Search Results (173)

The formation of strategies and tactics of destructive impact (DI) at the stages of complex computer attacks (CCAs) largely depends on the content of intelligence data obtained by the intruder about the attacked elements of distributed information systems (DISs). This study analyzes scientific papers, methodologies and standards in the field of assessing the indicators of awareness of the intruder about the objects of DI and symmetrical indicators of intelligence security of the elements of the DIS. It was revealed that the aspects of changing the quantitative and qualitative characteristics of intelligence data (ID) at the stages of CCA, as well as their impact on the possibilities of using certain types of simple computer attacks (SKAs), are poorly studied and insufficiently systematized. This paper uses technologies for modeling the process of an intruder obtaining ID based on the application of the methodology of black, grey and white boxes and the theory of fuzzy sets. This allowed us to identify the relationship between certain arrays of ID and the possibilities of applying certain types of SCA end-structure arrays of ID according to the levels of identifying objects of DI, and to create a scale of intruder awareness symmetrical to the scale of intelligence protection of the elements of the DIS. Experiments were conducted to verify the practical applicability of the developed models and techniques, showing positive results that make it possible to identify vulnerable objects, tactics and techniques of the intruder in advance. The result of this study is the development of an intruder awareness scale, which includes five levels of his knowledge about the attacked system, estimated by numerical intervals and characterized by linguistic terms. Each awareness level corresponds to one CCA stage: primary ID collection, penetration and legalization, privilege escalation, distribution and DI. Awareness levels have corresponding typical ID lists that can be potentially available after conducting the corresponding type of SCA. Typical ID lists are classified according to the following DI levels: network, hardware, system, application and user level. For each awareness level, the method of obtaining the ID by the intruder is specified. These research results represent a scientific contribution. The practical contribution is the application of the developed scale for information security (IS) incident management. It allows for a proactive assessment of DIS security against CCAs—modeling the real DIS structure and various CCA scenarios. During an incident, upon detection of a certain CCA stage, it allows for identifying data on DIS elements potentially known by the intruder and eliminating further development of the incident. The results of this study can also be used for training IS specialists in network security, risk assessment and IS incident management. Full article

In real-world decision-making situations, experts often rely on subjective and imprecise judgments, frequently expressed using linguistic terms. While fuzzy logic offers a valuable tool to capture and process such uncertainty, traditional methods often convert fuzzy inputs into crisp values too early in the process. This premature defuzzification can result in significant loss of information and reduced interpretability. To address this issue, the present study introduces an enhanced fuzzy TOPSIS model that utilizes expected interval representations instead of early crisp transformation. This approach allows the original fuzzy data to be preserved throughout the analysis, leading to more transparent, realistic, and informative decision outcomes. The practical application of the proposed method is demonstrated through a supplier selection case study, which illustrates the model’s capability to handle real-world, complex, and qualitative decision environments. By explicitly linking the method to this domain, the study provides a concrete anchor for practitioners and decision-makers seeking transparent and robust evaluation tools. Full article

Microalgae-based carbon sequestration is promising for implementing carbon neutrality and reducing greenhouse gas emissions. However, as the technology remains in its early developmental stages, it presents a range of risks that may deter potential investors. To address these risks, this study proposes a group-based decision-making framework for the risk evaluation of microalgae carbon sequestration projects. Fifteen risk indicators are identified and categorized into four groups, including economic, technical, market, and environmental. To handle uncertainty and vagueness in the assessment, the framework uses trapezoidal fuzzy numbers and hesitant fuzzy linguistic sets to evaluate benchmark values. An expert credibility model is developed to assign weights to expert opinions by combining the subjective RANCOM method and the objective centroid method, both adapted for a fuzzy linguistic environment. A generalized aggregation operator is then used to combine expert evaluations. This operator integrates weighted and ordered averaging techniques and converts probabilistic linguistic terms into trapezoidal fuzzy numbers. The final risk level is determined using a fuzzy comprehensive evaluation method. The results indicate a medium-high level of risk, with a similarity score of 0.960. This suggests that while microalgae carbon sequestration holds great promise, effective planning and risk management are essential. For project managers and investors, this proposed framework helps quantify risk. It provides practical guidance for improving decision-making and strengthening project management. Full article

