The 5th International Conference on Information and Education Innovations (ICIEI 2020) will be held in London, United Kingdom during July 26-28, 2020. ICIEI 2020 aims to bring together researchers around the world to exchange their experiences. The conference will be held annually to provide an ideal platform for people to share new ideas, and research results about all aspects of Information and Education Innovations, and discuss the practical challenges encountered and the solutions adopted. It is one of the leading international conferences for presenting novel and fundamental advances in the field.
ICIEI 2020 will feature several plenary speakers, invited speakers as well as peer-reviewed paper presentations. Participants can join the conference by the following three types: 1. Full Paper 2. Abstract 3. Listeners.
Accepted and presented papers would be published into ACM conference proceedings (ISBN: 978-1-4503-7575-7), which will be indexed by Ei compendex, scopus, etc.
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The manuscript is in double-column format, up to10 pages. Manuscripts must be written in English and follow the instructions at Manuscript Formatting and Templates.
To ensure the high quality of the accepted papers, all submissions will be peer-reviewed.Please only submit original material where copyright of all parts is owned by the authors declared and which is not currently under review elsewhere.
Online Submission System:http://confsys.iconf.org/submission/iciei2020
ICIEI 2019, ISBN: 978-1-4503-7169-8, online: https://dl.acm.org/citation.cfm?id=3345094
ICIEI 2018, ISBN: 978-1-4503-6440-9, online: https://dl.acm.org/citation.cfm?id=3234825
Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. Now he is Reader in Modelling and Optimization at Middlesex University, an elected Bye-Fellow at Cambridge University and Adjunct Professor at Reykjavik University (Iceland). He is the Chair of the IEEE CIS Task Force on Business Intelligence and Knowledge Management. He has given many keynote talks at over 20 international conferences such as IEEE Mendel'12 (Czech Republic), BIOMA'12 (Slovenia), EU/ME'14 (Turkey), ICCS'15 (Iceland), SIBGRAPI'15 (Brazil), OIPE'16 (Italy), ISCBI'16 (Switzerland), BDIOT'17 (UK) and HS'17 (Spain). He has published more than 250 articles in peer-reviewed journals and 20 books with over 32000 citations. He has been on the prestigious list of Clarivate Analytics/Web of Science Highly Cited Researchers in 2016, 2017 and 2018.
"Deep Learning, Brain-like Computing & AI2.0"
Short biography: Frank Z. Wang is the Professor in Future Computing and Head of School of Computing (2010-2016), University of Kent, the UK. The School of Computing was formally opened by Her Majesty the Queen. His led school achieved an amazing result in the 2014 UK government REF (Research Excellence Framework): the research intensity was ranked 12th out of over 150 computing departments in the UK. Professor Wang's research interests include brain-like computer, memristor theory and applications, deep learning, cloud computing, big data, and green computing, etc. He has been invited to deliver keynote speeches and invited talks to report his research worldwide, for example at Princeton University, Carnegie Mellon University, CERN, Hong Kong University of Sci. & Tech., Tsinghua University (Taiwan), Jawaharlal Nehru University, Sydney University of Technology, and University of Johannesburg. In 2004, he was appointed as Chair & Professor, Director of Centre for Grid Computing at CCHPCF (Cambridge-Cranfield High Performance Computing Facility). CCHPCF is a collaborative research facility in the Universities of Cambridge and Cranfield (with an investment size of £40 million). Prof Wang and his team have won an ACM/IEEE Super Computing finalist award. Prof Wang is Chairman (UK & Republic of Ireland Chapter) of the IEEE Computer Society and Fellow of British Computer Society.
"Convolutional Neural Networks Challenges"
Abstract: Artificial intelligence and machine learning as its subset are undeniably among the most important developments in the recent history. Classification is one of the common tasks in machine learning used in various fields such as healthcare, security, agriculture, astronomy, and many more. Numerous applications require classification of images where one of the crucial factors that affects classification accuracy is the choice of the features. Image classification was widely studied in the past decades and huge progress was made a few years ago with the introduction of the convolutional neural networks. Convolutional neural networks represent a special class of deep neural networks that are achieving spectacular results in tackling different image classification problems. Building a CNN with the powerful tools that are available nowadays is a rather simple task while the improvement in the classification accuracy compared to the existing methods is significant. Even though it is relatively easy to implement and modify CNNs, finding the optimal configuration and architecture is a challenging task. To achieve the best possible results it is necessary to adjust various hyper-parameters such as the number and type of layers, number of neurons in each layer, kernel size, optimization algorithm, and many others and the optimal CNN for one dataset is not necessarily optimal for the other. Currently, there is no efficient method for tuning CNNs’ hyperparameters and determining its architecture. The usual method for setting CNN’s configuration is by guessing and estimating (guestimating) better values for the hyper-parameters. Since adjusting CNNs’ hyperparameters for the concrete problem represents a hard optimization problem, it can be solved by swarm intelligence algorithms. This process is time consuming but several studies show that adjusting CNNs’ hyperparameters by swarm intelligence algorithms improves the classification accuracy. Another challenge with the CNN models is that automatically extracted features can be used for differencing instances from different classes but they are not easy for understanding and interpretation which is a highly desirable characteristic, especially when CNN is used for tasks such as medical diagnostics or for controlling autonomous vehicles. There is an effort in the scientific community to overcome this problem and to understand features extracted by the CNNs.
