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In this paper, we propose a new method to deal with uncertain data in the context of Common Cause Failure (CCF) analysis. Uncertain CCF data refer to the data for which the number of components involved in the failure events is not exactly known. We introduce a new formalism to describe uncertain CCF data to avoid subjective probabilities for the number of failed components in each CCF event that are used in classical methods such as the impact vector method. The parameters of the α -factor model are estimated using the maximum likelihood method relying on properties of the nested Dirichlet distribution and grouped Dirichlet distribution. A data augmentation technique with an expectation-maximization algorithm is also developed for some schemes of data with uncertainty. Finally, we evaluate the performance of the proposed method through numerical simulations and illustrate its application using an example from the literature. [ABSTRACT FROM AUTHOR]

Uncertainty of data, the degree to which data are inaccurate, imprecise, untrusted, and undetermined, is inherent in many contemporary database applications, and numerous research endeavours have been devoted to efficiently answer skyline queries over uncertain data. The literature discussed two different methods that could be used to handle the data uncertainty in which objects having continuous range values. The first method employs a probability-based approach, while the second assumes that the uncertain values are represented by their median values. Nevertheless, neither of these methods seem to be suitable for the modern high-dimensional uncertain databases due to the following reasons. The first method requires an intensive probability calculations while the second is impractical. Therefore, this work introduces an index, non-probability framework named Constrained Skyline Query processing on Uncertain Data (CSQUiD) aiming at reducing the computational time in processing constrained skyline queries over uncertain high-dimensional data. Given a collection of objects with uncertain data, the CSQUiD framework constructs the minimum bounding rectangles (MBRs) by employing the X-tree indexing structure. Instead of scanning the whole collection of objects, only objects within the dominant MBRs are analyzed in determining the final skylines. In addition, CSQUiD makes use of the Fuzzification approach where the exact value of each continuous range value of those dominant MBRs' objects is identified. The proposed CSQUiD framework is validated using real and synthetic data sets through extensive experimentations. Based on the performance analysis conducted, by varying the sizes of the constrained query, the CSQUiD framework outperformed the most recent methods (CIS algorithm and SkyQUD-T framework) with an average improvement of 44.07% and 57.15% with regards to the number of pairwise comparisons, while the average improvement of CPU processing time over CIS and SkyQUD-T stood at 27.17% and 18.62%, respectively. [ABSTRACT FROM AUTHOR]

The digitalization of the global landscape of electricity consumption, combined with the impact of the pandemic and the implementation of lockdown measures, has required the development of a precise forecast of energy consumption to optimize the management of energy resources, particularly in pandemic contexts. To address this, this research introduces a novel forecasting model, the robust multivariate multilayered long- and short-term memory model with knowledge injection ( Robust - mv - M - LSTM - CI ), to improve the accuracy of forecasting models under uncertain conditions. This innovative model extends the capabilities of mv - M - LSTM - CI by incorporating an additional branch to extract energy consumption from adversarial noise. The experiment results show that Robust - mv - M - LSTM - CI demonstrates substantial improvements over mv - M - LSTM - CI and other models with adversarial training: multivariate multilayered long short-term memory (adv-M-LSTM), long short-term memory (adv-LSTM), bidirectional long short-term memory (adv-Bi-LSTM), and linear regression (adv-LR). The maximum noise level from the adversarial examples is 0.005. On average, across three datasets, the proposed model improves about 24.01% in mean percentage absolute error (MPAE), 18.43% in normalized root mean square error (NRMSE), and 8.53% in R 2 over mv - M - LSTM - CI . In addition, the proposed model outperforms "adv-" models with MPAE improvements ranging from 35.74% to 89.80% across the datasets. In terms of NRMSE, improvements range from 36.76% to 80.00%. Furthermore, Robust - mv - M - LSTM - CI achieves remarkable improvements in the R 2 score, ranging from 17.35% to 119.63%. The results indicate that the proposed model enhances overall accuracy while effectively mitigating the potential reduction in accuracy often associated with adversarial training models. By incorporating adversarial noise and COVID-19 case data, the proposed model demonstrates improved accuracy and robustness in forecasting energy consumption under uncertain conditions. This enhanced predictive capability will enable energy managers and policymakers to better anticipate and respond to fluctuations in energy demand during pandemics, ensuring more resilient and efficient energy systems. [ABSTRACT FROM AUTHOR]

