Search Results (102)

The incorporation of digital music into the reform of education has become one of the primary methods to improve educational outcomes, increase creativity, and innovate the practices in the classroom. This combination, together with the close industry–academia cooperation, presents the possibilities to integrate educational strategies in accordance with the technological and creative demands of the contemporary world. Nevertheless, uncertainty, reluctance, symmetry, and subjectivity in expert ratings are significant problems to cope with when considering multi-criteria decision-making (MCDM). To resolve them, this paper suggests a Picture Fuzzy Faire Un Choix Adequat (PF-FUCA) decision support model, where fifteen options will be rated by seven criteria, depending on the contribution of four professional decision-makers. These findings indicate that the PF-FUCA framework is effective and superior to the current PF-MCDM models, as illustrated by sensitivity and comparison analysis. The identified best strategies based on the framework are blockchain-based music copyright education and integrated music–STEM platforms that, when combined, offer a viable policy instrument to policymakers, educators, and industry stakeholders. Full article

In the current digital landscape, where platforms process AI-generated content and intelligent network traffic on a large scale, it is the duty of such platforms to continuously measure the reliability, trustworthiness, and security of various data streams. Driven by this practical challenge, this research develops an effective decision-support mechanism in intelligent decision-making in big-data AI-generated content and network systems. The decision problem has considered several uncertainties, including content authenticity, processing efficiency, user trust, cybersecurity, system scalability, privacy protection, and cost of computing. The multidimensional uncertainty of AI-generated information and trends in network behavior are challenging to capture in traditional crisp and fuzzy decision-making models. To fill that gap, a new Picture Fuzzy Faire Un Choix Adequat (PF-FUCA) methodology is proposed, based on multi-perspective expert assessment and better computational aggregation to improve the accuracy of rankings, symmetry, and uncertainty treatment. A case scenario comprising fifteen different alternative intelligent decision strategies and seven evaluation criteria are examined under the evaluation of four decision-makers. The PF-FUCA model successfully prioritizes the best strategies to control AI-based content and network activities to generate a stable and realistic ranking. The comparative and sensitivity analysis show higher robustness, accuracy, and flexibility levels than the existing MCDM techniques. The results indicate that PF-FUCA is specifically beneficial in settings where a large amount of data has to flow, a high uncertainty rate exists, and the variables of decision are dynamic. The research introduces a scalable and credible methodological conception that can be used to facilitate high levels of intelligent computing applications to content governance and network optimization. Full article

The concept of symmetry is fundamental to the study of algebra; it serves as the basis for a branch of group theory that is essential to abstract algebra. A semigroup is a structure that builds upon the concept of a group, similarly extending the idea of symmetry found within groups. In this study, we specifically focus on semigroups. The main objective of this research is to apply the notion of m-polar picture fuzzy sets (m-PPFSs), with m being a natural number, in investigations into semigroups, as this concept generalizes m-polar fuzzy sets (m-PFSs) and picture fuzzy sets (PFSs). This research introduces the concepts of m-polar picture fuzzy left ideals (m-PPFLs), m-polar picture fuzzy right ideals (m-PPFRs), m-polar picture fuzzy ideals (m-PPFIs), m-polar picture fuzzy bi-ideals (m-PPFBs), and m-polar picture fuzzy generalized bi-ideals (m-PPFGBs) in semigroups. This study examines the relationships between these concepts, showing that every m-PPFL (m-PPFR) in the semigroups is also an m-PPFB, and that every m-PPFB in the semigroups is an m-PPFGB. However, the opposite is not true. Additionally, we provide the characteristics of the m-PPFLs, m-PPFRs, m-PPFIs, m-PPFBs, and m-PPFGBs in semigroups. We further discuss the connections between the m-PPFLs (m-PPFIs) and the m-PPFBs within the framework of regular semigroups, and most importantly, we show that, if the semigroup is regular, then the m-PPFBs and m-PPFGBs are equal. Finally, we utilize the properties of the m-PPFLs, m-PPFRs, m-PPFIs, m-PPFBs, and m-PPFGBs within semigroups to explore the classifications of regular semigroups. Full article

