arXiv subjects

R. P. Jagadeesh Chandra Bose

Publications and source records attributed to R. P. Jagadeesh Chandra Bose.

2 recordsLinked to original sources

arXiv2019

Multiperspective Conformance Analysis of Central Venous Catheter Installation Procedure

Training and practice play a key role in a medical students' attainment of surgical procedural skills. It is beyond doubt that good skills correlate with better clinical outcomes and improved healthcare. Timely, holistic, and effective feedback provide a significant impetus to students acquiring skills with precision. In this paper, we analyze the activities performed by students while learning the central venous catheter installation procedure. We perform a holistic analysis, using trace alignment, declarative conformance checking, data visualization, and statistical analysis techniques, at different levels of abstraction on control-flow and time perspectives and provide insights at individual student level as well as across students. These insights can help students discover what they are doing right and where they are not and take corrective steps. Instructors can uncover common patterns and mistakes that students demonstrate and think of interventions in their teaching methodology.

physics.med-ph↗

arXiv2018

Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing

We have recently witnessed tremendous success of Machine Learning (ML) in practical applications. Computer vision, speech recognition and language translation have all seen a near human level performance. We expect, in the near future, most business applications will have some form of ML. However, testing such applications is extremely challenging and would be very expensive if we follow today's methodologies. In this work, we present an articulation of the challenges in testing ML based applications. We then present our solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers. We have developed metamorphic relations for an application based on Support Vector Machine and a Deep Learning based application. Empirical validation showed that our approach was able to catch 71% of the implementation bugs in the ML applications.

cs.SE↗