Pairing conceptual modeling with machine learning

Machine learning algorithms in air quality modeling

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...

High-Level Student Modeling with Machine Learning

We have constructed a learning agent that models student behavior at a high level of granularity for a mathematics tutor. Rather than focusing on whether the student knows a particular piece of knowledge, the learning agent determines how likely the student is to answer a problem correctly and how long he will take to generate this response. To construct this model, we used traces from previous...

Modeling delay-based PUFs with Machine Learning

Physical Unclonable Functions (PUFs) are well studied mechanisms to establish keys in vulnerable devices and have many commercial applications. However, weaknesses in specific types of PUFs allow modeling using different methods. Modeling attacks on delay based PUFs will be presented using several Machine Learning (ML) methods that best fit the PUF model. The challenge-response pairs (CRPs) wil...

Modeling Language for Machine Learning

For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach. We sho...

Machine Learning in User Modeling