Association rule mining plays a critical role in uncovering item correlations and hidden patterns within transactional data, particularly in e-commerce environments. Despite the widespread use of Apriori and FP-Growth algorithms, few studies offer a statistically rigorous, tool-based comparison of their performance on real-world e-commerce data. This paper addresses this gap by evaluating both algorithms in terms of execution time, memory consumption, rule generation volume, and rule strength (support, confidence, and lift). Implementations in RapidMiner and an analysis through SPSS establish statistically significant performance differences, particularly under varying support thresholds. Our findings confirm that FP-Growth consistently outperforms Apriori for large-scale datasets due to its ability to bypass candidate generation, while Apriori retains pedagogical and small-scale relevance. The study contributes practical guidance for data scientists and e-commerce practitioners choosing suitable rule-mining techniques based on their data size and performance constraints.

This paper presents an optimized method to reduce the points number to be used in order to identify a person using fuzzy fingerprints. Two fingerprints are similar if n out of N points from the skin are identical. We discuss the criteria used for choosing these points. We also describe the properties of fuzzy logic and the classical methods applied on fingerprints. Our method compares two matching sets and selects the optimal set from these, using a fuzzy reasoning system. The advantage of our method with respect to the classical existing methods consists in a smaller number of calculations.

This study proposes a hierarchical communication framework for drone swarms designed to enhance security and operational efficiency. Leveraging elliptic curve cryptography and space quanta concepts, the model ensures continuous authentication and risk assessment of participating entities. Experimental results demonstrate the framework’s effectiveness in mitigating security risks, achieving reliable communication even in adverse conditions. Key findings include significant improvement in threat detection accuracy and reduced computational overhead, validating the model’s applicability for real-world drone swarm operations. These contributions establish a robust foundation for secure and resilient drone coordination.

Computing systems grouped in subnets use distributed security models, in general, by creating session keys based on the Diffie–Hellman model, and calculating the necessary parameters for this, on each of the systems. In the particular case of a network of devices heterogeneous in terms of computing power, such as IoT, the modeling of a security system of the entire structure will have to take into account the fact that some devices have a very low computing power. In this sense, starting from the study of some general models, used in structures of this type, an integrated structure was developed to secure communications and test certain vulnerable components, to calculate a degree of risk that they are maliciously intended. The system was developed with a customized mathematical model, a scheme for propagation and management of cryptographic parameters and a test in a real environment by creating the algorithmic model and implementing it within a structure of a beneficiary.