14th International IEEE (Tech Co-sponsor) Conference on Software, Knowledge, Information Management and Applications

(With The International Workshop on University and Digital Sustainability)

02-04 December 2022, CamTech University, Phnom Penh, Cambodia
(www.skimanetwork.org and www.camtech.edu.kh)

Prof. Dr. Dewan Md. Farid

  • Big Data Analytics Employing Machine Learning: Challenges and Issues

    Prof. Dr. Dewan Md. Farid is a Professor in the Department of Computer Science and Engineering at United International University, Bangladesh. He is an IEEE Senior Member. He worked as a Postdoctoral Fellow/Staff at the following research labs/groups: (1) Computational Intelligence Group (CIG), Department of Computer Science and Digital Technology, University of Northumbria at Newcastle, UK in 2013, (2) Computational Modelling Lab (CoMo) and Artificial Intelligence Research Group, Department of Computer Science, Vrije Universiteit Brussel, Belgium in 2015-2016, and (3) Decision and Information Systems for Production systems (DISP) Laboratory, IUT Lumière – Université Lyon 2, France in 2020. Prof. 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 94 peer-reviewed scientific articles, including 29 journal papers in the field of Machine Learning, Data Mining, Data Science and Big Data. Prof. 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. Prof. Farid received the following Erasmus Mundus scholarships: (1) LEADERS (Leading mobility between Europe and Asia in Developing Engineering Education and Research) to undertake a staff level mobility at the Faculty of Engineering, University of Porto, Portugal in 2015, (2) cLink (Centre of excellence for Learning, Innovation, Networking and Knowledge) for pursuing Postdoc at University of Northumbria at Newcastle, UK in 2013, and (3) eLink (east west Link for Innovation, Networking and Knowledge exchange) for pursuing Ph.D. at University Lumière Lyon 2, France in 2009. Prof. Farid also received Senior Fellowship I and II awards by National Science & Information and Communication Technology (NSICT), Ministry of Science & Information and Communication Technology, Government of the People’s Republic of Bangladesh respectively in 2008 and 2011 for pursuing Ph.D. at Jahangirnagar University


    Data is growing exponentially. Only, Google engenders 10 exabytes of data every day. There will be around 175 zettabytes of data by 2025. Big Data is known as the 3Vs: Volume, Variety, and Velocity. Extracting knowledge/patterns from Big Data is a complex, challenging, and time consuming task as Big Data is very large, high-dimensional, complex, multi-variate and streaming. Big Data Analytics (BDA) examples includes discovering consumer shopping habits, personalised marketing, personalised health plans, live road mapping for autonomous vehicles, etc. In this talk, we will address the following Big Data challenges: (1) how to store Big Data? (2) how to process Big Data? (3) how faster process the Big Data? Nowadays, advanced Machine Learning algorithms e.g. Deep Learning become very popular for Big Data mining. This talk will present the challenges and issues for BDA applying Machine Learning techniques. We will discuss about incremental/adaptive learning for BDA. Since the presence of noisy instances and less important features in data may cause overfitting. So, we will discuss the techniques to remove the noisy troublesome instances from Big Data and selecting informative subset of features from original high-dimensional features. Concept drifting in Big Data is one of the common issues as data concept can be changed over the time. So, we will discuss novel class detection and classification process. Finally, the talk will be concluded by discussing multi-class imbalanced Big Data classification.

    General Terms: Adaptive Learning, Big Data, Deep Learning, Noisy Data, Machine Learning.

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Important Dates

  • Special session and tutorial proposal :
    10 October 2022
  • Paper Submission Deadline :
    10 October 2022
  • Notification of Paper Acceptance :
    25 October 2022
  • Camera Ready Paper Deadline :
    24 November 2022
  • Conference Date :
    2-4 December 2022
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