CSIS Seminar
Posted: Fri Sep 03, 2021 3:03 pm
Department of Computer Science and Information Systems
Date: September 4, 2021
Day: Saturday
Time: 11:00 A.M. – 12:00 P.M.
Google meet link: https://meet.google.com/ksj-jtbj-zwr
The details of the seminars are as follows:
Seminar-1
Speaker: Mukesh Kumar Rohil
Title: A Framework for Mutation Testing of Machine Learning Systems
Authors: Raju Singh and Mukesh Kumar Rohil
Conference: The 33rd International Conference on Software Engineering and Knowledge Engineering (SEKE 2021); July 01-10, 2021; Pittsburgh, Pennsylvania, USA (Held online)
Abstract: In this paper, we provide an insight journey of Testing of Machine Learning Systems (MLS), its evolution, current paradigm, and we propose a machine learning mutation testing framework with scope for future work. Machine Learning (ML) Models are being used even in critical applications such as Healthcare, Automobile, Air Traffic control, Share Trading, etc., and failure of an ML Model can lead to severe consequences in terms of loss of life or property. To remediate this, the ML community around the world, must build highly reliable test architectures for critical ML applications. At the very foundation layer, any test model must satisfy the core testing attributes such as test properties and its components. These attributes should come from the software engineering discipline but the same cannot be applied in as-is form to the ML testing and in this paper, we explain why it is challenging to use Software Engineering Principles as-is when testing any MLS.
Seminar-2
Speaker: Mukesh Kumar Rohil
Title: Visualization and Predictive Analysis of Key Performance Indicators for Supply Chain Management
Authors: Mukesh Kumar Rohil and Aditya Saxena
Conference: 37th International Business Information Management Association Conference (IBIMA Conference); May 30-31, 2021; Cordoba, Spain (Held online)
Abstract: Business Intelligence can be used to provide valuable insights into the working of a supply chain. We are advocating to use Parallel Coordinates for visualizing Key Performance Indicators of a Supply Chain. Further, we have used Multiple Linear Regression in Predictive Analytics to predict the value of Gross Margin Return on Investment based on Cash Conversion Cycle and Days Sales Outstanding. The model has been trained based on 10 years of simulated data and further predictions are made on Gross Margin Return on Investment. Predictive analysis is implemented to forecast the returns on investment which will form the basis of business decisions concerning investment on inventory in the subsequent time cycles. We have implemented Parallel Coordinates Plot for visualization of the Supply Chain Key-Performance Indicators for better side-by-side visualization and comparison of the trends, using the R Language.
All are cordially invited to attend.
Best RegardsSudeept Mohan, HOD, CSIS
Vandana Agarwal, Seminar Coordinator