As an information granulation technology, clustering plays a pivotal role in unsupervised learning, serving as a fundamental cornerstone for various data mining techniques. The effective and accurate classification of data is a central focus for numerous researchers. For a dataset, we assert that the classification performance of a clustering method is significantly influenced by uncertain data, particularly those situated at the cluster boundaries. It is evident that uncertain data encapsulate richer information compared with others. Generally, the greater the uncertainty, the more information the data holds. Therefore, conducting a comprehensive analysis of this particular subset of data carries substantial significance. This study presents an approach to characterize data distribution properties using fuzzy clustering and defines the boundary and non-boundary characteristics (certainty and uncertainty) of the data. To improve the classification performance, the strategy focuses on reducing the uncertainty associated with boundary data. The proposed scheme involves inserting data points with the cloud computing technology based on the distribution characteristics of the membership functions to diminish the uncertainty of uncertain data. Building upon this, the contribution of boundary data is reassigned to the prototype in order to diminish the proportion of uncertain data. Subsequently, the classifier is optimized through data label (classification error) supervision. Ultimately, the objective is to leverage clustering algorithms for classification, thereby enhancing overall classification accuracy. Experimental results substantiate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Uncertainty of data, the degree to which data are inaccurate, imprecise, untrusted, and undetermined, is inherent in many contemporary database applications, and numerous research endeavours have been devoted to efficiently answer skyline queries over uncertain data. The literature discussed two different methods that could be used to handle the data uncertainty in which objects having continuous range values. The first method employs a probability-based approach, while the second assumes that the uncertain values are represented by their median values. Nevertheless, neither of these methods seem to be suitable for the modern high-dimensional uncertain databases due to the following reasons. The first method requires an intensive probability calculations while the second is impractical. Therefore, this work introduces an index, non-probability framework named Constrained Skyline Query processing on Uncertain Data (CSQUiD) aiming at reducing the computational time in processing constrained skyline queries over uncertain high-dimensional data. Given a collection of objects with uncertain data, the CSQUiD framework constructs the minimum bounding rectangles (MBRs) by employing the X-tree indexing structure. Instead of scanning the whole collection of objects, only objects within the dominant MBRs are analyzed in determining the final skylines. In addition, CSQUiD makes use of the Fuzzification approach where the exact value of each continuous range value of those dominant MBRs’ objects is identified. The proposed CSQUiD framework is validated using real and synthetic data sets through extensive experimentations. Based on the performance analysis conducted, by varying the sizes of the constrained query, the CSQUiD framework outperformed the most recent methods (CIS algorithm and SkyQUD-T framework) with an average improvement of 44.07% and 57.15% with regards to the number of pairwise comparisons, while the average improvement of CPU processing time over CIS and SkyQUD-T stood at 27.17% and 18.62%, respectively.

The proliferation of high-dimensional data in many advanced database applications is a result of today’s technological advancements. These data points that correspond to objects are often without a precise description, which make their representation uncertain. While the concept of data streaming is not new, its practical uses are only recently emerging. This research focuses on continuous range data—a type of uncertain data common in database applications—that do not have explicit representations of their exact values. Furthermore, the identification of skyline objects—one of the popular database applications—becomes more challenging when skylines are to be identified from a collection of continuously generated input data streams where objects might have imprecise values. This makes it imperative to determine which approach has the optimal accuracy for estimating or predicting the uncertain values and at the same time able to handle a massive streams of data that are continuously generated and analyze them almost instantly to provide accurate and timely responses. Given this, the following techniques are selected—Linear Regression (LR), k-Nearest Neighbour (k-NN), Random Forest (RF), Decision Trees (DT), and Centre and Range Method (CRM) and their effectiveness is evaluated in terms of execution time, precision, recall, F1-score, and root mean square error (RMSE). Additionally, in order to verify the accuracy of each prediction technique, the predicted data derived from its model is used to derive skyline objects, which are subsequently compared to the actual skyline results. An inaccurate prediction of a continuous range value would result in incorrect set of skyline objects.

