An innovative decision-making approach for unmanned aerial vehicle software selection using bipolar fuzzy aggregation operators

Unmanned aerial vehicle (UAV) technology is functioning as a substitute for numerous remote sensing applications, particularly those requiring high-resolution satellite imaging, which is undergoing a significant transformation. This is especially beneficial in areas with persistent cloud cover, like the North East, as it facilitates the operation of UAVs in those specific areas. The North Eastern Space Applications Centre (NESAC) is seeking data processing and analysis software capable of handling the photographs and videos captured by UAVs and supporting the most recent operating system version. Adoption of technology involves a complicated and unpredictable decision-making process. Bipolar fuzzy sets are useful in decision-making because they provide an elegant representation of uncertainty by permitting both positive and negative membership degrees at the same time. In this study, we define ordered weighted averaging, ordered weighted geometric, dynamic ordered weighted averaging, and dynamic ordered weighted geometric operators within the context of a bipolar fuzzy setting. Additionally, we delineate several essential characteristics of these operators. We demonstrate a systematic approach to resolve MADM issues. Furthermore, we effectively implement these novel methodologies to address the MADM challenge pertaining to the identification of the most auspicious data processing and analysis software intended for UAVs. In the end, we perform a comparative analysis to demonstrate the viability and applicability of the suggested methodologies in contrast to established approaches.