally, different attribute settings of RL in RL-EA are discussed. In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets to facilitate a quick comparative study. Finally, we analyze potential directions for future research. This survey serves as a rich resource for researchers interested in RL-EA as it overviews the current state-of-the-art and highlights the associated challenges. By leveraging this survey, readers can swiftly gain insights into RL-EA to develop efficient algorithms, thereby fostering further advancements in this emerging field. [ABSTRACT FROM AUTHOR]

The ongoing growth of the Internet of Things and machine learning technology have provided increased motivation for the development of smart healthcare. In this study, a disease diagnosis system is proposed for remote identification and early prediction in smart healthcare environments. The originality of this study resides in the innovative implementation of ensuing modules to improve diagnostic accuracy of the system. First, fuzzy clustering based on the forest optimization algorithm is employed to detect outliers and a self-organizing fuzzy logic classifier is applied to supplement missing data in electronic medical records (EMRs). A feature selection technique using the battle royale optimization algorithm is then developed to remove redundant information and identify optimal EMR features. The refined and fused data are further classified using an eigenvalue-based machine learning algorithm to determine whether a patient exhibits a certain disease. Simulation experiments are conducted with widely used heart disease and diabetes datasets to evaluate the performance of the proposed system, using accuracy, precision, recall, and F-measure as evaluation metrics. • A diagnostic system is proposed for early disease prediction in smart healthcare. • Fuzzy clustering is applied to remove outliers from electronic medical records. • A self-organizing fuzzy logic classifier is developed to supplement missing data. • A feature selection scheme is included to identify optimal features from the data. • Eigenvalue classification is used to ascertain whether a patient exhibits a disease. [ABSTRACT FROM AUTHOR]

This study is devoted to the generalization of information granules by forming higher order, namely, order-2 information granules. Information granules are semantically meaningful entities, which play a central role in knowledge representation and system modeling in the framework of Granular Computing. The encountered information granules could exhibit significant heterogeneity because of the diversified formal formalisms. To facilitate an effective generalization of heterogeneous granular data when using clustering algorithms, an efficient scheme has been proposed to form a unified representation of various types of granular data by using Possibility–necessity measures. Once the clustering process has been completed in the possibility–necessity feature space, the higher order information granules come as results of decoding by involving the possibility–necessity metrics and fuzzy relational calculus. The extent to which the higher order information granules are supported by the granular data present at a lower level of hierarchy is quantified in terms of the membership degrees obtained in the clustering process. Experimental studies concerning a series of publicly available datasets coming from UCI and KEEL machine learning repositories are carried out in this study. • A novel encoding/decoding method for representing heterogeneous granular data. • Clustering algorithm can be applied to cluster encoded heterogeneous granular data. • Supported by fuzzy relational calculus theory and Possibility-necessity measures. [ABSTRACT FROM AUTHOR]

In recent years, classical fuzzy clustering-based neural networks (FCNNs) have been successfully applied to regression tasks. The determination of the parameters such as cluster centers of the existing hard c-means (HCM) or fuzzy c-means (FCM), leads to the performance deterioration of the model because of the sensitivity of HCM or FCM to noise and outliers. Moreover, there are also several factors for over-fitting and degradation of the robustness of the ensuing model. To solve such problems, two improved clustering techniques and L 2 norm-regularization are considered in the proposed robust fuzzy clustering-based neural networks (RFCNNs) modeling. SVs-based hard c-means (SVs-based HCM) and SVs-based fuzzy c-means (SVs-based FCM) designed with support vectors (SVs) can reduce the interference of uncorrelated data, including noise and outliers, thereby enhancing the main data characteristics effectively, as well as leading to the construction on the improved network model. L 2 norm-regularization can be used to alleviate the degradation of robustness caused by overfitting. In terms of improving the performance of the model through SVs-based HCM or SVs-based FCM, as well as robustness completed through L 2 norm-regularization, the superiority of RFCNNs was verified by experimenting with synthetic data and publicly available data from machine learning datasets. [ABSTRACT FROM AUTHOR]

