Chaotic quasi-opposition marine predator algorithm for automatic data clustering

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 21, 2025

---

Role of artificial intelligence in enhancing competency assessment and transforming curriculum in higher vocational education

Frontiers in Education, Journal Year: 2025, Volume and Issue: 10

Published: April 28, 2025

The study investigates the competency assessment outcome of AI-driven training, student engagement, and demographic factors. Previous studies have examined these factors individually, but this research integrates them to assess their combined impact on scores. Variables such as scores, gender, vocational training levels were systematically collected following FAIR principles. Python libraries used for cleaning preprocessing dataset; missing values filled outliers handled using Tukey method. use EDA further disclosed strong positive correlations with engagement scores resulting from training. Nonetheless, since it is an observational study, associations must not be taken causal. Inferential statistics - like t -tests ANOVA established by gender level. Machine learning algorithms predict Random Forests showed highest predictive power compared linear regression ( R 2 = 0.68 vs. 0.41). This suggests necessity modeling non-linear relationships in prediction. (ANOVA, -tests) revealed training-level effects. outperformed 0.41), uncovering relationships. KMeans clustering three groups necessitating individualized interventions: Cluster 1 (high AI engagement/low competency) requires skill-building support; (balanced engagement/competency) served ongoing adaptive training; 3 (low engagement/high engagement-fostering strategies. These results highlight importance AI-supported interaction improve attainment. findings practical implications education institutions promoting personalized approaches that are responsive various needs students. Ethical considerations AI-based evaluation, including bias fairness, worthy exploration.

Language: Английский

---

A Multi-Strategy Enhanced Marine Predator Algorithm for Global Optimization and UAV Swarm Path Planning

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 112095 - 112115

Published: Jan. 1, 2024

Language: Английский

---

Mechanical and Civil Engineering Optimization with a Very Simple Hybrid Grey Wolf—JAYA Metaheuristic Optimizer

Mathematics, Journal Year: 2024, Volume and Issue: 12(22), P. 3464 - 3464

Published: Nov. 6, 2024

Metaheuristic algorithms (MAs) now are the standard in engineering optimization. Progress computing power has favored development of new MAs and improved versions existing methods hybrid MAs. However, most (especially algorithms) have very complicated formulations. The present study demonstrated that it is possible to build a simple metaheuristic algorithm combining basic classical MAs, including modifications optimization formulation maximize computational efficiency. (SHGWJA) developed here combines two methods, namely grey wolf optimizer (GWO) JAYA, widely used problems continue attract attention scientific community. SHGWJA overcame limitations GWO JAYA exploitation phase using elitist strategies. proposed was tested successfully seven “real-world” taken from various fields, such as civil engineering, aeronautical mechanical (included CEC 2020 test suite on real-world constrained problems) robotics; these include up 14 variables 721 nonlinear constraints. Two representative mathematical (i.e., Rosenbrock Rastrigin functions) 1000 were also solved. Remarkably, always outperformed or competitive with other state-of-the-art competition winners high-performance all cases. In fact, found global optimum best cost at 0.0121% larger than target optimum. Furthermore, robust: (i) cases, obtained 0 near-0 deviation runs practically converged solution; (ii) optimized 0.0876% design; (iii) function evaluations 35% average cost. Last, ranked 1st 2nd for speed its fastest highly their counterpart recorded

Language: Английский

---