Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution data more accurately. Different ensembling methods, such as bagging, boosting, and stacking/blending, have been studied adopted extensively in research practice. While bagging boosting focus on reducing variance bias, respectively, stacking approaches target both f...

Abstract Artificial intelligence and machine learning applications are of significant importance almost in every field human life to solve problems or support experts. However, the determination model achieve a superior result for particular problem within wide real-life application areas is still challenging task researchers. The success could be affected by several factors such as dataset cha...

Let T be a unit equilateral triangle, and T1, . . . , Tn be n equilateral triangles that cover T and satisfy the following two conditions: (i) Ti has side length ti (0 < ti < 1); (ii) Ti is placed with each side parallel to a side of T . We prove a conjecture of Zhang and Fan asserting that any covering that meets the above two conditions (i) and (ii) satisfies ∑n i=1 ti ≥ 2. We also show that ...

Evidence-based treatments and preventive interventions in the child and family area have not met with widespread adoption by practitioners. Despite the high prevalence of child behavioral and emotional problems, many parents and families in need are not receiving or participating in services, and when they do, the most efficacious interventions are not what is usually provided. Simultaneously a...