The banking sector plays a key role in the economic, social, and political development of a country. The study of the financial performance of banks is essential for investors, creditors, and other interested parties. The aim of this research was to rank the second-tier banks in Albania by financial performance using a fuzzy multi-criteria decision model (fuzzy MCDM). For the ranking of banks, eight financial criteria were taken into account during the years 2020, 2021, and 2022 for 11 banks in the Albanian banking sector. Based on the selected indicators, a decision-making model was created. The Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) methods were used in this research. The results of the FAHP method showed that the most important indicators are Equity and EBT. The results of the TOPSIS method showed that Banka Kombëtare Tregtare (BKT) had the best indicators for the observed years. The contribution of this research is in understanding the financial operations of banks in Albania.

Project risk management is one of the project management knowledge areas that identifies, analyzes and deals with project risks. One of the important factors influencing the decision-making of a project-based organization is the level of risk tolerance of organization. This study focuses on the factors affecting the level of risk tolerance of project-based organizations. For this purpose, in the first step, the potential factors affecting risk tolerance are extracted by reviewing the related literature. In the next step, the factors affecting the organization's risk tolerance level are identified by using the Fuzzy Delphi method in several steps. The most effective factors are identified by experts? judgment using a questionnaire. Then, the relationships between these factors are determined by using the Interpretive Structural Modeling (ISM) method. The intensity of these relationships and the intensity of the effect of the factors are investigated by using the Fuzzy DEMATEL method. Finally, the factors are ranked based on their weights by utilizing the Fuzzy DEMATEL method. In this study, 13 external and internal factors are ranked using questionnaires based on the experts? opinions. Four external factors include political conditions and international relations, the conditions of the capital markets such as stock market, investment security and government support. These factors have significant influence on the other factors as well as the project-based organization. The findings of this study direct project managers to accurately identify the risk tolerance level of the key project stakeholders in order to efficiently plan and implement project risk management and achieve project goals.

Selecting a tractor is one of the most complex investment decisions an agricultural producer faces. There are numerous types of tractors on the market, each differing in technical, economic, and ecological characteristics. The aim of this research is to demonstrate how multi-criteria analysis methods can aid in this decision-making process, using a practical example of selecting an optimal tractor for the Myzeqe area in Albania. In this study, a decision-making model was developed based on a hybrid fuzzy methodology, combining the fuzzy LOPCOW (Logarithmic Percentage Change-Driven Objective Weighting) and fuzzy MABAC (Multi-Attributive Border Approximation Area Comparison) methods. The findings show that the determination of criterion weights is less crucial, with the T15 tractor exhibiting the best overall indicators. This research primarily contributes to developing a methodology in agriculture that enhances production outcomes.

Smart technologies are increasingly used in agriculture, with drones becoming one of the key tools in agricultural production. This study aims to evaluate affordable drones for agricultural use in the Posavina region, located in northern Bosnia and Herzegovina. To determine which drones deliver the best results for small and medium-sized farms, ten criteria were used to evaluate eight drones. Through expert evaluation, relevant criteria were first established and then used to assess the drones. The selected drones are designed for crop monitoring and are priced under EUR 2000. Using the fuzzy A-SWARA (Adapted Step-wise Weight Assessment Ratio Analysis) method, it was determined that the most important criteria for drone selection are control precision, flight autonomy, and ease of use, all of which are technical attributes. The fuzzy MARCOS method revealed that the best-performing drones are also the most affordable. The drones D5, D4, and D8 demonstrated the best results. These findings were confirmed through comparative analysis and sensitivity analysis. Their features are not significantly different from those of more expensive models and can, therefore, be effectively used for smart agriculture. This study demonstrates that drones can be a valuable tool for small farms, helping to enhance agricultural practices and productivity.

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