Thermal insulation is essential in lowering the energy consumption of buildings. However, many fossil-based insulation and exterior cladding materials are derived from petrochemical components, which often have adverse ecological impacts. This study explores the effectiveness of integrating sustainable thermal insulation solutions into building design to reduce energy consumption and minimize ecological impact. Focusing on an energy-efficient breakfast house located in Van, Turkey, the project was modeled using Autodesk-Revit software (2023). A comprehensive analysis was conducted by generating eighty alternative scenarios, combining two distinct wall structures, eight fiber-based natural insulation materials, and five wood-based exterior cladding materials. The energy performance of each scenario was evaluated using IES-VE software (2024.1), focusing on annual total energy consumption and CO2 emissions, while accounting for regional climatic conditions and targeted indoor comfort levels. To further refine the selection of optimal materials, a hybrid evaluation was performed using multi-attribute decision approaches, including LODECI, MAXC, and DEPART. These methods provided a systematic framework for comparing the performance of wood-based insulation materials across multiple criteria. In order to verify the accuracy of the proposed multi-attribute decision models, a comparative analysis has been undertaken with other multi-attribute decision methods (COPRAS, ARAS and WASPAS). The study highlights the technical feasibility of incorporating cost-effective, eco-friendly fiber-based and wood-based materials into building envelopes, demonstrating their potential to significantly enhance energy efficiency and reduce environmental impact. By combining advanced simulation tools with robust decision-making methodologies, this research offers a scientifically grounded approach to sustainable architectural design, providing important outputs for future applications in energy-efficient construction.

In the face of increasing financial uncertainty and market complexity, this study presents a novel risk-aware financial forecasting framework that integrates advanced machine learning techniques with intuitionistic fuzzy multi-criteria decision-making (MCDM). Tailored to the BIST 100 index and validated through a case study of a major defense company in T\"urkiye, the framework fuses structured financial data, unstructured text data, and macroeconomic indicators to enhance predictive accuracy and robustness. It incorporates a hybrid suite of models, including extreme gradient boosting (XGBoost), long short-term memory (LSTM) network, graph neural network (GNN), to deliver probabilistic forecasts with quantified uncertainty. The empirical results demonstrate high forecasting accuracy, with a net profit mean absolute percentage error (MAPE) of 3.03% and narrow 95% confidence intervals for key financial indicators. The risk-aware analysis indicates a favorable risk-return profile, with a Sharpe ratio of 1.25 and a higher Sortino ratio of 1.80, suggesting relatively low downside volatility and robust performance under market fluctuations. Sensitivity analysis shows that the key financial indicator predictions are highly sensitive to variations of inflation, interest rates, sentiment, and exchange rates. Additionally, using an intuitionistic fuzzy MCDM approach, combining entropy weighting, evaluation based on distance from the average solution (EDAS), and the measurement of alternatives and ranking according to compromise solution (MARCOS) methods, the tabular data learning network (TabNet) outperforms the other models and is identified as the most suitable candidate for deployment. Overall, the findings of this work highlight the importance of integrating advanced machine learning, risk quantification, and fuzzy MCDM methodologies in financial forecasting, particularly in emerging markets.

In modern business conditions, where competitiveness is evident across all operational segments, it is necessary to adopt a proactive management approach, i.e., to actively manage business performance. To keep pace with evolving trends and technologies, a daily presence on social networks and an adequate level of product promotion are necessary. This paper proposes a Fuzzy MCDM (Multi-Criteria Decision-Making) model to define the future direction of a perfumery regarding the application of digital marketing, using a two-phase group decision-making process. A total of ten digital advertising variants were considered, combining five social networks: Facebook, Instagram, TikTok, YouTube, and Threads. The results obtained through the application of the original Fuzzy MCDM model indicate that companies should focus their efforts on promoting designer and oriental perfumes via Facebook, Instagram, and TikTok in order to enhance growth and potentially expand their business operations.

Online banking continues to grow in popularity due to its convenience, but banks face significant challenges in ensuring secure customer identity verification. Traditional authentication methods such as PINs, passwords, and one-time passwords have shown limitations, especially in the wake of the COVID-19 pandemic, which accelerated the demand for seamless and contactless solutions. Voice biometrics have emerged as a reliable alternative, offering enhanced fraud protection and a more user-friendly experience. In Malaysia, this technology enables customer verification without the need for PINs or security questions. This study proposes an advanced authentication approach that integrates keystroke dynamics and voice biometrics within a multi-factor authentication framework. By leveraging artificial intelligence and fuzzy logic, the system aims to deliver heightened security and a smoother user experience. The goal is to provide Malaysian online banking users with a safer and more secure digital environment.

