Informatica

Nowadays, it is agreed that fuzzy sets are suitable for capturing and representing the concept of vagueness and uncertainty, and various fuzzy reasoning systems are being developed based on them. Researchers have proposed fuzzy set extensions to improve the performance and accuracy of these systems. The research questions arise regarding how fuzzy sets have evolved and what the main trends in their evolution are. To address these questions, our research presents a chronological and bibliometric analysis of fuzzy sets based on papers extracted from the Web of Science database. The main findings and contributions have been identified, systematized and visualized in a fuzzy set keyword map of 65 fuzzy set extensions. These extensions are primarily used for decision-making, reasoning, and prediction, particularly in the context of digital transformation, by integrating digital technologies into all areas of business, transforming operations and enhancing value delivery to customers. As organisations increasingly adopt digital technologies, the need for robust frameworks to manage uncertainty becomes critical. The main trends indicating the directions of fuzzy sets development, an overview of the variety and popularity of fuzzy sets over the years, and the impact of countries engaged in fuzzy set research are also identified and reported. The results support researchers and practitioners working on fuzzy sets and their applications by providing valuable insights into the fuzzy set topic, its existing extensions, and, more generally, to any field of investigation where fuzzy sets are relevant, particularly in the realm of digital transformation.

New generation battery technology investments play a key role in the transition process from fossil fuels to renewable energy. The main problem related to the subject is that decision makers experience uncertainty about which of these numerous criteria affecting investment performance are prioritized. The lack of comprehensive models in the literature for systematically prioritizing these criteria creates a significant gap. The aim of this study is to determine the priority strategies to increase the performance of new generation battery technology investments. In this context, an innovative decision-making model is developed by integrating multi-facet fuzzy sets, logarithmic least-squares and WASPAS techniques. This study makes a significant contribution to the literature by prioritizing the performance indicators of new generation battery technology investments via an innovative decision-making model. The development of multi-facet fuzzy sets in this study provides an important contribution to the literature. Moreover, dynamic decision-making opportunity is provided by redefining membership degrees with different parameter sets for each scenario. This provides the opportunity to make clearer decisions based on scenarios and dynamic evaluations in complex decision-making processes. The main findings of the study indicate that circularity and compatibility with existing manufacturing infrastructure are priorities in improving the performance of these projects.

Fair comparison with state-of-the-art evolutionary algorithms is crucial, but is obstructed by differences in problems, parameters, and stopping criteria across studies. Metaheuristic frameworks can help, but often lack clarity on algorithm versions, improvements, or deviations. Some also restrict parameter configuration. We analysed source codes and identified inconsistencies between implementations. Performance comparisons across frameworks, even with identical settings, revealed significant differences, sometimes even with the authors’ own code. This questions the validity of comparisons using such frameworks. We provide guidelines to improve open-source metaheuristics, aiming to support more credible and reliable comparative studies.

Industries have increasingly adopted supply chain management practices to sustain competitive advantage, fostering collaboration among supply chain partners for effective coordination. While prior research has explored whether inter-partner relationships influence supply chain network performance, these studies have primarily focused on perceived effects rather than emprical observations. This study investigates the impact of trust on supply chain network performance through linguistic summarization. Its originality lies in integrating linguistic summarization with heterogeneous information network modelling, a novel method for evaluating trust-driven performance effects in supply chains. We modelled supply chain networks as heterogeneous information networks, representing companies and products as distinct node types, and their interactions as varied link types. A linguistic summarization framework was developed for these networks, and its application in the automotive industry enabled the validation of literature-derived hypotheses through the truth degree of linguistic summaries. The findings demonstrate that trust significantly enhances organizational performance, particularly in terms of profitability. Supply chain managers, analysts, and researchers especially gain from this study since it offers a data-driven, interpretable framework for assessing how trust affects network performance, which promotes cooperation, transparency, and decision-making.

Most classification algorithms involve subjective inputs or hyperparameters to be determined prior to performing the classification. When taking different input or hyperparameter values, each classification algorithm will comprise a collection of classifiers. In this work, we propose a data-driven methodology for assessing similarity in consensus agreement within such a collection of classifiers, and between two classification algorithms, conditional on the dataset of interest. The core of our approach lies in considering the variability introduced by different hyperparameter values for each algorithm when performing such comparisons. We address these problems by evaluating the similarity through consensus agreement and by proposing the application of asymmetric similarity indices based on the Jaccard coefficient. We present the proposed methodology on two publicly available datasets.