Short biography: Milan Tuba is the Vice Rector for International Relations, Singidunum University, Belgrade, Serbia and was the Head of the Department for Mathematical Sciences at the State University of Novi Pazar and the Dean of the Graduate School of Computer Science at John Naisbitt University. He received B. S. in Mathematics, M. S. in Mathematics, M. S. in Computer Science, M. Ph. in Computer Science, Ph. D. in Computer Science from the University of Belgrade and New York University. From 1983 to 1994 he was in the U.S.A. first at Vanderbilt University in Nashville and Courant Institute of Mathematical Sciences, New York University and later as Assistant Professor of Electrical Engineering at Cooper Union School of Engineering, New York. During that time he was the founder and director of Microprocessor Lab and VLSI Lab, leader of scientific projects and theses supervisor. From 1994 he was Assistant Professor of Computer Science and Director of Computer Center at University of Belgrade, from 2001 Associate Professor, Faculty of Mathematics, University of Belgrade, from 2004 also a Professor of Computer Science and Dean of the College of Computer Science, Megatrend University Belgrade. He was teaching more than 20 graduate and undergraduate courses, from VLSI Design and Computer Architecture to Computer Networks, Operating Systems, Image Processing, Calculus and Queuing Theory. His research interest includes nature-inspired optimizations applied to computer networks, image processing and combinatorial problems. Prof. Tuba is the author or coauthor of more than 200 scientific papers and coeditor or member of the editorial board or scientific committee of number of scientific journals and conferences. He was invited and delivered around 60 keynote and plenary lectures at international conferences. Member of the ACM, IEEE, AMS, SIAM, IFNA.
I have worked at the University of Sunderland since 1992, having graduated from the University with a First Class Honours Degree in Combined Science (Computer Science and Physiology). I then went on to complete a PhD in applied artificial intelligence, focussing on the use of neural networks in predictive maintenance, which was awarded in 1996. During the 1990s I established a research centre – the Centre for Adaptive Systems – at the University, which became recognised by the UK government as a Centre of Excellence for applied research in adaptive computing and artificial intelligence. The Centre undertook many projects working with and for external organisations in industry, science and academia, and for three years ran the Smart Software for Decision Makers programme on behalf of the Department of Trade and Industry. I have successfully supervised in PhDs in fields ranging from neural networks, hybrid systems, and bioinformatics through to lean manufacturing, predictive maintenance, and business and maintenance strategies. I went on to become Associate Dean, and then Dean, of the School of Computing and Technology, covering Computer Science and Engineering; in 2008 I became the Dean of the Faculty of Applied Science, and in 2010 Pro Vice Chancellor of the University. I am, and have, been a member of many regional, national and international organisations linked to my own research or professional areas, or on behalf of the University. Since 1996 I have been the Editor-in-Chief of Neural Computing & Applications, an international scientific peer reviewed journal published by Springer Verlag. Prior to entering academia I worked in industry including several years working overseas on major civil and structural engineering projects, developing and implementing new computerised planning systems.
Since 2000, Paolo Terenziani is Full Professor at the Institute of Computer Science of DISIT, Università del Piemonte Orientale, Alessandria, Italy. The research activity of Paolo Terenziani has begun in 1987 and it concerns mainly the field of Artificial Intelligence, and specifically the areas of knowledge representation and automatic reasoning (with particular attention to representation and reasoning with temporal constraints), the fields of Temporal Databases and of Medical Informatics. Regarding these topics Paolo Terenziani has published more than 150 papers in peer-reviewed international journals, books, conference proceedings and workshops (in particular, he has achieved ten publications on the IEEE Transactions of Knowledge and Data Engineering in the last five years). Since 1997, Paolo Terenziani leads the GLARE project, in collaboration with the hospital San Giovanni Battista in Turin, for developing a prototype of a domain-independent software system for the acquisition, representation and execution of clinical guidelines. Since 2015 Paolo Terenziani is in the board of AIME (Artificial Intelligence in Medicine Europe). As early as in 1998, for his research activity, he won the “Artificial Intelligence Prize” from Italian Association for Artificial Intelligence. He has won “distinguished\best” paper awards in several international conferences, including AMIA 2012, Chicago, USA, November 2012 (more than 1000 submissions).