• Evaluating different models' perspectives for real-time adversarial attack detection in VANETs. • Identifying and preprocessing DSRC adversarial data using standard and fusion preprocessing approaches. • Selection for the golden ML model using a fuzzy decision-making framework for VANETs. • Evaluating the efficiency of the developed framework in four assessment processes. This paper addresses various issues in the literature concerning adversarial attack detection in Vehicular Ad-hoc Networks (VANETs). These issues include the failure to consider both normal and adversarial attack perspectives simultaneously in Machine Learning (ML) model development, the lack of diversity preprocessing techniques for VANETs communication datasets, the inadequate selection guidelines for real-time adversarial attack detection models, and the limited emphasis on explainability in adversarial attack detection. In this study, we propose an original fuzzy decision-making framework that incorporates multiple fusion standpoints. Our framework aims to evaluate multi-ML models for real-time adversarial attack detection in VANETs, focusing on three stages. The first stage involves identifying and preprocessing Dedicated Short-Range Communication (DSRC) data using standard and fusion preprocessing approaches. Two communication scenarios, normal and jammed, are considered, resulting in two DSRC datasets. In the second stage, we develop multi-ML models based on the DSRC datasets using standard preprocessing and feature fusion preprocessing for dataset-1 and dataset-2, respectively. The third stage evaluates the multi-ML models using a fuzzy decision-making approach based on the Fuzzy Decision by Opinion Score Method (FDOSM) and an adversarial attack decision fusion matrix. The External Fusion Decision (EFD) settings of the FDOSM address individual ranking variance, provide a unique rank and select the best model. Experimental results demonstrate that the K-Nearest Neighbors Algorithm (kNN) model achieves the highest explain score of 0.2048 in dataset-1 using standard preprocessing, while the Random Forest (RF) model applied to dataset-2 using fusion preprocessing emerges as the most robust and golden model against adversarial attacks, with a score of 0.1819. This finding suggests that the fusion preprocessing approach using Principal Component Analysis (PCA) is more suitable for addressing normal and adversarial attack perspectives. Furthermore, our fuzzy framework undergoes evaluation in terms of systematic rank, sensitivity analysis, explainability analysis, and comparison analysis. Overall, this framework provides valuable insights for researchers and practitioners in VANETs, informing the execution, selection, and interpretation of multi-ML models to tackle adversarial attack detection problems effectively. The new fuzzy framework demonstrates that multi-ML models based on feature fusion preprocessing are more effective. [ABSTRACT FROM AUTHOR]

Traffic classification groups similar or related traffic data, which is one main stream technique of data fusion in the field of network management and security. With the rapid growth of network users and the emergence of new networking services, network traffic classification has attracted increasing attention. Many new traffic classification techniques have been developed and widely applied. However, the existing literature lacks a thorough survey to summarize, compare and analyze the recent advances of network traffic classification in order to deliver a holistic perspective. This paper carefully reviews existing network traffic classification methods from a new and comprehensive perspective by classifying them into five categories based on representative classification features, i.e., statistics-based classification, correlation-based classification, behavior-based classification, payload-based classification, and port-based classification. A series of criteria are proposed for the purpose of evaluating the performance of existing traffic classification methods. For each specified category, we analyze and discuss the details, advantages and disadvantages of its existing methods, and also present the traffic features commonly used. Summaries of investigation are offered for providing a holistic and specialized view on the state-of-art. For convenience, we also cover a discussion on the mostly used datasets and the traffic features adopted for traffic classification in the review. At the end, we identify a list of open issues and future directions in this research field. • Comprehensive evaluation criteria to assess classification methods. • A thorough review on traffic classification methods. • A discussion on advantages and disadvantages of classification methods. • An analysis on classification granularity by dividing it into four levels. • Exploration of open issues and future research directions. [ABSTRACT FROM AUTHOR]

