Developing advanced engineering materials with enhanced mechanical and metallurgical properties has caused non-traditional machining (NTM) processes to emerge as cost-effective and energy-efficient options. Improper settings of the input parameters of those processes may often lead to inferior...

This paper proposes new inverse data envelopment analysis (DEA) models considering input and output efficiency measures for the time substitution problem. We compare our DEA model with existing models in the literature. We show that existing models in the literature overestimate the required...

Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL) research for their architectural advantages. CNN relies heavily on hyperparameter configurations, and manually tuning these hyperparameters can be time-consuming for researchers, therefore we need efficient...