Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification t...

Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed provide insights into the GNN decision making process. These are first important steps on way explainability, but generated explanations often hard understand for users that not AI experts. To overcome this problem, we introduc...

This research aimed to analyse about the signs contained in marketing advertisements of Morris Lifestyle, as well meaning sign through symbolic relation between signifier and signified. applied a qualitative descriptive method with data source is Lifestyle advertisement video, used this study are text, images sound. The findings consist found advertisements. There thirteen advertisement, for ex...

What is the problem-solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out cognitive sociological context diagnostic medical sociology regarding limits automation decision-based tasks. Specifically, it presents a practical way evaluating artificiality symptoms signs in encounters, with an emphasis on visualization process doctor-patient relationships. In ...