In this article, to address the uncertainty of preference information in interrelated conflicts in the real world, a hierarchical conflict modeling and analysis approach based on hesitant fuzzy linguistic term sets (HFLTSs) is proposed. First, considering the hesitancy and fuzziness of decision makers (DMs) when expressing preferences in hierarchical conflicts, a preference representation approach based on HFLTSs is introduced. Building upon hesitant fuzzy linguistic preference, four distinct types of hesitant fuzzy stability definitions of the two-level hierarchical graph model for conflict resolution (HGMCR) are extended, and a corresponding algorithm is developed to solve the global conflict hesitant fuzzy equilibrium states. Finally, this study is applied to investigate the outbreak and development of a specific international conflict, verifying the feasibility and effectiveness of the proposed approach. The hesitant fuzzy equilibrium states of an international conflict indicate that the attitudes of domestic forces reflect a nation’s performance in the warand that the conflict may endure for an extended duration. The hierarchical conflict modeling and analysis approach based on HFLTSs allows DMs to express the hesitation and fuzziness of preferences under uncertainty, facilitates the comprehension of the intrinsic logic behind interactions among DMs at various levels, and enhances the analysis to achieve more foresighted equilibria. Full article

Despite the ideal of a unified Single Market, a powerful “home bias” pervades EU public procurement, hinting at unseen barriers that conventional analysis fails to capture. This study introduces an interpretable AI framework to investigate these dynamics, pairing a LightGBM model with SHapley Additive exPlanations (SHAP) to examine the vast Tenders Electronic Daily (TED) database (2018–2023). Concretely, we propose a fuzzy linguistic layer that translates SHAP’s complex quantitative outputs into intuitive, human-readable terms. Our model effectively distinguishes local from non-local awards (AUC ≈ 0.855), revealing that while high-value contracts expectedly attract broader competition, the most potent predictors are a country’s own history of local awards and structural factors like the buyer’s type and location. This points not to isolated incidents, but, rather, to deep-seated patterns shaping market fairness. Our combined XAI-Fuzzy approach offers a new instrument for transparent governance, enabling policymakers to diagnose market realities and forge a more genuinely open and equitable European public square. Full article

To address the challenges of assessing information security risks in smart energy systems, this study proposes a multi-attribute decision support method based on interval type-2 fuzzy numbers (IT2TrFN). First, expert questionnaires were designed to gather insights from eight specialists in the fields of smart energy and safety engineering. Linguistic terms associated with IT2TrFN were employed to evaluate indicators, converting expert judgments into fuzzy numerical values while ensuring data reliability through consistency measurements. Subsequently, a decision hierarchy structure and an expert weight allocation model were developed. By utilizing the score and accuracy functions of IT2TrFN, the study determined positive and negative ideal solutions to rank and prioritize the evaluation criteria. Key influencing factors identified include the rate of excessive initial investment, regulatory stringency, information security standards, environmental pollution pressure, and incident response timeliness. The overall risk index was calculated as 0.5839, indicating a moderate level of information security risk in the evaluated region. To validate the robustness of the model, sensitivity analyses were conducted by varying IT2FWA (Weighted aggregated operator) and IT2FGA (Weighted geometric operator) operator selections and adjusting weight coefficients. The results reveal that key indicators exhibit high risk under different scenarios. This method provides an innovative tool for the scientific evaluation of information security risks in smart energy systems, laying a solid theoretical foundation for broader regional applications and the expansion of assessment criteria. Full article

This article describes solutions to control problems using fuzzy logic, which facilitates the development of decision support systems across various fields. However, addressing this task through the manual creation of rules in specific fields necessitates significant expert knowledge. Machine learning methods can identify hidden patterns. A key novelty of this approach is the algorithm for generating fuzzy rules for a fuzzy controller, derived from interpreting a decision tree. The proposed algorithm allows the quality of the control actions in organizational and technical systems to be enhanced. This article presents an example of generating a set of fuzzy rules through the analysis of a decision tree model. The proposed algorithm allows for the creation of a set of fuzzy rules for constructing fuzzy rule-based systems (FRBSs). Additionally, it autogenerates membership functions and linguistic term labels for all of the input and output parameters. The machine learning model and the FRBS obtained were assessed using the coefficient of determination ($R^2$). The experimental results demonstrated that the constructed FRBS performed on average 2% worse than the original decision tree model. While the quality of the FRBS could be enhanced by optimizing the membership functions, this topic falls outside the scope of the current article. Full article

