Dr Mohammad Jahanbakht is an innovative software engineer with diverse skills and interests from code development and numerical modeling to web programming and cloud technologies, and further to data science and machine intelligence. In addition to computer sciences, Mohammad has a rich background in the simulation and design of electronic, electromagnetic, antenna, and microwave technologies. The interdisciplinary research background has allowed Mohammad to participate in many research-based, as well as industrial-scale projects, including environmental studies, maritime research, and biodiversity monitoring.

  • Machine learning and deep neural networks
  • AI-powered embedded systems for edge processing
  • Code development and web applications
  • Data science, numerical modelling, and simulation
Research Disciplines

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

Journal Articles

ResearchOnline@JCU stores 14+ research outputs authored by Dr Mohammad Jahanbakht from 2008 onwards.

Current Funding

Current and recent Research Funding to JCU is shown by funding source and project.

Department of Climate Change, Energy, the Environment and Water - Contract Research

Enhancing AI detection of dugong and other marine megafauna species

Indicative Funding
$45,455 over 1 year
Standardised aerial surveys have been conducted across northern Australia for over three decades to monitor dugong populations. JCU is currently monitoring dugongs across the entire eastern Queensland coast using conventional observer survey approach. In parallel to the monitoring work, JCU is experimenting the use of aerial images to conduct these large scales surveys. Preliminary results from the image processing work reveals that substantial efforts need to be put in to streamline and fast-track the processing of large image datasets to make imagery survey a cost-effective approach in the future. An AI for detecting dugongs is available but was developed based on images collected in Western Australian waters, a different habitat compared to eastern Queensland inshore waters. Preliminary tests ran by our team suggest that the current AI requires additional research work to improve its level of precision while other competitive AIs also need to be investigated. Upon completion of our tests, our team will outline steps toward the improvement of the current AI and/or exploration of alternative methods with the end goal of producing an automated approach that fast-tracks the processing of large image datasets collected during large-scale marine wildlife surveys.
Christophe Cleguer and Mohammad Jahanbakht (TropWater)
Dugong (Dugong dugon); Aerial Surveys; Conservation and Management; Aerial Imagery; Artificial Intelligence

Australian Government Department of Agriculture, Water and the Environment - National Environmental Science Program 2 (NESP 2) - Marine and Coastal Hub (NESP MAC Hub)

NESP 3.4 Better management of catchment runoff to marine receiving environments in northern Australia.

Indicative Funding
$95,011 over 1 year (administered by Reef and Rainforest Research Centre)
Runoff from catchments in northern Australia has the potential to carry large amounts of sediment and nutrients. These available nutrients are important in driving coastal and estuary productivity, including many commercially and recreationally targeted species. Water resource development in northern Australia could reduce the supply of freshwater flow to the coast, thereby limiting supply of nutrients. This research project will examine the potential risks water resource development presents to Gilbert (QLD), Daly (NT) and Ord (WA) marine and coastal areas. We will complete a literature review, undertake flood plume modelling and examine vegetation damage along these coastal areas.
Nathan Waltham, Mohammad Jahanbakht and Paula Cartwright (TropWater)
Marine ecosystems; Water resource development; Fisheries; Water quality; Estuaries; modelling

The map shows research collaborations by institution from the past 7 years.
Note: Map points are indicative of the countries or states that institutions are associated with.

  • 5+ collaborations
  • 4 collaborations
  • 3 collaborations
  • 2 collaborations
  • 1 collaboration
  • Indicates the Tropics (Torrid Zone)

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