Landslides are one of the most frequent and devastating natural disasters around the world with intensifying impacts on human lives and the environment. To effectively deal with landslides and their consequences, it is primarily important to demarcate areas susceptible to landslides. This can be done through landslide susceptibility mapping (LSM). In this study, a novel approach for landslide susceptibility mapping based on the uncertain and parameter-free density-based clustering (UPFDBSCAN) algorithm was proposed. It merges the ideas from the dominant set clustering algorithm, the DBSCAN algorithm, and the uncertain data modeling method. The study aims to overcome the limitations of depending on user-defined density parameters, the inability to identify clusters of varied densities, and to model the uncertain data, in the DBSCAN algorithm and most of the existing clustering algorithms. This improves the clustering accuracy and efficiency for LSM modeling. For this purpose, the proposed model was experimented with an inventory containing 506 samples of landslide and non-landslide locations, and data of 7 landslide influencing factors from the Baota District in Shaanxi, China. The model's performance was evaluated and compared with existing clustering-based LSM models as state-of-the-art methods based on standard evaluation metrics. The results revealed that the proposed model obtained the highest performance (sensitivity = 0.935, specificity = 0.944, accuracy = 0.939, AUC = 0.881, JC = 0.898, and purity = 0.899) and it was thus superior to the other models. This study's findings can help decision-makers, policymakers, and land stakeholders to implement significant strategies with early warning systems to predict, prevent, and mitigate the occurrence and impacts of landslides. [ABSTRACT FROM AUTHOR]

The logistic distribution is frequently encountered to model engineering, industrial, healthcare and other wide range of scientific data. This work introduces a flexible neutrosophic logistic distribution (LDN) constructed using the neutrosophic framework. The LDN is considered to be ideal for evaluating and quantifying the uncertainties included in processing data. The suggested distribution offers greater flexibility and superior fit to numerous commonly used metrics for assessing survival, such as the hazard function, reliability function, and survival function. The mode, skewness, kurtosis, hazard function, and moments of the new distribution are established to determine its properties. The theoretical findings are experimentally proven by numerical studies on simulated data. It is observed that the suggested distribution provides a better fit than the conventional model for data involving imprecise, vague, and fuzzy information. The maximum likelihood technique is explored to estimate the parameters and evaluate the performance of the method for finite sample sizes under the neutrosophic context. Finally, a real dataset on childhood mortality rates is considered to demonstrate the implementation methodology of the proposed model. [ABSTRACT FROM AUTHOR]

Background and Objectives: In this paper, a new version of the particle swarm optimization (PSO) algorithm using a linear ranking function is proposed for clustering uncertain data. In the proposed Uncertain Particle Swarm Clustering method, called UPSC method, triangular fuzzy numbers (TFNs) are used to represent uncertain data. Triangular fuzzy numbers are a good type of fuzzy numbers and have many applications in the real world. Methods: In the UPSC method input data are fuzzy numbers. Therefore, to upgrade the standard version of PSO, calculating the distance between the fuzzy numbers is necessary. For this purpose, a linear ranking function is applied in the fitness function of the PSO algorithm to describe the distance between fuzzy vectors. Results: The performance of the UPSC is tested on six artificial and nine benchmark datasets. The features of these datasets are represented by TFNs. Conclusion: The experimental results on fuzzy artificial datasets show that the proposed clustering method (UPSC) can cluster fuzzy datasets like or superior to other standard uncertain data clustering methods such as Uncertain K-Means Clustering (UK-means) and Uncertain K-Medoids Clustering (UK-medoids) algorithms. Also, the experimental results on fuzzy benchmark datasets demonstrate that in all datasets except Libras, the UPSC method provides better results in accuracy when compared to other methods. For example, in iris data, the clustering accuracy has increased by 2.67% compared to the UK-means method. In the case of wine data, the accuracy increased with the UPSC method is 1.69%. As another example, it can be said that the increase in accuracy for abalone data was 4%. Comparing the results with the rand index (RI) also shows the superiority of the proposed clustering method. [ABSTRACT FROM AUTHOR]