Anonymous multi-recipient signcryption (AMRS) is an important scheme of public-key cryptography (PKC) and applied for many modern digital applications. In an AMRS scheme, a broadcast management centre (BMC) may sign and encrypt a plaintext data (or file) to a set of multiple recipients. Meanwhile, only these recipients in the set can decrypt the plaintext data and authenticate the BMC while offering anonymity of their identities. In the past, some AMRS schemes based on various PKCs have been proposed. Recently, due to side-channel attacks, the existing cryptographic mechanisms could be broken so that leakage-resilient PKC resisting such attacks has attracted the attention of cryptographic researches. However, the work on the design of leakage-resilient AMRS (LR-AMRS) schemes is little and only suitable for multiple recipients under a single PKC. In this paper, the first leakage-resilient and seamlessly compatible AMRS (LRSC-AMRS) in heterogeneous PKCs is proposed. In the proposed scheme, multiple recipients can be members of two heterogeneous PKCs, namely, the public-key infrastructure PKC (PKI-PKC) or the certificateless PKC (CL-PKC). Also, we present a seamlessly compatible upgradation procedure from the PKI-PKC to the CL-PKC. The proposed scheme achieves three security properties under side-channel attacks, namely, encryption confidentiality, recipient anonymity and sender (i.e. BMC) authentication, which are formally shown by the associated security theorems. Finally, by comparing with related schemes, it is shown that the proposed LRSC-AMRS scheme is suitable for heterogeneous recipients and the computational cost of each recipient’s unsigncryption algorithm is constant $O(1)$.

Ordered Weighted Averaging (OWA) operators have been widely applied in Group Decision-Making (GDM) to fuse expert opinions. However, their effectiveness depends on the selection of an appropriate weighting vector, which remains a challenge due to limited research on its impact on Consensus Reaching Processes (CRPs). This paper addresses this gap by analysing the influence of different OWA weighting techniques on consensus formation, particularly in large-scale GDM (LSGDM) scenarios. To do so, we propose a Comprehensive Minimum Cost Consensus (CMCC) model that integrates OWA operators with classical consensus measures to enhance the decision-making process. Since existing OWA-based Minimum Cost Consensus (MCC) models struggle with computational complexity, we introduce linearized versions of the OWA-based CMCC model tailored for LSGDM applications. Furthermore, we conduct a detailed comparison of various OWA weight allocation methods, assessing their impact on consensus quality under different levels of expert participation and opinion polarization. Additionally, our linearized formulations significantly reduce the computational cost for OWA-based CMCC models, improving their scalability.

Traditional Anti-Money Laundering (AML) systems rely on rule-based approaches, which often fail to adapt to evolving money laundering tactics and produce high false-positive rates, overwhelming compliance teams. This study proposes an innovative machine learning (ML) framework that leverages Conditional Tabular Generative Adversarial Networks (CTGANs) to address severe class imbalance, a common challenge in Suspicious Activity Reporting (SAR). Implemented in Python, CTGAN generates realistic synthetic samples to enhance minority-class representation, improving recall and F1-scores. For instance, the Random Forest (RF) model achieves a recall of 0.991 and an F1-score of 0.528 in oversampled datasets with engineered variables, highlighting the effectiveness of CTGAN in mitigating imbalance. This framework also incorporates SQL-based feature engineering using Oracle Analytics, creating dynamic variables such as cumulative sums, rolling averages, and ranks. The modelling phase and exploratory data analysis are conducted in the SAS programming language, employing Logistic Regression (LR) as baseline, Decision Trees (DT), and RF. Evaluation across undersampled and oversampled datasets, combined with varying probability thresholds, reveals key trade-offs between sensitivity and precision. Among the models, RF consistently achieves the highest ROC-AUC scores, ranging from 0.945 in undersampled datasets to 0.951 in oversampled configurations, demonstrating its robustness and accuracy in SAR detection. By integrating CTGAN and TF-IDF (textual feature transformation in Python) with SQL-engineered variables, this framework provides a comprehensive data-driven approach to AML. It reduces false positives, strengthens the detection of suspicious activities, and ensures scalability, adaptability, and compliance with regulatory standards.

Establishing secure keys over untrusted networks is one of the most fundamental cryptographic tasks. While two-party key establishment protocols are available for many scenarios, even offering resistance to potential adversaries equipped with quantum computing resources, the multi-party scenario is not as well understood. In particular, there is a need to find designs that can make the most of the technologies available to each party involved in a cooperative n-party key establishment.

A public key signcryption with equality test (PKSCET) scheme is a public key signcryption (PKSC) scheme with the property of equality test. However, all the existing PKSCET schemes are vulnerable to a new kind of security threats, called side-channel attacks, which could potentially lead to the unauthorized disclosure of sensitive information or even the compromise of secret keys, undermining the overall confidentiality and integrity of the system. Therefore, this study aims to propose the first leakage-resilient PKSCET (LR-PKSCET) scheme that achieves resistance to side-channel attacks. Moreover, the proposed LR-PKSCET scheme is demonstrated to possess four security properties, namely, leakage resilience, indistinguishability, one-wayness, and existential unforgeability. Based on the proposed LR-PKSCET scheme, an anti-scam system (application) is presented to mitigate the ongoing occurrence of a myriad of scam cases.