This study presents a novel dynamic localisation-based decision (DLBD) with fuzzy weighting with zero inconsistency (FWZIC) under a probabilistic single-valued neutrosophic hesitant fuzzy set (PSVNHFS) environment to benchmark Hybrid Multi Deep Transfer and Machine Learning (HMDTML) models. The novel DLBD method is proposed to generate a dynamic localisation decision matrix based on the upper and lower boundaries and the length of the scale. The superiority of DLBD derives from its ability to manage dynamic changes with boundary value consequences. In addition, the utilization of PSVNHFS in conjunction with DLBD and FWZIC has proven to effectively address the challenges posed by vagueness, uncertainty and hesitancy in the benchmarking procedure. The proposed methodology consists of three primary three steps: i) the adaptation of 48 HMDTML models, including 4 deep transfer learning models and 12 machine learning models trained on a dataset of 936 chest X-ray images obtained from both COVID-19 patients and individuals without the disease. Then, these models were evaluated based on seven evaluation criteria, and a decision matrix was proposed. ii) The development of a PSVNH–FWZIC to assign weights to the evaluation criteria. iii) The formulation of a PSVNH–DLBD for the purpose of benchmarking HMDTML models. Results of the PSVNH–FWZIC revealed that AUC and time were the most important evaluation criteria, while precision was the least important. Furthermore, the results from PSVNH–DLBD, reveal that Model M24 (Painters-Decision Tree) earned the highest rank when λ = 2 , 3 , 4 , 5 a n d 6 , followed by Model M25 (SqueezeNet-AdaBoost) and Model M34 (DeepLoc-kNN), while Model M39 (DeepLoc-SVM) had the lowest rank (rank = 48) across all λ values. The proposed method underwent sensitivity and comparison analyses to confirm its reliability and robustness. [ABSTRACT FROM AUTHOR]

The growth potential of a career reflects its future prospects and is an important consideration for individuals and organizations when career planning. There is still a lack of quantitative assessment tools for growth potential of careers. In this study, considering the key role of human capital in human resource management, as well as the excellent performance of complex network and machine learning in big data analysis and prediction, a career growth potential assessment model with human capital ensemble is proposed through human capital-based occupational mobility network and ensemble learning. First, an occupational mobility network is constructed based on online professional dataset to associate occupations with each other. Then, five dimensions of human capital measurements are designed to quantify human capital in terms of education, experience, social capital, occupational size, and concentration. These are then combined with the occupational mobility network to create a new network that depicts human capital flows among occupations. Finally, an ensemble framework for assessing career growth potential is constructed to integrate multidimensional human capital information in the network and obtain quantitative scores of growth potential. This study is the original attempt to adopt a data-driven idea and an intelligent approach to understand career growth potential. The experimental results show that it also makes a useful exploration for modeling human capital flows and intelligent assessment of career prospects. [ABSTRACT FROM AUTHOR]

This article develops a multi-view multi-label learning for incomplete data which are ubiquitous with the usage of three kinds of representations including within-view representation, cross-view representation, and consensus-view representation. Different from the recent learning machines, the proposed learning machine takes the feature-oriented information, label-oriented information, and associated information between features and labels in multiple representations together and exploits the hidden useful information of available instances with the usage of instance–instance correlations, feature–feature correlations, label-label correlations, and feature–label correlations. The developed learning machine is named as within- cross- consensus-view representation-based multi-view multi-label learning with incomplete data (WCC-MVML-ID). Extensive experiments on multiple multi-view and multi-label data sets with incomplete data validate the effectiveness of WCC-MVML-ID and it can be concluded that (1) WCC-MVML-ID outperforms other compared learning machines and its performances are more stable even though the missing rates of features and labels being larger; (2) compared with within-view information and consensus-view information, cross-view information is more useful for the processing problem about incomplete data; (3) WCC-MVML-ID can converge within 45 iterations. [ABSTRACT FROM AUTHOR]

