Artificial Intelligence Innovation Laboratory

Get Started


AiiLAB and COE

AiiLAB has signed a Memorandum of Understanding (MoU) with Co.E Development Company Limited for research collaboration.

AiiLAB and Better World Traffic

AiiLAB has signed a Memorandum of Understanding (MoU) with Better World Traffic Company Limited for research collaboration.

SuperAI Project Collaboration

Dr.Korawit and Dr.Kwankamon from AiiLAB participated in the Super AI Engineer Season 2 project of the Artificial Intelligence Association of Thailand in the AI Clinic Session. They talked about AIoT: When Artificial Intelligence Meets the Internet of Things.

Dr. Korawit unofficially visited Musashino University

Dr. Korawit gave a talk about the College of Computing and current research at Musashino University.

Musashino University Researcher visited the College of Computing and AiiLAB for research collaboration.

Researchers and executives from Musashino University, Japan, visited the College of Computing for academic and research collaboration. One of the activities was visiting AiiLAB for the Smart Trash Box development project and talking about the cooperation with Karon municipal for implementing Smart Trash Box in the Karon district area.


On June 1, 2022, the Artificial Intelligence Innovation Laboratory (AiiLAB) under the College of Computing, Prince of Songkla University, Phuket Campus. Dr Kwankamon Dithkan, lab director and Dr Kornwit Pruitchainimmit, researcher, together with Mr Niphon Ekwanich, Chairman of Phuket Development Company Limited, signed a memorandum of understanding on the Research and Academic Cooperation (MOU) for research cooperation and knowledge development in academic works, innovations and drive the utilization of artificial intelligence technology.

  Now the industry of Artificial Intelligence (AI) technology is growing and has a huge impact on both business and society. The increase in the use of AI has led to many transformations and developments in various industries. Make the organization change the way of doing business. method reform change the environment In order for organizations to run their own businesses and compete in the global economy, businesses need to adopt new technologies to cope with rapid technological changes and create new business opportunities. To help strengthen competitiveness and increase revenue.

  Meanwhile, the demand for technology related to artificial intelligence to add value to businesses or to innovate to create wealth and stand for the country is constantly growing. But most university research has limitations, making it unable to be applied to businesses or to be developed into products. For this reason, the research team has the idea to set up an Artificial Intelligence Innovation Laboratory, a collaboration of researchers with AI expertise to research and develop artificial intelligence deep technologies and researchers at Specializes in the development and application of artificial intelligence technology to meet business needs and create innovative products as the name of the research group.



To research and develop a body of knowledge in artificial intelligence that can be developed into innovative products that help develop the country and improve people's quality of life.


To create a research network in artificial intelligence technology.


To transfer knowledge to the students in order to develop students' physical potential and promote awareness and skills in artificial intelligence technology.


To apply the developed artificial intelligence technology in order to the benefit of businesses and external agencies.


Kwankamon Dittakan, Ph.D.

(Lab Director)

Tel: 076-276-000 Ext 6712


  • Doctor of Philosophy (PhD) in Computer Science, University of Liverpool, United Kingdom, 2016
  • Master of Science (M.Sc.) in Computer Science, Prince of Songkla University, Thailand, 2007
  • Bachelor of Science (B.Sc.) in Computer Science, Prince of Songkla University, Thailand, 2001

  • Research Interest
    Artificial Intelligence, Data Science, Machine Learning, Data Mining, Image Analysis

Korawit Prutsachainimmit, Ph.D.


Tel: 076-276-000 Ext 6733


  • Doctor of Philosophy (PhD) in Computer Science, Tokyo Institute of Technology, Tokyo, JAPAN, April 2016
  • Master of Engineer in Computer Science, Tokyo Institute of Technology, Tokyo, JAPAN, April 2012
  • Bachelor of Science (B.Sc.) in Computer Science, Prince of Songkla University, Songkhla, Thailand, March 1998

  • Research Interest
    Software Engineering, Mobile Computing, Web Engineering, Internet of Things

Monchanok Thongthep


Tel: 076-276-000 Ext 6136


  • Master of Science (M.Sc.) in Computational, Chulalongkorn University, Thailand, 2002
  • Bachelor of Science (B.Sc.) in Mathematics, Mahidol University, University, Thailand, 1998

  • Research Interest
    Web Programming, System Analysis, System Design

Graduate Student Research

Ph.D Students

Mrs Sujittra Sangiem

Research Topic
The Development of Convolutional Neural Network Architecture for Road Surface Condition Detection Using Video and Sensor Data
Doctor of Philosophy in Data Science,
College of Digital Sciences, Prince of Songkla University, 2020 - present

Mr Sophal Chan

Research Topic
The Development of Deep Learning Architecture for Abnormal Mitral Valve Detection using the heart sound
Doctor of Philosophy in Data Science,
College of Digital Sciences, Prince of Songkla University, 2020 - present

Mr Mathus Teppaitoon

Research Topic
Intelligent Relighting of Portrait Imagery in Virtual Video Production Using Machine Learning
Doctor of Philosophy in Data Science, College of Digital Sciences, Prince of Songkla University, 2021 - present

Master Students

Mr Kahabodee Prakobchat

Research Topic
Vehicle Physical Appearance Identification using Convolutional Neural Network
Master of Science in Data Science,
College of Digital Sciences, Prince of Songkla University, 2021 - present

Assistant Researchers

Mr Worrapong Ongsakul

Bachelor of Science (B.Sc.) in Information Technology,
College of Computing, Prince of Songkla University, May 2020

Ms Tassanee Hatthiya

Master of Science in Information Technology,
College of Computing, Prince of Songkla University, 2018 - 2021

Bachelor of Science in Information Technology,
College of Computing, Prince of Songkla University, 2014 - 2018

Ms Pratthana Jinapol

Bachelor of Science (B.Sc.) in Software Engineering,
College of Computing, Prince of Songkla University, 2017 - 2020


  • 1. International Journals
    • Sophal Chan,Kwankamon Dittakan and Matias Garcia-Constantino, Image Texture Analysis for Medical Image Mining: A comparative study direct to Osteoarthritis Classification using Knee X-ray Image, International Journal on Advanced Science, Engineering and Information Technology, Vol 10, No 6, 2020, pp2189-2199. (Indexed in Scopus, CiteScore: 1.31, Quartile 2).
    • Sophal Chan, Kwankamon Dittakan and Subhieh El Salhi, Osteoarthritis detection by Applying Quad-tree Analysis to Human Joint Knee X-ray imagery, International Journal of Computers and Applications, Taylor & Francis, 2020, pp 1-8. (Indexed in Scopus, CiteScore: 1.3, Quartile
    • Sujittra Sa-ngiem, Kwankamon Dittakan, Kanya Temkiatvises, Sirisak Yaisoongnern and Kongkiat Kespechara, Cerebral Microbleed Detection by Extracting Area and Number from Susceptibility Weighted Imagery using Convolutional Neural Network, May 2019, Journal of Physics: Conference Series, Volume 1229, pp 012038. (Indexed in Scopus, CiteScore: 0.7). DOI: 10.1088/1742-6596/1229/1/012038
    • Tassanee Hattiya, Kwankamon Dittakan, and Salang Musikasuwan, Diabetic Retinopathy Detection using Convolutional Neural Network: A Comparative Study on Different Architectures, Accepted to publish in Mahasarakham International Journal of Engineering Technology. (Indexed in TCI Tier 2)
    • Kwankmaon Dittakan, Nawanol Theera-Ampornpunt, Pum-Riang Thai Silk Pattern Classification Using Texture Analysis. