Energy sustainability is described as an ability to get the energy supplies without diminishing the ability of future generations to provide themselves with any energy. Preceded by the mentioned notion, this paper will be focused on bipolar hesitant fuzzy soft sets (BHFSS) related to the problem of energy sustainability. It actually was a proposed new mathematical method able to conquer ambiguity and uncertainty while determining the different choices in energy-related decisions. In this way, it lead to more informative and better choices to be made, thus leading to the utilization of sustainable energy systems. The paper introduced basic operations and comparison rules for BHFSS. Furthermore, algebraic norms-based aggregation operators were proposed to make the model more robust and flexible so that it was adaptable to a wide range of energy sustainability decisions. Main characteristics of the BHFSS aggregation operators were discussed in detail. Last but not least, this paper also provided a comparison of the BHFSS-based approach with one of the most popular multi-criteria decision-making (MCDM) approaches known as compromise solution (CoCoSo). This comparison confirmed how BHFSS can control for uncertainty and how it can reflect preferences in a mapped way, which afforded it strengths in uses like choosing renewable power and strategy for lowering $C{O}_2$ emissions.

Association rule mining (ARM) is a technique for discovering meaningful associations within databases, typically handling discrete and categorical data. Recent advancements in ARM have concentrated on refining calculations to reveal connections among various databases. The integration of shuffled frog leaping optimization (SFLO) processes has played a crucial role in this pursuit. This paper introduces an innovative SFLO-based method for performance analysis. To generate association rules, we utilize the apriori algorithm and incorporate frog encoding within the SFLO method. A key advantage of this approach is its one-time database filtering, significantly boosting efficiency in terms of CPU time and memory usage. Furthermore, we enhance the optimization process's efficacy and precision by employing multiple measures with the modified SFLO techniques for mining such information.The proposed approach, implemented using MongoDB, underscores that our performance analysis yields notably superior outcomes compared to alternative methods. This research holds implications for fruit shape database mining, providing robust support for fruit class classification.