A multivariate polynomial regression was used to fit the relationship between average energy consumption ECavg2 and the two variables: peak computing power Rpeak and energy price index PI. The ...

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This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian and stationary on ...

On the other hand, polynomial regression is a useful technique when the pattern of the data indicates a nonlinear relationship between the dependent and independent variables. Moreover, higher-degree ...

Kim, Y. and Oh, H. (2021) Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP. Materials, ...

The integration of fuzzy graph theory with polynomial regression in this context is a pioneering step, offering valuable insights into the structural attributes of dihedral groups. This research goes ...

While the distinction between computationally infeasible and feasible problems has classically been “NP-hard vs. polynomial-time solvable,” in the big data era it becomes “polynomial-time vs. (quasi-) ...