| Lecturer Profile |
Anand R. Prasad, Dr. & ir. (MScEngg) from Delft University of Technology, The Netherlands, is Chief Advanced Technologist, Executive Specialist, at NEC Corporation, Japan, where he leads the mobile communications security activity. Anand is the chairman of 3GPP SA3, founder chairman of the GISFI Security & Privacy group and member of the governing body / council member of GISFI and TSDSI. He has 20+ years of experience in networking, wireless and mobile communications product design, development and business development in companies around the globe.
Anand has published 6 books and authored 50+ peer reviewed papers in international journals and conferences. Two of the books he co-authored on security are “Security in Next Generation Mobile Networks SAE/LTE and WiMAX”, published by River Publishers, and “Security for Mobile Networks and Platforms”, published by Artech House. He is a series editor for standardization book series and editor-in-chief of the “Journal of ICT Standardization” published by River Publishers. He is recipient of the 2014 ITU-AJ “Encouragement Award: ICT Accomplishment Field” and the 2012 (ISC)² “Asia Pacific Information Security Leadership Achievements (ISLA) Award as a Senior Information Security Professional”. Anand is a certified information systems security professional (CISSP). |
| Lecture Abstract |
Prediction models and their efficient evaluation arise as an important and timely direction of fundamental and applied research. However, there are no ideal models. It is impossible to envision a situation where any model can deliver an ideal fit to experimental data and needless to say that the quality of any model is of paramount importance to any application. To offer a sound and realistic evaluation of the quality of constructed models, it is legitimate and intuitively appealing to admit that the prediction results come in a certain non-numeric way. The talk focus on the concepts of predictive models with prediction results being realized in the form of information granules. It introduces a comprehensive algorithmic setting as well as a detailed quantification of quality of information granules. Besides, some well-known constructs of statistically-guided prediction in linear regression are briefly reviewed. The generic idea of prediction intervals is generalized to embrace information granules of prediction including constructs. The talk also introduces a concept of a granular parameter space and a granular output space yielding a granular nature of prediction. |