Especially in crowded cities, the public transportation system is one of the most crucial elements that influences quality of life and also demonstrates progress. For this purpose, a new SERVQUAL model, expanded with sustainability and Industry 4.0 dimensions, is proposed to evaluate service quality in the public transport system. This model, called SPT SERVQUAL 4.0 (Sustainable Public Transport SERVQUAL 4.0), is created with a three-level hierarchical criteria structure by developing the structure of the traditional SERVQUAL model. First of all, criteria weights are determined using the Bayesian Best–Worst Method (BWM) and expert evaluations for each level. Afterwards, the Picture Fuzzy WASPAS method is applied in order to rank the public transportation alternatives using the obtained criteria weights. The proposed hybrid methodology is applied on a real case study of five different bus alternatives in the Izmir public transportation system. As a result, the best public transportation bus alternative is found to be electric buses. The study, which adapts the dimensions of Industry 4.0 and sustainability, two of the most important issues of our age, to the evaluation of public transport system service quality, contributes by providing insights into system improvement and strategy development in the public or private sector. Full article

Effectively managing Construction and Demolition Waste (CDW) requires prioritizing multi-dimensional risks, a task complicated by the inherent uncertainty and subjectivity of expert judgments. While classical methods like Failure Modes and Effects Analysis (FMEA) and Fine–Kinney (FK) provide a diagnostic structure, they struggle to capture the vagueness in subjective assessments. This study addresses this gap by developing an integrated framework that couples the classical FMEA/FK criteria (Severity, Exposure, Probability, Detectability, Frequency) with Picture-Fuzzy (PiF) multi-criteria decision making. The methodology first elicits criterion importances from 15 experts using PiF Stepwise Weight Assessment Ratio Analysis (PiF-SWARA), which retains approval, indeterminacy, rejection, and refusal degrees to reduce information loss. Subsequently, it ranks 40 risk factors using the PiF Technique for Order Preference by Similarity to Ideal Solution (PiF-TOPSIS). Results show severity is the most influential criterion, followed by exposure and probability. The framework identifies the highest-priority risks as cumulative pollution with rising complaints, groundwater leakage, and insufficient investment/operating budgets. A sensitivity analysis confirms that environmental and financial risks remain consistently prominent across various weighting scenarios. This harmonized FMEA/FK–PiF-SWARA–TOPSIS approach yields a transparent and defensible prioritization, offering a practical tool for managers to allocate resources effectively, focusing on critical environmental controls and addressing core financial deficiencies in CDW systems. Full article

Iran is a uniquely compelling case due to its ancient and diverse culinary heritage, coupled with a strategic national mandate to significantly boost tourism, making the development of this high-impact sector a crucial policy imperative. The present study adopts a scenario planning approach to first identify the key factors influencing food tourism in rural areas of Iran, then explores plausible future scenarios for rural tourism development, and finally ranks strategic alternatives for enhancing food tourism in these regions. Methodologically, the research combines a goal-oriented, descriptive-analytical approach with future study techniques. Data for the initial phase were collected through a literature review, field studies (surveys, interviews), and expert surveys, and subsequently analyzed using MICMAC and ScenarioWizard software tools. Strategic alternatives were then evaluated using Picture Fuzzy Sets (PFSs) and the COPRAS method based on six critical factors. The findings reveal that six primary factors—promotional activities, pricing, food quality, infrastructure, government support, and investment—play pivotal roles in advancing food tourism in rural Iran. Based on these six primary factors, the study constructs three future scenarios: optimistic, stagnant, and crisis-driven scenarios. In the third phase of the analysis, employing Picture Fuzzy COPRAS and Picture Fuzzy Analytic Hierarchy Process (PF-AHP), the results indicate that “food festivals and promotional campaigns” carry the greatest weight and are deemed the most influential in attracting tourists, whereas “investment” ranks the lowest. Following normalization and application of weights, COPRAS analysis identifies “improving the quality of tourism infrastructure” as the most effective strategy, receiving the highest score (464.0620). A sensitivity analysis further confirms that the overall ranking of the strategies remains stable despite changes in the criteria weights, with only minor shifts observed among mid-ranked alternatives. These results offer policymakers a practical decision-making tool to allocate limited resources efficiently and focus on high-impact strategies that support the sustainable development of food tourism in Iran’s rural areas. Full article

