Prioritizing image processing and big data analytics factors for software development using DEMATEL-choquet method in bipolar complex fuzzy context

Bipolar complex fuzzy set (BCFS) theory is a newly developed framework for addressing real-world decision-making (DM) problems that involve ambiguous, uncertain, and dual-valued information. The theory is based on bipolar fuzzy set (BFS) and complex fuzzy set (CFS), which enable the association of both positive membership degree (PMD) and negative membership degrees (Ne-MD) within the unit square of the complex plane. This paper considers the use of Aczel-Alsina (AA) operational laws in the BCFS setting to introduce two new aggregation operators (AOs): bipolar complex fuzzy Choquet integral Aczel-Alsina averaging (BCFCIAAA) and order averaging (BCFCIAAOA). These operators are studied analytically in terms of essential properties, such as idempotency, monotonicity, and boundedness. To demonstrate the superiority of the proposed approach, we develop a hybrid DM model that combines the decision-making trial and evaluation laboratory (DEMATEL) method with the Choquet integral (CI) within the BCFS framework. This model is applied to a real-world case study focused on prioritizing critical factors influencing the integration of image processing and big data analytics (IP-BDA) into software development. The causal effects indicate that feature extraction, automation and efficiency, as well as object detection, are critical causal factors, whereas image enhancement, security, and segmentation are dependent effects. These findings offer actionable insights for decision-makers (DMKs), emphasizing the importance of intelligent feature handling and scalable analytics workflows in designing adaptive and high-performance software systems.