In practical scenarios, it is common to encounter fuzzy data that contains numerous imprecise observations. The uncertainty associated with this type of data often leads to the use of interval statistical measures and the proposal of neutrosophic versions of probability distributions to better handle such data. This study introduces a new generalized design of the log-logistic distribution within a neutrosophic framework, building upon encouraging applications of this distribution in fields such as economics, engineering, survival analysis, and lifetime modeling. The proposed neutrosophic loglogistic distribution (NLLD) is analyzed in terms of statistical properties, including moments, shape coefficients, and various survival characteristics. To evaluate the performance of the predicted neutrosophic parameters, an estimation procedure is conducted. Finally, the practical application of the proposed model is demonstrated using a sample dataset consisting of 128 bladder cancer patients. [ABSTRACT FROM AUTHOR]

Intuitionistic Fuzzy Multi-Criteria Decision Making methods provide a set of effective support tools that help the decision-maker deal with uncertainty in real-life complex decision-making problems. Therefore, we present IFMCDM, an open-source R package that enables evaluation and rank ordering alternatives represented in an intuitionistic fuzzy context. The IFMCDM package consists of two multi-criteria methods, Intuitionistic Fuzzy TOPSIS, and Intuitionistic Fuzzy Synthetic Measure, both based on reference points. Moreover, the R package offers two representations of reference points and two distance measures (Hamming and Euclidean) which allows four variants of each method. This enables decision-makers to be more flexible and comprehensively analyze real complex decision-making problems. Additionally, a useful function that converts raw data into an intuitionistic fuzzy representation has been included.

The discovery of soft sets is accredited to Molodtsov. This theory can cope with difficult circumstances with a lot of ambiguity, like those where deciding is hard. The bipolar soft set (BSS) and neutrosophic soft set (NSS) are algebraic models that can be viewed as soft set expansions. The BSS theory states that we weigh the pros and cons when deciding and NSS theory can handle belief system ambiguity, contradiction, and lack of knowledge due to its truth and falsity membership values. The concept of BSS and NSS are explained in comprehensive detail in this article. This article examined the weighted bipolar soft set (WBSS) and the weighted neutrosophic soft set (WNSS), as well as how to make accurate decisions under uncertain or inadequate information. A detailed comparison of information extraction approaches using weighted bipolar and neutrosophic soft sets may be lacking in the literature. These strategies may have been studied separately, but there may be little research comparing their performance under different settings and with diverse data. Filling this gap with a thorough and rigorous comparison study would help comprehend these techniques' practical benefits and drawbacks.

The paper introduces the update of the Python Intuitionistic Fuzzy Decision Making (PyIFDM) package, emphasizing its focus on Multi-Criteria Decision Analysis (MCDA) with uncertain data modeled as Intuitionistic Fuzzy Sets (IFS). The update enhances PyIFDM’s capabilities, introducing five new IF-MCDA methods and basic mathematical operations on IFS. The update includes the implementation of Max normalization, a Hausdorff measure-based Euclidean distance, and a module for IFS-similarity measures. The release contributes to a significant library upgrade, providing advanced tools for modeling fuzzy data in and outside the MCDA space. PyIFDM now supports a greater variety of popular IF-MCDA methods, offering flexibility and applicability in decision-making scenarios. The added visualization module facilitates the graphical representation of IFS, aiding the interpretation of complex decision models involving intuitionistic fuzzy data. The enhanced version of the PyIFDM library expands its applicability in research methodologies, making it a more efficient tool for conducting multi-criteria decision analysis in an intuitionistic fuzzy environment.

