International Journal of Uncertainty Fuzziness and Knowledge-Based Systems

Liu regression, originally developed in the context of classical (crisp) statistics as a biased estimation method to mitigate multicollinearity, has not been previously extended to fuzzy regression frameworks. In this study, we propose a novel fuzzy adaptation of Liu regression, implemented within an [Formula: see text]-cut-based estimation structure. This approach provides a systematic methodolo…

Globally, e-commerce and social media sites are rapidly improving every day. Social media is a dynamic platform where people can share their thoughts and feelings. Sentiment analysis plays a crucial role in understanding public sentiment. The existing method has some drawbacks, including difficulties in emotion detection and a small amount of data used for analysis. However, for cross-domain sent…

This study presents a comprehensive machine learning framework that integrates advanced ensemble classifiers with bio-inspired optimization to perform high-accuracy classification of multivariate structured data. The proposed approach is applied to classify countries based on health, demographic, and economic indicators. Specifically, three state-of-the-art boosting models, Extreme Gradient Boost…

This article explores the increasing need for home health care services (HHCS) in developed nations due to rising life expectancy and changing family structures. To this end, we introduce a sophisticated two-stage stochastic programming model designed for the HHCS. The main objective is to efficiently assign caregivers to patients and optimize their travel routes, a complex task made more challen…

Cryptocurrency resembles the eminent key elements in the progress of financial and economic domains, which enables safe and secure money transactions. However, these currencies are not preferred as an investment prospect because of the price volatility and the unpredictable market price. However, the existing techniques fail to learn the uncertainty and fluctuations in their prices limiting their…

Uncertainty widely exists in standby redundancy system designing, and it is of great significance for decision makers in acquiring the system with less cost but longer lifetime. This paper explicitly integrates the uncertainties from the lifetime and cost of the units and the resource limitations in cost and weight, in an expected lifetime-maximization standby redundancy system optimization probl…

In the application of interval type-2 (IT2) Mamdani fuzzy logic systems (FLSs), centroid type-reduction is a commonly used reduction method. This paper introduces two new algorithms, the Binary algorithm and its extended version, the Extended-Binary algorithm, to address the centroid reduction problem in IT2 Mamdani systems. These two algorithms are compared with the EKM algorithm, with a focus o…

Frequent itemset mining is a key task in data mining, particularly for analyzing customer purchase behavior in large-scale transaction datasets. This paper proposes a novel method called SPGAN-ECG-FISM-LSDA—Semantic Preserved Generative Adversarial Network-driven Enhanced Candidate Generation for Efficient and Scalable Frequent Itemset Mining. The approach begins by converting transaction data in…

This study introduces a new clustering method based on the multivariate fuzzy K-modes algorithm. The proposed algorithm incorporates attribute weights determined by Gini impurity, which evaluates the significance of attribute values in both within-cluster and between-cluster variances. Additionally, instead of relying on the Hamming distance, the probabilistic distance is employed to compute the …

Autonomous driving systems must be capable of making quick decisions based on the perceived environment and specific driving conditions. Perception models in these systems perform well in detecting objects under favorable conditions but their performance deteriorates in poor visibility or with partly occluded objects. To reduce risks from undetected objects, autonomous vehicles must incorporate a…

This article introduces a rapid and stable actor-critic learning-based Takagi–Sugeno–Kang type-2 fuzzy controller (AL-TFC) designed for nonlinear and uncertain applications. The critic-based adaptive tuning network of the AL-TFC employs the backpropagation of the error method, utilizing a neural network to implement the critic network. Parameter updates are performed using Lyapunov criteria to mi…

In survey sampling, the traditional estimators frequently rely on the assumptions regarding the accuracy and certainty of the data, which may not always hold in practical applications, potentially leading to errors or missing information. In contrast, the neutrosophic estimators extend beyond the conventional binary framework of true-false by introducing a third component, ‘indeterminacy’. This a…

In clustering, the measures used to quantify similarity between observations and clusters vary across methods. Density-based methods are well-suited for chained data, while partition-based methods perform better for elliptical-shaped clusters of relatively equal sizes. Identifying natural clusters in data with complex structures, such as weakly connected clusters of varying sizes, remains a signi…

Support vector classification (SVC) and support vectors regression (SVR) are learning machines that have excellent generalization performance. The data which used by classical statistical learning theory is assumed precise. However, the data from real world sometimes low-quality or imprecise, the uncertainty theory and uncertain statistics are appropriate methods to process the imprecise observat…

Nowadays, with the progress in the different fields of science and technology, the massive number of information is easily available in the internet. It helps users to gather abundant information quickly based on their requirements however this caused severe barriers in reading, and difficulties in accessing usable information. Thus, different summarization techniques are generally followed for t…

This work uses a novel Cubic B-spline approach to analyse the influence of different fuzzy initial conditions on the solution of the hybrid fuzzy differential equation using the strongly generalized differentiability concept. It explores the solution of this fuzzy differential equation with triangular and trapezoidal fuzzy numbers with triangular-shaped initial conditions and assesses the impact …

Distance measures for Intuitionistic Fuzzy Sets (IFSs) have been extensively studied from a theoretical perspective and successfully applied in various fields, such as pattern recognition, medical diagnosis, and multi-criteria decision-making. However, the literature highlights several shortcomings in these intuitionistic fuzzy distance measures. This paper proposes a novel distance measure for I…

We address a multi-class classification problem on a tabular dataset comprising both continuous and categorical features, as commonly found in financial and actuarial domains. To achieve robust and reliable predictions, we propose a credal classifier built from an ensemble of augmented probabilistic models, each capable of self-assessing its confidence in the predicted class distribution for a gi…

Due to the widespread increase of online reviews, sentiment classification is now a fascinating study in both academic and industrial research. Annotated training data is difficult to obtain, so reviews help in many domains. Numerous methods have been created for sentiment classification, but the process of retrieving information is not exact, less effective, and has a slower rate of convergence.…

The Poisson regression model is widely used in statistical data modeling in diverse fields, such as epidemiology, economics, engineering, and sports. Its popularity stems from its ability to effectively describe the relationship between a statistical response variable and a set of explanatory variables. However, multicollinearity among predictors and outliers in the data can severely affect the r…