Sharif Digital Repository

Despite the significant progress of machine learning models in the health domain, current advanced methods usually produce non-transparent and black-box models, and for this reason, they are not widely used in medical decision-making. To address the issue of non-transparency in black-box models, interpretable machine learning models have been developed. In the health domain, counterfactual scenarios can provide personalized explanations for predictions and suggest necessary changes to transition from an undesirable outcome class to a desirable one for physicians. The aim of this study is to present an interpretable machine learning framework in the health domain that, in addition to having... 

Surface Active Agents/surfactant have a strong tendency to adsorb at the interface between two immiscible fluids, due to the existence of hydrophobic portions in their molecules’ structure. Accumulation of these molecules on the liquid interface changes the intermolecular forces and causes the interfacial tension to decrease. Production of drops of the same size at a specified rate and controlling their movement’s speed are among the most important factors effective in the efficiency of processes associated with liquid drops, which could profoundly be influenced by the presence of a small amount of a surface active agent. In this study, the effects of surface active agents on the growth,...