About

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.

Teaching
  • CP1402: Internet Fundamentals (Level 1; CNS & TSV)
  • CP1406: Web Design and Development (Level 1; CNS & TSV)
  • CP1802: Internet Fundamentals (Level 1; CNS & TSV)
  • CP1806: Web Design and Development (Level 1; CNS & TSV)
  • CP2409: Network Forensics and Data Communications (Level 2; TSV)
  • CP2412: Game Design and Technologies (Level 2; TSV)
  • CP2414: Network Security (Level 2; CNS)
  • 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; TSV)
Interests
Research
  • Document Analysis
  • Image Processing
  • Artificial Neural Networks
  • Support Vector Machines
  • Biometric
Research Disciplines
Socio-Economic Objectives
Publications

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
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 3 years
Summary
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.
Investigators
Jan Strugnell, Marcus Sheaves, Carlo Mattone, Ickjai Lee, Joanne Lee, Jason Holdsworth and Art (Hemmaphan) Suwanwiwat (College of Science & Engineering)
Keywords
Abalone (Haliotidae); Machine Learning

World Wide Fund for Nature - 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 2 years
Summary
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.
Investigators
Marcus Sheaves, Carlo Mattone, Michael Bradley, Joanne Lee, Jason Holdsworth, Art (Hemmaphan) Suwanwiwat and Ickjai Lee (College of Science & Engineering)
Keywords
Artificial Intelligence; Phone App; Caught Fish; Catch Rate
Supervision

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.

Current
  • 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)
Completed
  • A data-driven approach towards a realistic and generic crowd simulation framework (2020, PhD , Secondary Advisor)
Collaboration

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)

Connect with me
Share my profile
Share my profile:
jcu.me/art.suwanwiwat

Email
Phone
Location
Advisory Accreditation
Primary Advisor
Find me on…
Icon for ORCID profile Icon for ResearchGate profile Icon for LinkedIn profile page Icon for Google Scholar profile Icon for Academia.edu profile

Similar to me

  1. A/Prof Trina Myers
    College of Science & Engineering
  2. Dr Tao Huang
    College of Science & Engineering
  3. Dr Joanne Lee
    College of Science & Engineering
  4. Dr Dmitry Konovalov
    College of Science & Engineering
  5. Prof Ian Atkinson
    eResearch Centre