Recently, symmetric networks have received much attention in various applications. They are a single route for incoming and outgoing network traffic. In symmetric networks, one of the fundamental categories of wide-ranging scheduling problems with several practical applications is the FSSP. Strictly speaking, a scheduling issue is found when assigning resources to the activities to maximize goals. The difficulty of finding solutions in polynomial time makes the flow shop scheduling problem (FSSP) NP-hard. Hence, the utilization of a hybrid optimization technique, a new approach to the flow shop scheduling issue, on symmetric networks is given in the current research. In order to address this issue, each party’s strengths are maximized and their weaknesses reduced, and this study integrates the Ant Colony Algorithm with Particle Swarm Optimization (ACO-PSO). Even though these methods have been employed before, their hybrid approach improves their resilience in a variety of sectors. The ACO-PSO is put to the test by contrasting it with innovative algorithms in the literature. The search space is first filled with a variety of solutions by the algorithm. Using pheromones in the mutual region, the ACO algorithm locally controls mobility. Moreover, the PSO-based random interaction among the solutions yields the global maximum. The PSO’s random interaction among the solutions typically results in the global maximum. The computational research demonstrates that the recommended ACO-PSO method outperforms the existing ones by a large margin. The Friedman test also shows that the average algorithm ranks for ACO and PSO are 1.79 and 2.08, respectively. The proposed method has an average rank of 2.13 as well. It indicates that the suggested algorithm’s effectiveness increased.

Predicting student performance using data mining and learning analysis technique in Libyan Higher Education

Several algorithms strategies based on Rough Set Theory (RST) have been used for the selection of attributes and grouping objects that show similar features. On the other hand, most of these clustering techniques cannot deal tackle partitioning. In addition, these processes are computationally complexity and low purity. In this study, the researcher looked at the limitations of the two rough set based techniques used, Information-Theoretic Dependency Roughness (ITDR) and Maximum Indiscernible Attribute (MIA). They also proposed a novel method for selecting clustering attributes, Maximum mean Attribute (MMA). They compared the performance of MMA, ITDR and MIA technique, using UCI and benchmark datasets. Their results validated the performance of the MMA with regards to its purity and computational complexity.