07-30, 18:40–18:50 (UTC), Green Track
Industry 4.0, simply I4.0 or I4, refers to the “Fourth Industrial Revolution” that's the new digital industrial technology for transforming industries into smart/intelligent industries (iIndustry) by connecting machines with intelligent robots and Industrial Internet of Things (IIoT) devices. In this talk, we have addressed and proposed several issues for knowledge mining from Industrial Big Data (iBigData) in Industry 4.0 using Julia programming language.
Industry 4.0 engenders and analysis data across the machines in iIndustry to produce high-quality products at low costs, and changes traditional production relationships among suppliers, producers, and customers. Industry 4.0 amalgamates nine technologies to transform industrial production, which includes: (1) Big Data Analytics, (2) Autonomous Robots/ Robotics, (3) Simulation, (4) Horizontal & Vertical System Integration, (5) Industrial Internet of Things (IIoT), (6) Cybersecurity, (7) Cloud Computing, (8) Additive Manufacturing (such as 3-D printing), and (9) Augmented Reality. I4.0 uses Decision Support Systems (DSS) incorporating with knowledge mining techniques to know what actions need to take in future that help manufacturers to optimise their operations quickly. The fourth revolution ameliorates the industries with intelligent computing fuelled by data with Machine Learning (ML) and Data Mining (DM) technologies. In this talk, we have addressed several issues for knowledge mining process in Industry 4.0 using Julia programming language. Knowledge mining is the process of extracting hidden information/patters from Industrial Big Data (iBigData) to lucid market trends, customer preferences and other information that’s useful to businesses. Industrial Big Data is extremely large that we can't store all the data into a single computer/machine; so, we need more scalable and robust learning approach to deal with iBigData. We have collected the data set with 1067371 instances named “Online Retail II” from UCI Machine Learning Repository (https://archive.ics.uci.edu/) and implemented RainForest and BOAT (Bootstrapped Optimistic Algorithm for Tree construction) learning algorithms using Julia. RainForest and BOAT are basically decision tree (DT) based supervised learning algorithms for classifying Big Data. We have presented a new decision tree merging approach that addresses the repetition and replication problems in tree pruning. Industrial Big Data is multivariate, high-dimensional, noisy, and also the characteristics of data can be changed over the time (e.g. concept drifting in data streaming environment). In this talk, we also discussed the how we can handle the noisy and streaming data; find the most informative training instances, so that we can build a learning model with minimum number of instances. For selecting informative training instances, we have used simple partition-based clustering approach and implemented clustering algorithm in Julia.
Dr. Shatabda is Associate Professor and Undergraduate Program Co-ordinator of Computer Science and Engineering Department.
He achieved his Ph. D degree from the Institute for Integrated and Intelligent Systems (IIIS), Griffith University in 2014. His thesis is titled “Local Search Heuristics for Protein Structure Prediction”. He completed his BSc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology (BUET) in 2007.
Research interest of Dr. Shatabda includes bioinformatics, optimization, search and meta-heuristics, data Mining, constraint programming, approximation Algorithms and graph theory. He has a number of quality publications in both national and international conferences and journals.
He has worked as Graduate Researcher in Queensland Research Laboratory, NICTA, Australia. Prior entering the teaching line he worked as a Software Engineer in Vonair Inc, Bangladesh.
Dr. Dewan Md. Farid is a Postdoctoral staff in the Decision and Information Systems for Production systems (DISP) Laboratory, IUT Lumière – Université Lyon 2, France, and Associate Professor (on leave), Department of Computer Science and Engineering, United International University, Bangladesh. He worked as a Postdoctoral Fellow at the following research groups: (1) Computational Modeling Lab (CoMo), Department of Computer Science, Vrije Universiteit Brussel, Belgium in 2015-2016, and (2) Computational Intelligence Group (CIG), Department of Computer Science and Digital Technology, University of Northumbria at Newcastle, UK in 2013. Dr. Farid was a Visiting Faculty at the Faculty of Engineering, University of Porto, Portugal in June 2016. He holds a PhD in Computer Science and Engineering from Jahangirnagar University, Bangladesh in 2012. Part of his PhD research has been done at ERIC Laboratory, University Lumière Lyon 2, France by Erasmus-Mundus ECW eLink PhD Exchange Program. He has published 84 peer-reviewed scientific articles, including 28 journal papers in the field of machine learning, data mining, and big data. Dr. Farid received the following awards: (1) JuliaCon 2019 Travel Award for attending Julia Conference at the University of Maryland, Baltimore, USA, and (2) United Group Research Award 2016 in the field of Science and Engineering. He received a2i Innovation Fund of Innov-A-Thon 2018 (Ideabank ID No.: 12502) from a2i-Access to Information Program – II, Information and Communication Technology (ICT) Division, Government of the People's Republic of Bangladesh. Dr. Farid received the following Erasmus Mundus scholarships: (1) LEADERS (Leading mobility between Europe and Asia in Developing Engineering Education and Research) in 2015, (2) cLink (Centre of excellence for Learning, Innovation, Networking and Knowledge) in 2013, and (3) eLink (east west Link for Innovation, Networking and Knowledge exchange) in 2009. Dr. Farid also received Senior Fellowship I, and II award by National Science & Information and Communication Technology (NSICT), Ministry of Science & Information and Communication Technology, Government of Bangladesh respectively in 2008 and 2011. He is a member of IEEE.
A. SEKHARI Seklouli is currently member of DISP laboratory. Aicha Sekhari Seklouli is a co-leader of “System Lifecycle Modeling and Optimization” axis in DISP and a Head of the RTI knowledge Transfer and innovation pole. Her research activities deal with Product and Production Sustainability, including Product Life cycle Management and Supply Chain Management. She co-advice PhDs AND Masters Students in these fields. Dr Sekhari Seklouli was technical manager of EASYIMP FP7 FoF ICT NMPB, working on the development of an MBSE method for smart Product. As a member of EcoSD (Eco Conception de système durable) research and industry association, She has many contribution in the field of Eco-PLM and Suitanaible product development. Dr. Sekhari Seklouli has been and currently coordinator/member of several international research and collaboration projects: FP7 ICT FI-PPP FITMAN, Erasmus mundus (eLink, cLink, eTourism, Fusion), Hubert Curien, ARC8, ...
A. SEKHARI Seklouli teaches in the fields of Production Management , Methods and Tools for Quality Management and Norms for Postgraduat and undergrad degrees.
A SEKHARI Seklouli was a head of Health, Safety and Environment (HSE) department. She received her Engineering Degree in Electronic in 1994 from Algerian University of Engineering Science in Setif. After three years of industrial experiences in Engineering maintenance, she joined INSA Lyon for her M. Sc. (DEA) degree in 1999. She obtained her Ph.D degree in Production System from University of Joseph Fourier of Clermond Ferrand in France in 2004. Dr. Sekhari Seklouli is currently collaborating on various EU and International teaching Projects (FSP; Transport II; ...).
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