Smart grid operators face escalating cyber threats and tight resource constraints, demanding the transparent, defensible prioritization of security controls. This paper asks how to select cybersecurity controls for smart grids while retaining picture fuzzy evidence throughout and supporting policy-sensitive “what-if” analyses. We propose a hybrid Picture Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) and Combinative Distance-based Assessment (CODAS) framework that carries picture fuzzy evidence end-to-end over a domain-specific cost/benefit criteria system and a relative-assessment matrix, complemented by multi-scenario sensitivity analysis. Applied to ten prominent solutions across twenty-nine sub-criteria in four dimensions, the model highlights Performance as the most influential main criterion; at the sub-criterion level, the decisive factors are updating against new threats, threat-detection capability, and policy-customization flexibility; and Zero Trust Architecture emerges as the best overall alternative, with rankings stable under varied weighting scenarios. A managerial takeaway is that foundation controls (e.g., OT-integrated monitoring and ICS-aware detection) consistently remain near the top, while purely deceptive or access-centric options rank lower in this context. The framework contributes an end-to-end picture fuzzy risk-assessment model for smart grid cybersecurity and suggests future work on larger expert panels, cross-utility datasets, and dynamic, periodically refreshed assessments. Full article

Terminal forensics in large mobile networks is a vital activity for identifying compromised devices and analyzing malicious actions. In contrast, the study described here begins with the domain of terminal forensics as the primary focus, rather than the threat itself. This paper proposes a new multi-criteria decision-making (MCDM) model that integrates complex picture fuzzy sets (CPFS) with the combinative distance-based assessment (CODAS), referred to throughout as complex picture fuzzy CODAS (CPF-CODAS). The aim is to assist in forensic analysis for detecting mobile botnet command and control (C&C) systems. The CPF-CODAS model accounts for the uncertainty, hesitation, and complex numerical values involved in expert decision-making, using degrees of membership as positive, neutral, and negative values. An illustrative forensic case study is constructed where three mobile devices are evaluated by three cybersecurity professionals based on six key parameters related to botnet activity. The results demonstrate that the model can effectively distinguish suspicious devices and support the use of the CPF-CODAS approach in terminal forensics of mobile networks. The robustness, symmetry, and advantages of this model over existing MCDM methods are confirmed through sensitivity and comparison analyses. In conclusion, this paper introduces a novel probabilistic decision-support tool that digital forensic specialists can incorporate into their workflow to proactively identify and prevent actions of mobile botnet C&C servers. Full article

In the current digital era, as global industries transform due to technological advancements, tracking trends in emerging services has assumed increased significance. This study proposes an innovative model that integrates circular picture fuzzy sets (CPFSs) with the Weighted Aggregated Sum Product Assessment (WASPAS) method to evaluate and rank various AI-driven trends within the service industry. The CPFS approach offers enhanced responses to uncertainty, symmetric information, indecision, and varying expert opinions, while the WASPAS method ensures a dependable system for ranking prominent trends. To facilitate the evaluation process, experts and relevant studies were consulted to establish criteria that address technological developments, organizational dynamics, and market fluctuations. A hybrid fuzzy Multi-Criteria Decision-Making (MCDM) framework enabled the analysis of several potential innovations related to AI and their prioritization in the context of digitalized sectors, including healthcare, finance, online shopping, retail, and logistics. This framework is a well-structured and flexible tool for professionals and policymakers seeking to navigate the challenges of identifying new trends within unpredictable digital environments. The findings indicate that the circular picture fuzzy WASPAS approach significantly enhances trend prioritization and fosters strategic thinking in digital innovation. Furthermore, the research provides valuable insights into the complexities of fuzzy decision-making and the promotion of AI-based innovation management. Full article