The importance of managing goods delivery in urban areas has reached its peak in recent years, driven by the constant and rapid growth of online commerce. Under such conditions, where smaller quantities of goods are ordered, yet the number of shipments continues to rise, the question of last-mile delivery (LMD) efficiency becomes increasingly relevant. This paper addresses the issue of last-mile delivery zone efficiency through the application of a new methodological approach. First, the concept of measuring last-mile delivery productivity is defined using a specific example from an urban environment. Next, Key Performance Indicators (KPIs) are established to enable a proper assessment of urban zone efficiency in line with the LMD concept. The main contribution of this study is the development of the IRN OWCM (Interval Rough Number Opinion Weight Criteria Method), which is used to calculate the weights of the criteria. To assess suitable delivery zones in terms of efficiency based on the defined KPIs, the previously developed IRN OWCM method is integrated with IRN AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalization). The results identify delivery zones that are suitable in terms of meeting standardized user needs. The developed model demonstrated stability through additional verification tests and can be adequately applied in cases when it is needed to minimize subjectivity and uncertainties.

Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp transitions between preference levels. These assumptions can lead to decision outcomes with insufficient differentiation, limited discriminatory capacity, and potential issues in consistency and sensitivity. To overcome these limitations, this study proposes a novel fuzzy decision-making framework by integrating Quasi-D-Overlap functions into the fuzzy MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution) method. Quasi-D-Overlap functions represent a generalized extension of classical overlap operators, capable of capturing partial overlaps and interdependencies among criteria while preserving essential mathematical properties such as associativity and boundedness. This integration enables a more intuitive, flexible, and semantically rich modeling of real-world fuzzy decision problems. In the context of real-time health monitoring, a case study is conducted using a hybrid edge–cloud architecture, involving sensor tasks such as heartrate monitoring and glucose level estimation. The results demonstrate that the proposed method provides greater stability, enhanced discrimination, and improved responsiveness to weight variations compared to traditional fuzzy MCDM techniques. Furthermore, it effectively supports decision-makers in identifying optimal sensor alternatives by balancing critical factors such as accuracy, energy consumption, latency, and error tolerance. Overall, the study fills a significant methodological gap in fuzzy MCDM literature and introduces a robust fuzzy aggregation strategy that facilitates interpretable, consistent, and reliable decision making in dynamic and uncertain healthcare environments.

Autonomous vehicles (AVs) have become a tangible presence on roads, indicating the emergence of a promising transportation technology for the future, possibly arriving sooner than anticipated. Nevertheless, the extensive integration of this technology is contingent on various factors, with the foremost being the level of public acceptance and adjustment to this advanced technology. Several factors, including safety, privacy, and cost, play crucial roles in fostering acceptance. Consequently, this research delves into the key determinants shaping individuals' willingness to embrace AVs. In this paper, a novel model, which consists of two methods: PIPRECIA and AROMAN with Interval Rough Numbers (IRNs) has been developed. The IRN PIPRECIA serves to define criterion weights, while the most significant contribution of the paper is the extension of the AROMAN method with IRNs for evaluating the public acceptance of autonomous vehicles and adapting all the necessary conditions for their use. The results show that a rapid implementation with extensive testing strategy represents the best solution.

: Traffic represents a complex field containing many challenges, especially for decision-makers responsible for traffic management. One of its most significant areas is the management of signalised intersections with regard to pedestrian behaviour. Measuring the start-up time of pedestrians and its influence on the rest of the traffic participants is necessary. This paper proposes a new interval fuzzy rough MCDM (Multi-Criteria Decision-Making) framework in order to conduct a complex analysis of different intersections in five selected cities in Bosnia and Herzegovina and Serbia with regard to pedestrian behaviour. The proposed model combines the IFRN SWARA (Interval Fuzzy Rough Number Stepwise Weight Assessment Ratio Analysis) and IFRN CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) methods, representing novelty from a scientific perspective. The main methodological contribution of this research consists in developing an extension of the CRADIS method based on IFRNs. The IFRN SWARA method is applied for calculating the weights of the employed criteria, while the selected cities are ranked by using the IFRN CRADIS method. The research involved many intersections with and without countdown displays and a sample of over 10,000 pedestrians, which is enough to draw solid conclusions. The verification tests carried out confirm the obtained results, proving that the proposed model is stable.

The requisition for maintainable constructions has been greatly raising over the last several years. To fulfil the maintainability necessities of a construction, decisions or changes must be done to a construction in the course of the preconstruction and design steps. This can be plausible utilizing building information modelling. To indicate the utilize of building information modelling in maintainable planning, an example nursing-house is received for modelling research. The energy efficiency of nursing-home is analysed utilizing Autodesk Revit and Green Building Studio simulation which contained different characteristics such as annual heating and cooling loads, annual energy usage. Through using the utilize of different building, insulation and roof materials in the nursing-home modelling, the nursing-home modelling is changed into a greener construction modelling. In addition, the effects of using green walls on the facade of the building on the energy performance were analysed. Utilizing simulation, the utilize of non-natural sources can be dramatically decreased through substituting for them with the utilize of sustainable natural sources by that means energy saving. Building information modelling has substantiated to be effective in providing maintainability with alternative material’s assessment and earlier decision-making. Furthermore, this study employed an integrated new MCDM model to evaluate the performance of four natural stones for utilize in a nursing home setting.