A new publication from Bielefeld University sets a benchmark in optimization research. Together with an international team, Professor Michael Römer from the Faculty of Business Administration and ...

Foundational optimization algorithms are the core driving force behind deep learning, evolving from early stochastic gradient descent (SGD) to the widely adopted Adam family. However, as the scale of ...

Traditional approaches to analytical method optimization (e.g., univariate and “guess-and-check”) can be time-consuming, costly, and often fail to identify true optima within the parameter space.

ABSTRACT: In the field of machine learning, support vector machine (SVM) is popular for its powerful performance in classification tasks. However, this method could be adversely affected by data ...

Abstract: Due to without considering the reliability of intermittent distribution power, and network load distribution is unreasonable. The planning effect of traditional active distribution network ...

Abstract: Image Segmentation based on multilevel thresholding using non-extensive (non-additive) entropy based techniques is challenging, and the optimal choice of thresholds is an effective approach ...