This paper presents a unique multi-Q valued bipolar picture fuzzy set (MQVBPFS) methodology to tackle issues in cybersecurity risk assessment under conditions of ambiguity and contradicting data. The MQVBPFS framework enhances classical fuzzy theory through three key innovations: (1) multi-granular Q-valued membership, (2) integrated bipolarity for representing conflicting evidence, and (3) refined algebraic operations, encompassing union, intersection, and complement. Contemporary fuzzy set methodologies, such as intuitionistic and image fuzzy sets, inadequately encapsulate positive, negative, and neutral membership degrees while maintaining bipolar information. Conversely, our MQVBPFS architecture effectively resolves this restriction. Utilizing this framework for threat assessment and risk ranking, we create a tailored cybersecurity algorithm that exhibits 91.7% accuracy (in contrast to 78.2–83.5% for baseline methods) and attains 94.6% contradiction tolerance in empirical evaluations, alongside an 18% decrease in false negatives relative to conventional approaches. This study offers theoretical progress in fuzzy set algebra and practical enhancements in security analytics, improving the handling of ambiguous and conflicting threat data while facilitating new research avenues in uncertainty-aware cybersecurity systems. Full article

Modern digital media requires digitalization to protect cultural traditions, languages, and artistic expressions meant for future generations. Implementing the best digitalization strategy remains difficult because of unpredictable technological advances, changing digital preservation standards, and financial constraints. This study deals with these intricate challenges through the establishment of the picture fuzzy combined compromise for ideal solution (PF-COCOFISO) decision-making approach. The proposed framework employs picture fuzzy sets (PFSs) to develop symmetrical fuzzy assessment tools that better manage systems operating in uncertain technological settings. This practical research analyzes digital heritage archive optimization by assessing various digitalization approaches regarding important criteria, including technological adaptability and preservation standards, levels of accessibility, cultural maintenance, security systems, and sustainability initiatives. Multiple conflicting criteria can be optimally managed through the PF-COCOFISO selection process, which improves decision-making reliability. This research establishes an operational method which allows cultural organizations and digital archivists and policymakers to achieve intangible heritage digital accessibility symmetry while preserving heritage through structured methods during unstable times. Full article

Excessive use of fossil fuel-powered vehicles is a major problem for the entire world today, because of which greenhouse gases are increasing day by day. As a result, climate change and global warming have grown to be serious problems that affect both the environment and life on Earth. However, the effective way of reducing greenhouse gases is to use electric vehicles for commuting. The assessment and selection of the best possible way of charging an electric vehicle is a convoluted decision-making challenge due to the presence of assorted contradictory criteria. Additionally, individual decision makers’ minds and insufficient data are obstacles to doing this. In this regard, interval-valued picture fuzzy sets have been considered as a compatible tool to handle vagueness. In this paper, a multi-attribute group decision-making problem with the bidirectional projection-based VlseKriterijumska Optimizacija I Kompromisno Resenje $\left(VIKOR\right)$ method is considered where the weights are partially known. The objective weights of the attributes in this model are determined using the deviation-based approach. The compromised solution is also assessed using the $VIKOR$ approach. Both the interval-valued image fuzzy Schweizer–Sklar power weighted geometric operator and the interval-valued picture fuzzy Schweizer–Sklar power weighted averaging operator are used in this process. Lastly, a numerical example showing the most suitable way to charge an electric vehicle is given to demonstrate the suggested methodology. To evaluate the robustness and efficacy of the suggested strategy, a comparative analysis with current techniques and a sensitivity analysis of the parameters are also carried out. Full article