Estimating interest rates is a typical multivariate prediction problem that has garnered considerable attention in the finance industry. However, the rising complexity of the prediction environment has presented significant obstacles to the methodology and application of traditional single-scale rational prediction models. Considering this challenge, the paper presents a new multivariate fusion prediction system that aims to tackle the following issues: (1) How to mine prediction information from multi-scale information systems (MSISs)? (2) How to describe the irrational behavior of decision-makers (DMs) in prediction processes? (3) How to improve the prediction performance and generalization capability of the models? Specifically, the score function of each feature under MSISs is primarily established by applying scale rules, regret theory (RT), and dominance relations to thoroughly explore prediction information across varying scales. Second, an adaptive S3WD model is developed for feature selection, which leverages the feature score distribution and the sequential three-way decision (S3WD) concept to avoid the problem of dimensional disasters, thereby improving the accuracy of selected features and reducing the learning cost. Furthermore, to enhance the effectiveness and rationality of the kernel extreme learning machine (KELM), an improved version known as IKELM is developed, which combines the regret-based comprehensive score of each sample. Finally, a multivariate fusion prediction system is constructed and abbreviated as an S3WD-IKELM-PSO system, where the particle swarm optimization (PSO) algorithm is employed to determine optimal parameters. It is worth noting that actual interest rate estimation datasets are utilized to verify the feasibility and validity of the proposed prediction system. According to the experimental results, the S3WD-IKELM-PSO system exhibits superior prediction performance and generalization ability compared to existing machine learning models. • A regret theory-based multivariate prediction system in MSISs is explored. • A regret-based feature evaluation system in an MSIS is designed. • An adaptive S3WD model for feature selection is proposed. • An improved KELM model is proposed. [ABSTRACT FROM AUTHOR]

This study focuses on two critical design aspects of a progressive fuzzy polynomial neural network (PFPNN): the influence of the gated recurrent unit (GRU) structure and the implementation of fitness-based candidate neuron selection (FCNS) through two probabilistic strategies. The primary objectives are to enhance modeling accuracy and to reduce the computational load associated with nonlinear regression tasks. Compared with the existing fuzzy rule-based modeling architecture, the proposed dynamic model consists of the GRU structure and the hybrid fuzzy polynomial architecture. In the initial two layers of the PFPNN, we introduce three types of polynomial and fuzzy rules into the GRU neurons (GNs) and fuzzy polynomial neurons (FPNs), which can effectively reveal potential complex relationships in the data space. The synergy of the FCNS strategies and the ℓ 2 regularization learning method is to design a progressive regression model adept at melding the GRU structure with a self-organizing architecture. The proposed GRU structure and polynomial-based neurons significantly improve the modeling accuracy for time-series datasets. The rational utilization of FCNS strategies can reinforce the network structure and discover the potential performance of neurons of the network. Furthermore, the inclusion of ℓ 2 norm regularization provides additional stability to the proposed model and mitigates the overfitting issue commonly encountered in many existing learning methods. We validated the proposed neural networks using six time-series, four machine learning, and two real-world datasets. The PFPNN outperformed other models in the comparison studies in 83.3% of the datasets, emphasizing its superiority in terms of developing a stable deep structure from diverse candidate neurons and reducing computational overhead. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, we pay widespread attention to the high reliability and interpretability of resulting architectures and algorithms. As one of the main methods of machine learning, the "black-box" mechanism of neural networks lacks interpretability. In order to alleviate the low interpretability, based on belief rule-based inference methodology using the evidential reasoning approach (RIMER), we propose a convolutional rule inference network (CRIN), which is interpretable by involving rules in belief rule base (BRB) under various types of uncertain knowledge representation. Considering the influence of global data distribution on attribute weights, the determination of each attribute may be affected by all other attributes, based on the sigmoid activation function, we determine optimized attribute weights to make sure that each attribute weight is formed by all attribute values. The derivation functions with optimized attribute weights based on evidential reasoning theory also showed to ensure the feasibility of the following learning algorithm. For expressing the influence of local rules input on final output in the network, the network framework and learning algorithm of CRIN based on convolution strategy are proposed. The inference process in RIMER is used as the feedforward mechanism to ensure the interpretability of the network. Meanwhile, the gradient descent method is used as back propagation algorithm to adjust the parameters of rule base to establish a more reasonable BRB. The experimental results and comparative analysis demonstrate that the proposed CRIN has advantages in terms of interpretability and learning capability. This study suggests new trends for interpretable network and uncertain classification issues in the reasoning approach. [ABSTRACT FROM AUTHOR]