Keynote Speaker

Prof. Sunil Vadera
Computer Science at University of Salford, UK

Sunil Vadera is the Dean of the School of Computing, Science and Engineering at the University of Salford in Greater Manchester, UK. Sunil was Chair of the UK BCS Knowledge Discovery and Data Mining Symposium held in Salford in 2009, A Programme Chair of the IFIP conference on Intelligent Information Processing in 2010, 2012,2016. His research has been published in some of the leading outlets, including the Computer Journal, ACM Transactions on Knowledge Discovery from Data, ACM Computing Surveys, Expert Systems Journal, Foundations of Science, and IEEE Transactions of Power Systems. Sunil was Chair of the British Computer Society Academic Accreditations Committee, that has responsibility for professional accreditation of programmes in the UK, from 2007-2009. He holds a PhD in Computer Science from the University of Manchester, is a Fellow of the BCS and was awarded the BDO best British Indian Scientist and Engineer in 2014 and the Amity Award for Research in 2018 in recognition of his contributions to the field. 

Sunil Vadera has led a number of projects in applying data mining and machine learning for problems in Energy, Finance, and Policy over the last decade, including:
• Developing new models for real time sensor validation of gas turbines
• Data mining of near miss data for the health and safety executive
• Analysis of SMART meters data for British Gas
• A major FP7 funded project on Self-Learning Energy Efficient Buildings and Open Spaces
• Analysing factors affecting children in need and troubled families
• Sub-prime lending aimed at improving financial inclusion
• Data mining for predicting client churn for a major Software House

Speech Title: Research Challenges in Applying Data Mining

Abstract: This presentation will give an overview of his experiences with some of these projects, highlight the lessons learned and outline the future challenges that need to be addressed if Big Data Analytics is going to be successful in addressing regional and global challenges such as managing energy consumption, climate change, finance, health and social inclusion.

Selected publications

• Lomax, S. and Vadera, S. (2017). A Cost-Sensitive Decision Tree Learning Algorithm Based on a Multi-Armed Bandit Framework, The Computer Journal,
Volume 60, Issue 7, 1 July 2017, Pages 941–956

• Nashnush, E., and Vadera, S. (2016). Learning cost-sensitive Bayesian networks via direct and indirect methods, Integrated Computer-Aided Engineering, doi: 10.3233/ICA-160514, available on line at: http://content.iospress.com/articles/integrated-computer-aided-engineering/ica514

• Lomax, S. and Vadera, S.(2013). A survey of cost-sensitive decision tree induction algorithms, ACM Computing Surveys, Vol45, No 2, 35 pages

• Sunil Vadera (2010), CSNL: A Cost-Sensitive Non-Linear Decision Tree Algorithm, ACM Transactions on Knowledge Discovery from Data, Vol 4, No 2, pp1-25

• Ibarguengoyatia, P. Sucar, E. and Vadera, S. (2008). Sensor Validation, in Bayesian Networks, O. Pourret, P. Naim, P. and B. Marcot (Eds), Wiley, pp187- 202.

Prof. Marat Akhmet
Middle East Technical University, Turkey

Marat Akhmet is a professor of mathematics at Middle East Technical University (Ankara, Turkey) known for his research on the chaos and bifurcation theory in differential equations and hybrid systems with applications in physics, neural networks, biology, medicine and economics . Born in Kazakhstan, he studied at Aktobe State University. He received his doctorate in 1984 at Kiev University . He has been awarded a Science Prize of TUBITAK (Turkey, 2015), for best achievments in scientific research. He is an author of four books: "Principles of Discontinuous Dinamical Systems", Springer, 2010, "Nonlinear Hybrid Continuous Discrete-Time Models", Atlantis Press (Springer), 2011, "Neural networks with Discontinuous Impact Activations," Springer, 2014, and "Replication of Chaos in Neural Networks, Economics and Physics", Springer&HEP, 2015. His has solved the Second Peskin conjecture for Integrate-and-fire biological oscillators, has introduced and developed theory of differential equations with piecewise constant argument of generalized type, many aspects of discontinous dynamical systems. The last decade his main subject of research is input-output analysis of chaos and irregular behavior of hybrid neural networks.

Speech Title: Attraction of Li-Yorke Chaos by Retarded SICNNs

Abstract: In the present study, dynamics of retarded shunting inhibitory cellular neural networks (SICNNs) is investigated with Li–Yorke chaotic external inputs and outputs. Within the scope of our results, we prove the presence of generalized synchronization in coupled retarded SICNNs, and confirm it by means of the auxiliary system approach. We have obtained more than just synchronization, as it is proved that the Li– Yorke chaos is extended with its ingredients, proximality and frequent separation, which have not been considered in the theory of synchronization at all. Our procedure is used to synchronize chains of unidirectionally coupled neural networks. The results may explain the high performance of brain functioning and can be extended by specific stability analysis methods. Illustrations supporting the results are depicted. For the first time in the literature, proximality and frequent separation features are demonstrated numerically for continuous-time dynamics. Developments of the results and aplications for neural networks will be discussed.

Prof. Branislav Vuksanovic
School of Engineering, University of Portsmouth, Portsmouth, UK

Branislav Vuksanovic was born in Osijek, Croatia in 1962. He graduated from the University of Belgrade, Serbia with degree in Electrical and Power Engineering. He holds MSc degree in Measurement and Instrumentation from South Bank University, London and a PhD in Active Noise Control from the University of Huddersfield, UK.

Previously, he worked as a Project Engineer for Croatian Electricity Board in Osijek, Croatia. During his academic career he worked as a Research Fellow at Sheffield and Birmingham Universities on Optical Brain Imaging and Medical Video Compression projects. He also worked as a Lecturer at the University of Derby where he was a member of Sensors and Controls Research Group. Currently he works as a Senior Lecturer at the University of Portsmouth, School of Engineering. He has published papers in the field of active noise control, biomedical signal processing and pattern recognition for intrusion detection and knowledge based authentication. He also published one book in Digital Electronics and Microcontrollers field.

Dr Branislav Vuksanovic is a member of IET, ILT and IACSIT. His current research interests are in the application of pattern recognition techniques for power systems and analysis of ground penetrating radar and ECG data.

Speech Title: Review of Some Techniques for Multivariate Data Analysis

Abstract:  The information age has resulted in masses of data in every field. Techniques to analyse this data are therefore becoming more and more important in all branches of engineering. Multivariate data analysis refers to a group of statistical and signal processing techniques and algorithms used to analyse data arising from more than one variable, i.e. it deals with the analysis of multivariable or multidimensional data sets. Common multivariate data analysis approach usually applied to multivariate data is data dimensionality reduction. The main aim of dimensionality reduction is to try and preserve as much of information present in the data whilst at the same time, reducing data dimensions of the original set. This usually makes data easier to understand and process in a more meaningful way. Computational demands are also significantly reduced in case of high-dimensional data. This presentation will review some of the most popular multivariate data analysis and dimensionality reduction techniques used to analyse various types of multivariate data in engineering applications. Principal (PCA) and independent (ICA) component analysis methods will be overviewed in this talk and a modification of PCA algorithm, known as singular spectrum analysis (SSA) then explained in more details. Results achieved by Dr Vuksanovic in applying those techniques during his previous research on various data sets including radar images, energy profiles and biomedical, brain signals will be presented.