Shahzeb Ansari

About

Doctoral Researcher with a demonstrated history of working in the higher education industry. Skilled in Electronic Engineering, Human Machine Interface, Pattern Recognition, Time Series Analysis, and Deep Learning. Strong research professional with a Doctor of Philosophy - PhD focused in Driver Behaviour Monitoring and Machine Intelligence from University of Wollongong.

Employment

• University of Wollongong Associate Research Fellow
  2022 - Present
• University of Wollongong Laboratory Demonstrator
  2018 - Present
• Quaid-e-Awam University of Engineering Science and Technology Lab-Engineer
  2014 - 2018

Education

• University of Wollongong PhD
  2018 - 2022
• NED University of Engineering and Technology Industrial Electronics
  2014 - 2017
• Mehran University of Engineering and Technology Electronic Engineering
  2009 - 2013

Projects & Funding

Projects & funding information is unavailable.

Publications (16)

• A Semantic Hybrid Temporal Approach for Detecting Driver Mental Fatigue
  Safety 2024 DOI: 10.3390/safety10010009
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• Impact of Post-Covid-19 on driver behaviour: A perspective towards pandemic-sustained transportation
  Journal of Transport & Health 2023 DOI: 10.1016/j.jth.2022.101563
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• Factors Influencing Driver Behavior and Advances in Monitoring Methods
  AI-enabled Technologies for Autonomous and Connected Vehicles 2023 DOI: 10.1007/978-3-031-06780-8_14
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• System Identification of Heterogeneous Multirotor Unmanned Aerial Vehicle
  Drones 2022 DOI: 10.3390/drones6100309
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• Automatic Tuning and Turbulence Mitigation for Fixed-Wing UAV with Segmented Control Surfaces
  Drones 2022 DOI: 10.3390/drones6100302
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• Automatic Driver Cognitive Fatigue Detection based on Upper Body Posture Variations
  Expert Systems with Applications 2022 DOI: 10.1016/j.eswa.2022.117568
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• Human-Machine Shared Driving: Challenges and Future Directions
  IEEE Transactions on Intelligent Vehicles 2022 DOI: 10.1109/tiv.2022.3154426
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• Application of Fully Adaptive Symbolic Representation to Driver Mental Fatigue Detection Based on Body Posture
  Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics 2021 DOI: 10.1109/SMC52423.2021.9659024
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• Driver Mental Fatigue Detection Based on Head Posture Using New Modified reLU-BiLSTM Deep Neural Network
  IEEE Transactions on Intelligent Transportation Systems 2021 DOI: 10.1109/TITS.2021.3098309
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• Unsupervised Patterns of Driver Mental Fatigue State Based on Head Posture Using Gaussian Mixture Model
  2020 IEEE Symposium Series on Computational Intelligence (SSCI) 2020 DOI: 10.1109/ssci47803.2020.9308534
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