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Distributed Applications Group (DAG)

Distributed Applications Group (DAG)

The mission of this group focuses on multidisciplinary research ranging from distributed systems, its applications in crowd sourcing, implementations of distributed applications that supports handicapped community, building up distributed AI applications in healthcare, developing distributed intelligent human-aware applications that possesses context awareness, etc. The group is also working in developing distributed learning theories and frameworks following the Software Quality Assurance standards to enhance the distributed virtual learning environments. Our research is fundamental, aimed at harnessing the true potential of distributed crowdsourcing to enhance various phases of software development ranging from requirements engineering till managing bug repositories. The group is application-driven, motivated by important application areas, such as developing distributed applications to utilize the unmanned aerial vehicle (UAV) systems in education & health, developing auditable distributed systems utilizing the state-of-the-art technologies, designing framework for dynamic collaborative swarms etc.

Research areas:

  • Crowd sourced based distributed applications
  • Human-aware computing
  • Assistive software development
  • Distributed AI applications for healthcare and education
  • Distributed Learning Computing
  • Distributed Requirement Engineering
  • Distributed Dynamic collaborative swarm computing
  • Ambient Assisted Computing

Completed Projects:

  • Hospital Indoor Positioning/Navigation System
  • Ergonomic Parking Solution – ParkMe
  • Automated Signature Inspection using Neural Networks
  • Image Compression using Deep Learning for Image Colorization

Current Projects:

  • Insect based navigation in multi-agent environment
  • Block chain based real-estate ledger system
  • Automatic validation of Bug Reports

Recent International Publications:

  • Yumna Zahid, Hina Khurshid, Memon, Z.A., On Improving Efficiency and Utilization of Last Level Cache in Multicore Systems. Journal of Information Technology and Control, vol. 47, No. 3, 2018, pp. 588-607. (Impact Factor: 0.800)
  • Asadullah Laghari, Memon, Z.A., Sadiq Ullah, Intesab Hussain, Cyber Physical System for Stroke Detection. IEEE Access, vol. 6, 2018, pp. 37444-37453. (Impact Factor: 3.557)
  • Kashif Laeeq, Zulfiqar A. Memon, An Integrated Model to Enhance Virtual Learning Environments with Current Social Networking Perspective, International Journal of Emerging Technologies in Learning, vol. 13, No. 9, 2018. 252-268. (ISI Indexed)
  • Memon Z.A., Mubarak H., Khimani A., Malik M., Karim S. EyeHope. (A Read Time Emotion Detection Application), Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. pp. 615-623, 2019. Springer. (Book Chapter)

Research Group Members:

  • Zulfiqar A. Memon (Principal Investigator and Team Lead)
  • Fahad Samad
  • Abdul Aziz
  • Abdul Rahman

PhD Students:

  • Kashif Laeeq
  • Asad Abbasi