Financial Risk Management (FRM) is crucial for organizations navigating complex and volatile economic conditions, as it aids in identifying and mitigating potential losses. Conventional FRM approaches are inadequate because they do not incorporate vagueness and variability in financial data. To overcome these challenges, this research presents interval-valued picture fuzzy measurement alternatives and rankings according to the Compromise Solution (IVPF-MARCOS) method. The IVPF-MARCOS method ranks investment strategies under uncertainty by assessing ten distinct investment options across seven key factors, including market risk and return on investment. It evidences its usefulness in enhancing decision-making, increasing accuracy in FRM, and developing Multi-Criteria Group Decision-Making (MCGDM) methodologies involving aggregation operators that are symmetric in nature. Consequently, this research establishes a compelling need to adopt improved fuzzy techniques in formulating the FRM to achieve more robust financial strategies. Full article

Previous studies have shown that language learners’ auditory word recognition behavior provides evidence for independent contributions of perceptual and phonolexical representations, and learners’ patterns of auditory word recognition have been characterized as resulting from “fuzziness” or “imprecision” associated with these representations. More recently, it has been argued that representational “fuzziness” may in fact take various forms (e.g., neutralized, precise, ambiguous). The purpose of the present study is to further build on this line of work by elaborating additional logically possible scenarios by crossing larger sets of logically possible types of perceptual and phonolexical representational precision/imprecision, as an exercise in exploring the empirical and theoretical implications of our characterizations of representational fuzziness in language learners. We collect new empirical data for the purpose of demonstrating how we might evaluate auditory word recognition performance relative to this fuller set of predicted scenarios. We computed the set of hypothesized scenarios by crossing possible perceptual and lexical representations. We crossed four possible perceptual representations (NeutralizedC + NeutralizedV, NeutralizedC + PreciseV, PreciseC + NeutralizedV, or PreciseC + PreciseV) and six possible phonolexical representations (Neutralized, Ambiguous, Not X, Precise, Fuzzy Word, or Word Length), for a total of 24 scenarios, each accompanied by a set of predictions with respect to accuracy on an auditory word–picture matching test. We interpret the group and individual performance relative to these scenarios with the ultimate aim of better understanding the implications of our assumptions about the nature of perceptual and phonolexical representations relative to observed patterns of learner behavior. Our hope is that in computing this factorial typology of logically possible scenarios and demonstrating a starting point for how we might empirically evaluate its predictions, we set the stage for future research to refine the hypothesis space through empirical studies of auditory word processing in language learners. Full article

The digital transformation age introduces cybersecurity threats into the hospitality industry by increasing the exposure and vulnerability of hospitality firms’ data and systems to hackers. The hospitality industry is a diverse segment of the service sector dedicated to the provision of services in areas such as accommodation, food and beverage, travel and tourism, and recreation, including hotels, restaurants, bars, travel agencies, and theme parks. Cybersecurity risks in the hospitality industry affect the data and systems of businesses such as accommodation, food, travel, and entertainment, primarily enabled by the industry’s increasing digitization. This study aims to map the principal cybersecurity risks to the main stakeholders by proposing a novel Picture Fuzzy Sets (PFSs)-based Matrix of Alliances and Conflicts: Tactics, Objectives, and Recommendations (MACTOR) approach. The purpose here is to examine each stakeholder’s position towards handling cybersecurity attacks and estimate the uncertain nature of personal judgments of industry representatives when stating their point of view. The research aimed to extract the triggering positions of the defined cybercrime risks to reach the root cause of these risks, as the point to try to mitigate first. Thus, this paper contributes to the literature in both theoretical and practical ways by proposing a new approach and by providing real industry officials’ perspectives to solve the challenges. A hospitality practitioner can easily understand their position in this service network and take action to prevent such cybercrimes. Full article