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Prof.Abir Jaafar Hussain Academic RankProfessor


  • Professor of Machine Learning

Research Interests:

  • Machine learning Data Analysis Image and Signal Processing


• Ph.D. in Medical Image and Signal Processing, 2000, Control System Center, University of Manchester (UMIST), UK Thesis: Polynomial Neural Networks and Applications to Image Compression and Signal Prediction • MSc. In Control and Information Technology, 1997, Control System Center, University of Manchester (UMIST), UK Dissertation: Fractal Simulation of Material Damage Accumulation in Cellulosic Fibres • B.S.Eng. (Hons) in Electronic Computer Systems, 1st class degree, 1995, Department of Electronic and Electrical Engineering, University of Salford, UK
• Visiting professor at CentralSupélec, France, November 2014- 2017 • Visiting Researcher at Tongji University, China, 24th of August 2015 – 29th of August 2015 • 2014-2018, Reader in Medical Image Analysis and Signal Processing, Liverpool John Moores University • 2001-2014, Senior Lecturer, Liverpool John Moores University
Member IET, 1993- present Member INNS 2014- present FHEA (Follow of Higher Education Academy), 2014- present
• LivingLens- LJMU KTP (PI), “The analysis of LivingLens data Using Data Science Approaches”, £318,000 • Royal Academy of Engineering, Newton Research Collaboration Programme- RCP1617/5/67, (PI), “A Data Science Approach for the Detection of Variable Light Sources in Uneven Time-Domain Astronomy”, (2016-2017), £6000 WarnDry Phase 2 SBRI (CI): “The development of prediction system for the detection of bed wetting in children”, (2015- 2016), this is a £1,000,000 funding with £102,775 Liverpool John Moores University share.
Selected Publications [1]. R Al-Shabandar, A Jaddoa, P Liatsis, AJ Hussain, (2021), “A deep gated recurrent neural network for petroleum production forecasting”, Machine Learning with Applications, 3, 100013. [2]. W Khan, A Hussain, SA Khan, M Al-Jumailey, R Nawaz, P Liatsis, (2021), “Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching”, Royal Society Open Science, 8 (1), 201823. [3]. JK Alwan, AJ Hussain, DH Abd, AT Sadiq, M Khalaf, P Liatsis, (2021), “Political Arabic Articles Orientation Using Rough Set Theory with Sentiment Lexicon”, IEEE Access. [4]. R. Nooripour, S. Hosseinian, A. J. Hussain, M. Annabestani, A. Maadal, L. E Radwin, P. Hassani-Abharian, N.G. Pirkashani, A. Khoshkonesh, 2021, “How Resiliency and Hope Can Predict Stress of Covid-19 by Mediating Role of Spiritual Well-being Based on Machine Learning”, Journal of religion and health, pp. 1-16. [5]. H Alaskar, AJ Hussain, W Khan, H Tawfik, P Trevorrow, P Liatsis, Z Sbaï, 2020, “A data science approach for reliable classification of neuro-degenerative diseases using gait patterns”, Journal of Reliable Intelligent Environments, 6 (4), pp. 233-247. [6]. Wasiq Khan, Abir J. Hussain, Haya Alaskar, Thar Baker, Fawaz Ghali, A Dhiya, Ahmed Al-Shamma'a, 2020,” Prediction of Flood Severity Level via Processing IoT Sensor Data Using a Data Science Approach” IEEE Internet of Things Magazine, 3 (4), pp. 10-15. [7]. N. Kleanthous, A. J. Hussain, W. Khan, P. Liatsis, (2020) “A new machine learning based approach to predict Freezing of Gait”, Pattern Recognition Letters, Volume 140, Pages 119-126,
I have supervised 22 PhD and MPhil students successfully. I am the director of Study for 11 of them.
Medical Image Edge Direction and Orientation Detecting Method, Inventors (Ali Al-Fayadh, Dhiya Al-Jumeily, Abir Jaafar Hussain), Pub. No. (WO/2008/119939), International Filing Date (26.03.2008), IPC (H04N 7/26), G06T 5/00 (2006.01).
• over 200 refereed journal and conference publications • 1 patents • 10 Chapters • 9 book Chapters • 20 Keynote speeches in national and international conferences and workshops • General chair of the IEEE technical sponsored conference series of Development of E System Engineering • Editor in Elsevier Neural Networks journal and MPDI Sensors journal • 22 successfully supervised PhD
I believe that respect is the way forward for both students and lectures. Research Embedded teaching will allow the students to learn the latest research and when Engineering programmes are linked with industry they will provide sharp engineers with the WoW (word of work) factor .
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