Background: Since periodontitis prevalence has increased globally and there is a bidirectional relationship between periodontitis and diabetes mellitus (DM), new methods of preventing and screening involving DM biomarkers could impact periodontitis management. We aimed to develop a fuzzy system to estimate the risk of periodontitis in patients with DM. Methods: Body mass index (BMI), glycemia (G), total cholesterol (C), and triglyceride (T) measurements were collected from 87 patients diagnosed with DM. Oral examinations were performed, and the number of the periodontal pockets (nrPPs) was determined. A fuzzy system was developed: BMI and G as inputs resulted in Periodontitis Risk 1 (PRisk1) output; C and T as inputs resulted in Periodontitis Risk 2 (PRisk2) output. From PRisk1 and PRisk2, the cumulative periodontitis risk (PCRisk) was assessed. Linguistic terms and linguistic grades (very small, small, medium, big, and very big) were assigned to the numerical variables by using 25 different membership functions. PCRisk and nrPP values were statistically processed. Results: In our developed fuzzy system, BMI, G, C, and T as input data resulted in periodontitis risk estimation. PCRisk was correlated with nrPP: when PCRisk increased by 1.881 units, nrPP increased by 1 unit. The fuzzy logic-based system effectively estimated periodontitis risk in type 2 diabetes patients, showing a significant correlation with the number of periodontal pockets. These findings highlight its potential for early diagnosis and improved interdisciplinary care. Full article

To tackle preference conflicts and uncertainty in large-group emergency decision-making (LGEDM), this study proposes a probabilistic linguistic LGEDM method integrating the Louvain algorithm and group pressure model. First, expert weights are determined based on a social trust network, and the Louvain algorithm is employed for expert clustering, reducing the complexity of large-scale decision information. Second, a group pressure model is introduced to dynamically adjust expert preferences, enhancing consensus and decision consistency. Third, probabilistic linguistic term sets (PLTSs) are utilized to represent fuzzy and uncertain information, while attribute weights are determined by incorporating both subjective and objective factors, ensuring scientific rigor in decision-making. Finally, an improved TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision-Making) method is adopted to account for the loss aversion behavior of decision-makers (DMs), enabling a more accurate characterization of psychological decision-making traits. The experimental results demonstrate that the proposed method outperforms existing approaches in terms of decision efficiency, group consensus, and result robustness, offering effective support for emergency decision-making in crisis situations. Full article

Since the 1960s, fuzzy logic has contributed to developing control systems based on modeling nonlinear problems using linguistic terms and inference rules. In the air quality domain, fuzzy logic has allowed us to tackle inferential environmental systems that are tolerant of human uncertainty and aimed at decision support. These systems are composed of three processes: a function to define a membership degree of the system’s value concerning a human linguistic term; an inference engine for decision making; and defuzzification methods focused on transforming the aggregated fuzzy set into a real-world value. Over the years, multiple mathematical formulas have been proposed to enrich membership functions or defuzzification methods; however, their use is sometimes limited to classical functions, limiting the importance of other proposals. This paper aims to evaluate the impact of the transformation functions in an air quality fuzzy system. The results of this work prove that the defuzzification method has a more significant effect than the others. It should be noted that by considering these results or their evaluation method, the quality of future fuzzy systems can be improved in both industrial and academic domains. Full article

With the advent of digitization, digital twin technology is gradually becoming one of the core technologies of the Industry 4.0 era, highlighting the increasing importance of digital twin project management. Despite its potential, DT projects face significant risks during implementation, stemming from technical, managerial, and operational complexities. To address these challenges, this study proposes an improved failure mode and effect analysis (FMEA) framework by integrating double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This framework converts qualitative assessments into quantitative metrics and calculates weights using a hybrid approach, enabling more precise risk prioritisation. Application of the model to an automotive manufacturing company’s DT project identified key risks, particularly in the iteration and upgrade phase, emphasising the importance of cross-departmental collaboration and robust digital infrastructure. The proposed model provides a systematic framework for enterprises to assess and mitigate risks, ensuring the successful deployment of DT projects. Full article

Representing and interpreting human opinions within an unstructured framework is inherently complex. Hesitant fuzzy linguistic term sets offer a comprehensive context that facilitates a nuanced understanding of diverse perspectives. This study introduces a methodology that integrates sentiment analysis with hesitant fuzzy linguistic term sets to effectively aggregate and compare news from diverse sources. By employing linguistic scales, our approach enhances the interpretation of various perceptions and attitudes, facilitating comprehensive knowledge extraction and representation. The main objective of this research is to conduct a comparative analysis of news coverage across European countries in relation to the Israel–Gaza war. This analysis aims to capture the multifaceted sensitivities surrounding the ongoing situation, highlighting how different nations perceive the conflict. Full article

