Dr Farrukh Hassan

Dr Farrukh Hassan

  • Lecturer
Department of Data Science and Artificial Intelligence
Google Scholar ORCID ResearchGate Web of Science
SDGs Focus
Good Health and Well Being
Quality Education
Industry, Innovation and Infrastructure
Sustainable Cities and Communities

Biography

Dr. Farrukh Hassan, a Ph.D. in IT, is currently working as lecturer in the Department of Data Science and Artificial Intelligence at Sunway University, Malaysia. He has contributed significantly to cutting-edge projects during his doctoral work centered on refining heuristic algorithms to estimate the noise threshold in acoustic emission signals. He earned an MS in Computer Science from Uppsala University, Sweden, where he spearheaded the automation of library systems through innovative Near Field Communication technologies. His practical expertise is underscored by a rich academic portfolio, having published research articles in reputable journals, solidifying his impactful contributions to the field of IT.

Academic & Professional Qualifications

  • PhD IT, Universiti Teknologi Petronas, Malaysia (2023)
  • M.Sc. Computer Science, Uppsala University, Sweden (2015)
  • M.I.T. Gomal University, Dera Ismail Khan, Pakistan (2004)

Research Interests

  • Artificial Intelligence
  • Machine Learning
  • Blockchain
  • Structural Health Monitoring

Teaching Areas

  • Artificial Intelligence
  • Data Mining
  • Database
  • Web Development

Courses Taught

  • Introduction to Data Science using R
  • Data Analytics using Python
  • Big Data Analytics
  • Introduction to Computer Applications
  • ICT in Environment
  • Web Applications and Development
  • Introduction to Networking
  • Theory of Automata

Notable Publications

  1. Hassan, Farrukh, et al. "State-of-the-art review on the acoustic emission source localization techniques." IEEE Access 9 (2021): 101246-101266.
    Link:  https://ieeexplore.ieee.org/abstract/document/9481912/ 
  2. Khan, Umair, et al. "Flow Regime Identification in Gas-Liquid Two-Phase Flow in Horizontal Pipe by Deep Learning." Journal of Advanced Research in Applied Sciences and Engineering Technology 27.1 (2022): 86-91.
    Link:  https://akademiabaru.com/submit/index.php/araset/article/view/4571 
  3. Hassan, Farrukh, et al. "A Hybrid Particle Swarm Optimization-Based Wavelet Threshold Denoising Algorithm for Acoustic Emission Signals." Symmetry 14.6 (2022): 1253.
    Link:  https://www.mdpi.com/2073-8994/14/6/1253 
  4. Hassan, Farrukh, et al. "Clustering-Based Quantitative Evaluation Using Acoustic Emission Waveforms for Corrosion Detection." Journal of Hunan University Natural Sciences 48.7 (2021).
    Link:  http://www.jonuns.com/index.php/journal/article/view/643 
  5. Hassan, Farrukh, et al. "AE Source Localization for Oil & Gas Pipelines using Machine Learning Technique." 2021 International Conference on Computer & Information Sciences (ICCOINS). IEEE, 2021.
    Link: https://ieeexplore.ieee.org/abstract/document/9497222/ 
  6. Saboor, Abdul, et al. "Design pattern based distribution of microservices in cloud computing environment." 2021 International Conference on Computer & Information Sciences (ICCOINS). IEEE, 2021.
    Link:  https://ieeexplore.ieee.org/abstract/document/9497188
  7. Rimsan, Mohamed, et al. "COVID-19: a novel framework to globally track coronavirus infected patients using blockchain." 2020 International Conference on Computational Intelligence (ICCI). IEEE, 2020.
    Link:  https://ieeexplore.ieee.org/abstract/document/9247659 
  8. Saboor, Abdul, et al. "Containerized microservices orchestration and provisioning in cloud computing: A conceptual framework and future perspectives." Applied Sciences 12.12 (2022): 5793.
    Link:  https://www.mdpi.com/2076-3417/12/12/5793