This study aims to develop a novel Multi Criteria Decision Making (MCDM) methodological model for use as a decision support tool in industrial machine selection problems. To this end, a new integrated MCDM model consisting of Logarithmic normalization and Standard Deviation (LOGSTA), Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), Logarithmic Decomposition of Criteria Importance (LODECI), and Evaluation by Distance from Ideal Solution of Alternatives (EDISA) methods has been designed for a real-world lathe selection problem faced by a manufacturing company in Turkiye. The criterion weights obtained according to the three methods (LOGSTA, LOPCOW, and LODECI) were combined, and the combined criterion weights were transferred to the EDISA method to create an alternative lathe ranking. According to the combined criterion weights, the criterion with the highest importance level was failure frequency (C1), while the criterion with the lowest importance level was active working time (C5). According to the ranking obtained as a result of transferring the combined criterion weights to the EDISA method, the lathe with the highest performance was determined to be Doosan PUMA VT 900 (A2), while the lathe with the lowest performance was determined to be Doosan Puma 300 LM (A1). The comparison analysis shows that the EDISA method produces the same rankings as the ARAS, COPRAS, and RAWEC methods. It is believed that this framework enables manufacturing managers to compare acquisition cost, reliability, operating characteristics, and resale value within a transparent decision process.

In conditions where information and communication technologies (ICT) dictate the “rules” of the market, the strong promotion and development of innovation-oriented small and medium-sized enterprises (SMEs) are essential. The transition from a traditional, linear system of waste management and fleet management in utility companies to a digital and circular-oriented system represents not only a significant challenge

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.

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.

: 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.