Art is an Information Technology lecturer with particular interests in in document analysis and recognition which is the subfield of Pattern Recognition and Artificial Intelligence.

She has been employing Artificial Neural Networks, Support Vector Machines, and Hidden Markov Models in her research. She is currently exploring biometric (based on signatures and name components). She is also interested in Network Security and deep learning.

  • CP1402: Internet Fundamentals (Level 1; CNS & TSV)
  • CP1403: Design Thinking (Level 1; TSV)
  • CP1406: Web Design and Development (Level 1; CNS & TSV)
  • CP2412: Game Design and Technologies (Level 2; TSV)
  • CP3404: Information Security (Level 3; CNS & TSV)
  • CP5604: Advanced Game Design (Level 5; TSV)
  • CP5631: Internet Fundamentals (Level 5; TSV)
  • CP5638: Web Design and Development (Level 5; CNS & TSV)
  • Document Analysis
  • Image Processing
  • Artificial Neural Networks
  • Support Vector Machines
  • Biometric
Socio-Economic Objectives

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU.

Journal Articles
Conference Papers

ResearchOnline@JCU stores 14+ research outputs authored by Dr Art (Hemmaphan) Suwanwiwat from 2013 onwards.

Current Funding

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

Fisheries Research & Development Corporation - Annual Competitive Round

Application of a machine learning approach for effective stock management of abalone

Indicative Funding
$115,649 over 2 years
Determining the number and size distribution of abalone present at various stages of production is critical information for effective stock management. Currently the Australian abalone aquaculture industry spends in the order of $25,000 per annum, per farm, gathering this information by hand. However, the resulting data is of mediocre quality, is limited in its scope, and collecting the data causes stress to the animals which can compromise growth and survival. Automated counting and measuring of abalone will increase farm efficiency and productivity in the short term and, in the longer term, will provide an advanced platform for further R&D improvements. Artificial intelligence and machine learning has now matured to a point that accurately counting and measuring abalone is possible using this approach. This project would involve the development, training and validation of a machine learning model to identify, segment and measure quantitative abalone traits in production systems, and render the product data to be accessible and applicable for farmers.
Jan Strugnell, Marcus Sheaves, Carlo Mattone, Ickjai Lee, Joanne Lee, Jason Holdsworth and Art (Hemmaphan) Suwanwiwat (College of Science & Engineering)
Abalone (Haliotidae); Machine Learning

World Wide Fund for Nature (US) - Contract Research

From Coastal Communities to Cloud Communities ? New Application and Artificial Intelligence to Monitor Fish Stocks Using Photos ? Application Development

Indicative Funding
$53,100 over 1 year
The Project aims at develop an artificial intelligence capable to autonomously identify fish species and number from images collected at fish markets in remote location, so that effective catch rate can be evaluated and management policies can be developed.
Marcus Sheaves, Carlo Mattone, Michael Bradley, Joanne Lee, Jason Holdsworth, Art (Hemmaphan) Suwanwiwat and Ickjai Lee (College of Science & Engineering)
Artificial Intelligence; Phone App; Caught Fish; Catch Rate

Advisory Accreditation: I can be on your Advisory Panel as a Primary or Secondary Advisor.

These Higher Degree Research projects are either current or by students who have completed their studies within the past 5 years at JCU. Linked titles show theses available within ResearchOnline@JCU.

  • An Automated approach to predict Sepsis in Adults from Electronic Medical Record (EMR) Data using Machine Learning and Natural Language Processing (PhD , Secondary Advisor)
  • Investigating Security Vulnerabilities in Healthcare IoT (PhD , Secondary Advisor)

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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