The discovery of soft sets is accredited to Molodtsov. This theory can cope with difficult circumstances with a lot of ambiguity, like those where deciding is hard. The bipolar soft set (BSS) and neutrosophic soft set (NSS) are algebraic models that can be viewed as soft set expansions. The BSS theory states that we weigh the pros and cons when deciding and NSS theory can handle belief system ambiguity, contradiction, and lack of knowledge due to its truth and falsity membership values. The concept of BSS and NSS are explained in comprehensive detail in this article. This article examined the weighted bipolar soft set (WBSS) and the weighted neutrosophic soft set (WNSS), as well as how to make accurate decisions under uncertain or inadequate information. A detailed comparison of information extraction approaches using weighted bipolar and neutrosophic soft sets may be lacking in the literature. These strategies may have been studied separately, but there may be little research comparing their performance under different settings and with diverse data. Filling this gap with a thorough and rigorous comparison study would help comprehend these techniques' practical benefits and drawbacks. [ABSTRACT FROM AUTHOR]

When dealing with uncertain data, traditional model construction methods often ignore or filter out noise data to improve model performance. However, this simple approach can lead to insufficient data utilization, model bias, reduced detection ability, and decreased robustness of detection models. Outliers can be considered as data that are inconsistent with other patterns at certain specific moments and are not always negative data, so their emergence is not always bad. In the process of data analysis, outliers play a crucial role in sample vector recognition, missing value processing, and model stability verification. In addition, unsupervised models have very high computation costs when recognizing outliers, especially non-parameterized unsupervised models. To solve the above problems, we used semi-supervised learning processes and used similarity as a negative selection criterion to propose a local density verification detection model (Vd-LOD). This model establishes similarity pseudo-labels for multi-label and multi-type samples, verifies the accuracy of outlier values based on local outlier factors, and increases the detector's sensitivity to outliers. The experimental results show that under different parameter settings with varying outlier quantities, Vd-LOD outperforms other detection models in terms of the significant increase in average time consumption caused by verifying the presence of relationships, while also achieving an approximate 6% improvement in average detection accuracy. [ABSTRACT FROM AUTHOR]

The subject of electric vehicles (EVs) is constantly relevant from the perspective of climate change and sustainability. Multi-Criteria Decision Analysis (MCDA) methods can be successfully used to evaluate models of such vehicles. In many cases, the MCDA methods are modified to account for uncertainty in the data. There are many ways to express uncertainty, including more advanced ones, such as fuzzy sets, for example, but expressing attributes in terms of interval numbers remains a popular method because it is an easy-to-implement and easy-to-understand technique. This study focuses on interval extensions of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. It aims to compare the most popular extension proposed by Jahanshahloo and the proposed new modification, which returns the result in an interval form. Certain inconsistencies of the Jahanshahloo extension are discussed, and it is explained how the new extension avoids them. Both extensions are applied to an EV evaluation problem taken from the literature as an example for sustainable assessment. The results are then analyzed, and the question of whether the input data of the interval should receive an evaluation in the form of interval results is addressed. [ABSTRACT FROM AUTHOR]