Fuzzy semantics have a wide range of applications in life, and especially when expressing people’s evaluation information, it is more specific. As people increasingly prefer to express their personal opinions through media platforms, the opinions of the general public have become an indispensable reference. However, information asymmetry can have a significant impact on the rationality of decision-making. Based on the above considerations, this paper extends bidirectional projection to probabilistic linguistic term sets to preserve the completeness of information as much as possible. The large-scale group decision-making problem under the probabilistic linguistic environment is extended to limited interval values, and a new group decision-making method named IVBPRT-ELECTRE III algorithm (ELECTRE III based on bidirectional projection and regret theory under limited interval-valued probabilistic linguistic term set) is proposed. The method is an extended $\mathrm{E}\mathrm{L}\mathrm{E}\mathrm{C}\mathrm{T}\mathrm{R}\mathrm{E} \mathrm{I}\mathrm{I}\mathrm{I}$ method based on limited interval-valued probabilistic linguistic term set (l-IVPLTS) bidirectional projection by regret theory approach. Firstly, this involves mining the online text comment information on social media about an emergency and considering the effect of the number of fans, determining the attributes and their initial weights for judging the strengths and weaknesses of the emergency management alternative using the TF-IDF and the $\mathrm{W}\mathrm{o}\mathrm{r}\mathrm{d}2\mathrm{v}\mathrm{e}\mathrm{c}$ technology, and using the entropy value to adjust the initial weight of attributes, not only considering the real opinions of the public, but also combining with the views of experts, making the decision-making alternative selection more scientific and reasonable. Secondly, this paper fills the gap of bidirectional projection under l-IVPLTS environment; then, combining l-IVPLTS bidirectional projection and regret theory to determine the objective weights of experts, combines the differences in individual expertise of experts to obtain the comprehensive weights of experts, and uses the extended $\mathrm{E}\mathrm{L}\mathrm{E}\mathrm{C}\mathrm{T}\mathrm{R}\mathrm{E} \mathrm{I}\mathrm{I}\mathrm{I}$ method to rank the alternatives. Finally, the feasibility and validity of the provided method is verified through the Yanjiao explosion incident as a case. Full article

Within the realm of graphic design sustainability, selecting appropriate solutions has become a crucial strategic decision for organizations aiming to optimize their operations. This paper presents a novel hybrid multi-criteria decision-making (MCDM) approach, integrating a fuzzy analytical hierarchy process (FAHP) and fuzzy measurement alternatives and ranking according to compromise solution (FMARCOS). Evaluation criteria for graphic design sustainability are determined through consultation with experts, with their judgments expressed using linguistic terms based on fuzzy numbers. Criteria weights are calculated using FAHP, and the ranking and selection of the optimal potential solution are determined using FMARCOS. Subsequently, sensitivity analysis of the criteria weights is conducted to validate the results. Findings indicate that the integrated FAHP and FMARCOS model provides a robust and adaptable assessment framework for graphic design sustainability, enabling companies to navigate complexities strategically and effectively. The key contribution of this research is its emphasis on a systematic and objective model, offering practical insights relevant to the industry. It also serves as a valuable benchmark for future research in similar fields. Full article

This study presents a novel sustainability plan tailored for Istanbul Airport, leveraging advanced decision-making methodologies to address the urgent need for sustainable practices in aviation. By integrating the entropy weighting and 2-tuple linguistic T-spherical fuzzy multi-criteria decision-making (MCDM) models, the study offers a comprehensive approach to evaluating and prioritizing sustainability criteria based on expert input from 12 professionals. The novelty of this research lies in its unique combination of advanced MCDM techniques with cutting-edge technologies, including IoT-enabled monitoring systems, digital twin models, blockchain-based sustainability reporting, and carbon capture initiatives, tailored specifically for large-scale airport operations. The study develops a phased implementation roadmap comprising three stages: (1) a short-term focus on energy efficiency and renewable energy infrastructure, achieving significant cost reductions within a 3–7.5-year payback period; (2) medium-term initiatives integrating IoT and digital twins to enhance operational efficiency; and (3) long-term measures incorporating carbon capture and blockchain for transparency and compliance. Key implementation steps include upgrading energy systems, deploying IoT sensors, creating digital replicas of airport infrastructure, and establishing regulatory and stakeholder collaboration frameworks. This research contributes a replicable framework for airports worldwide, bridging theoretical models with actionable solutions. Full article