Aim. The purpose of this research is to employ a fuzzy approach to assess the system behavior of the washing unit in a paper plant using vague, uncertain and inaccurate data. The washing unit is the main operational part of a paper plant for which analysis of system behavior is important to choose an appropriate maintenance strategy. The analysis has been carried out for washing unit of a paper plant situated in northern India. Methods. The proposed approach comprises qualitative and quantitative analysis. In qualitative analysis, the basic arrangement of the washing unit is modelled by Petri Net model. In quantitative analysis, the fuzzy λ-τ approach has been used for analyzing the systems' failure behavior more accurately. Uncertainties in failure/repair data of every subsystem/component of the washing unit are quantified using trapezoidal fuzzy numbers. Results. To assess the performance and failure dynamic behavior of the washing unit quantitatively, six reliability parameters including failure rate, repair time, mean time between failure, expected number of failures, reliability and availability at three different spread levels have been evaluated employing trapezoidal fuzzy numbers. The fuzzified values of these reliability parameters of washing unit have been defuzzified employing center of area defuzzification technique. Further, crisp values and defuzzified values of these parameters using triangular fuzzy numbers have also been obtained. The results obtained by the proposed methodology have been compared with those obtained by fuzzy λ-τ approach based on triangular fuzzy numbers. The information/results obtained through the fuzzy λ-τ approach with trapezoidal fuzzy number are conservative in nature, therefore, these results may be used by system specialist/system analysts for the future plan of implementation. Conclusion. Using this approach, six reliability parameters are evaluated and the trend (increase or decrease) of these reliability parameters is examined for performance analysis of washing unit in a paper plant. Based on these investigations, suitable maintenance policy can be established that will assist maintenance manager/system analysts/engineers in improving system performance by implementing appropriate preventive maintenance procedures. As a result, it will help in achieving a long time system availability and maximizing overall productivity of the paper plant. The implications of this fuzzy reliability approach to industry maintenance and operation planning are quite beneficial. [ABSTRACT FROM AUTHOR]

It is difficult to label the label proportions data contaminated by noise in practical applications. This paper applied multi-view learning to uncertain learning with label proportions to improve classifier both performance and stability. Firstly, the proposed method introduced kernel function and super instance label for the representation of multi-view knowledge and label proportions information respectively, and considered the limitation of uncertain label proportions data to construct the model of uncertain label proportions information-based multi-view learning. Then, it transformed the model to its dual forms by the Lagrange method for solving the problem and introduced an iterative framework to obtain the classifiers of multiple views. Finally, this paper applied the proposed method to datasets of text categorization. Extensive experiments show that the proposed method outperforms the existing single-view learning with label proportions methods in accuracy and noise sensitivity. [ABSTRACT FROM AUTHOR]

The stability problem of switched systems plays an essential role in the study of long-term behavior. In fact, systems containing both time delay and uncertainty terms may lead to performance degradation of those systems. Therefore, we are interested in the robust stability for discrete-time switched positive time-varying delay systems with interval uncertainties in the case of all modes being unstable. Based on the proposed time-scheduled multiple co-positive Lyapunov–Krasovskii functional of each mode, new sufficient conditions for the global uniform asymptotic stability of the systems are derived. An effective time-dependent switching law utilized in this work is mode-dependent dwell time. In addition, the robust stability criteria in an asymptotic sense are formulated for the systems without time-varying delay. Compared with the existing related works, our results are less conservative and more general than some previous research. Finally, two numerical examples are provided to illustrate the effectiveness and correctness of the developed theoretical results. [ABSTRACT FROM AUTHOR]

Cluster validity indices (CVIs) for evaluating the result of the optimal number of clusters are critical measures in clustering problems. Most CVIs are designed for typical data-type objects called certain data objects. Certain data objects only have a singular value and include no uncertainty, so they are assumed to be information-abundant in the real world. In this study, new CVIs for uncertain data, based on kernel probabilistic distance measures to calculate the distance between two distributions in feature space, are proposed for uncertain clusters with arbitrary shapes, sub-clusters, and noise in objects. By transforming original uncertain data into kernel spaces, the proposed CVI accurately measures the compactness and separability of a cluster for arbitrary cluster shapes and is robust to noise and outliers in a cluster. The proposed CVI was evaluated for diverse types of simulated and real-life uncertain objects, confirming that the proposed validity indexes in feature space outperform the pre-existing ones in the original space. [ABSTRACT FROM AUTHOR]

As an information granulation technology, clustering plays a pivotal role in unsupervised learning, serving as a fundamental cornerstone for various data mining techniques. The effective and accurate classification of data is a central focus for numerous researchers. For a dataset, we assert that the classification performance of a clustering method is significantly influenced by uncertain data, particularly those situated at the cluster boundaries. It is evident that uncertain data encapsulate richer information compared with others. Generally, the greater the uncertainty, the more information the data holds. Therefore, conducting a comprehensive analysis of this particular subset of data carries substantial significance. This study presents an approach to characterize data distribution properties using fuzzy clustering and defines the boundary and non-boundary characteristics (certainty and uncertainty) of the data. To improve the classification performance, the strategy focuses on reducing the uncertainty associated with boundary data. The proposed scheme involves inserting data points with the cloud computing technology based on the distribution characteristics of the membership functions to diminish the uncertainty of uncertain data. Building upon this, the contribution of boundary data is reassigned to the prototype in order to diminish the proportion of uncertain data. Subsequently, the classifier is optimized through data label (classification error) supervision. Ultimately, the objective is to leverage clustering algorithms for classification, thereby enhancing overall classification accuracy. Experimental results substantiate the effectiveness of the proposed scheme.

The introduction of fuzzy logic assumptions into Multi-Criteria Decision Analysis (MCDA) methods has broadened the application area of these techniques, increasing their practical potential. Due to their popularity, decision-makers need to have access to open and comprehensive tools to assist them in their decision-making process. In this paper, we propose a software library written in Python for multi-criteria computing in an uncertain environment. PyIFDM (Python Intuitionistic Fuzzy Decision Making) offers users access to selected Intuitionistic Fuzzy MCDA methods, normalization techniques, distance measures, score functions, or objective weighting methods. It enables users to build complex decision-making models regarding the uncertainties occurring in the problem.

Efficiency evaluation is a very important and key issue in competitive conditions. Organizations and companies face various uncertainties, and this makes it extremely difficult and complex to evaluate their efficiency. In this research, opened-network data envelopment analysis models have been developed in uncertain space for three uncertain states, including: uncertain outputs, uncertain inputs, and simultaneous uncertain inputs and outputs. The proposed models have been used to evaluate the efficiency of 10 two-stage processes, seller and buyer in a supply chain, and the impact of data uncertainty is examined. The results obtained from the developed models have been compared with the results of traditional DEA network models. The validity and accuracy of the developed models have also been examined. The results have shown that the reliability of the proposed models is higher than the traditional DEA network model. Also, by examining the efficiency of decision-making units in different conditions of data uncertainty and deviations, it was determined that the greater the range of this deviation, the lower the efficiency score of different units will be, which is consistent with reality. [ABSTRACT FROM AUTHOR]

In this paper, we establish various results of duality for a new class of constrained robust nonlinear optimization problems. For this new class of problems, involving functionals of (path-independent) curvilinear integral type and mixed constraints governed by partial derivatives of second order and uncertain data, we formulate and study Wolfe, Mond-Weir and mixed type robust dual optimization problems. In this regard, by considering the concept of convex curvilinear integral vector functional, determined by controlled second-order Lagrangians including uncertain data, and the notion of robust weak efficient solution associated with the considered problem, we create a new mathematical context to state and prove the duality theorems. Furthermore, an illustrative application is presented. [ABSTRACT FROM AUTHOR]

In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy numbers, a similarity criterion based on the intersection region of the fuzzy numbers is used. The performance of the suggested clustering method has been experimented on both benchmark and artificial datasets. These datasets are used in the fuzzy form. The experiential results represent that the suggested clustering method with fuzzy cluster centers can cluster triangular fuzzy datasets like other standard uncertain data clustering methods. Experimental results demonstrate that, in almost all datasets, the proposed clustering method provides better results in accuracy when compared to Uncertain K-Means and Uncertain K-medoids algorithms.

Landslide susceptibility mapping (LSM) is a crucial step during landslide assessment and environmental management. Clustering algorithms can construct effective models for LSM. However, a random selection of important parameters, inconsideration of uncertain data, noise data, and large datasets can limit the implementation of clustering in LSM, resulting in low and unreliable performance results. Thus, to address these problems, this study proposed an optimized clustering algorithm named O-CURE, which combines: the traditional Clustering Using REpresentatives algorithm (CURE), that is, efficient for large datasets and noise data, the partition influence weight (PIW)-based method to enhance the selection of sample sets and the city block distance (CIBD) for processing of the uncertain data in CURE clustering during LSM modeling. A database containing 293 landslide location samples, 213 non-landslide samples, and 7 landslide conditioning factors was prepared for the implementation and evaluation of the method. Also, a Multicollinearity analysis was conducted to select the most appropriate factors, and all the factors were acceptable for modeling. Based on O-CURE, landslide density, and the partitioning around medoids (PAM) algorithm a susceptibility map was constructed and classified into very high (33%), high (18%), moderate (24%), low (13%), and very low (12%) landslide susceptible levels. To evaluate the performance of the O-CURE model, five statistic metrics including accuracy, sensitivity, specificity, kappa, and AUC were applied. The analysis shows that O-CURE obtained accuracy = .9368, sensitivity = .9215, specificity = .9577, kappa = .8496, and AUC = .896 is an indication of high-performance capability. Also, the proposed method was compared with the CURE algorithm, three existing clustering methods, and popular supervised learning methods. From this assessment, O-CURE outperformed the other clustering methods while showing significant and more consistent performance than the supervised learning methods. Therefore, we recommend that the O-CURE model and the constructed map can be useful in assessing landslides and contribute to sustainable land-use planning and environmental management in light of future disasters.

Decision-making is an integral part of life. Many situations require us to make rational choices among numerous decision options. In addition, real-world problems are characterized by uncertainties that hinder the entire process. Multi-Criteria Decision Analysis (MCDA) comes with help. Many evaluation techniques have been developed along with fuzzy logic assumptions, classified as fuzzy MCDA tools. These methods allow efficient and effective evaluation of decision variants in an uncertain environment. Existing tools facilitate the work of these methods, but they are limited or not updated with newly established technologies. Therefore, this article proposes the pyFDM (Python Fuzzy Decision Making): a new Python 3 software library to facilitate calculations using methods that operate on uncertain data. In addition to evaluation methods, various techniques for normalization, defuzzification, distance measure, or objective criteria weighting are also included. It provides a comprehensive set of tools for using available techniques to perform calculations in this field. The operation of the proposed library is demonstrated through two practical examples. The first is directed to selecting the most appropriate stock, while the second is devoted to the ERP system selection firms. The presented examples show that the implemented library can be effectively used for fuzzy decision-making problems based on Triangular Fuzzy Numbers. The modular structure allows the interchangeable use of available methods, making the tool a comprehensive environment for calculations in this area.

The subject of electric vehicles (EVs) is constantly relevant from the perspective of climate change and sustainability. Multi-Criteria Decision Analysis (MCDA) methods can be successfully used to evaluate models of such vehicles. In many cases, the MCDA methods are modified to account for uncertainty in the data. There are many ways to express uncertainty, including more advanced ones, such as fuzzy sets, for example, but expressing attributes in terms of interval numbers remains a popular method because it is an easy-to-implement and easy-to-understand technique. This study focuses on interval extensions of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. It aims to compare the most popular extension proposed by Jahanshahloo and the proposed new modification, which returns the result in an interval form. Certain inconsistencies of the Jahanshahloo extension are discussed, and it is explained how the new extension avoids them. Both extensions are applied to an EV evaluation problem taken from the literature as an example for sustainable assessment. The results are then analyzed, and the question of whether the input data of the interval should receive an evaluation in the form of interval results is addressed.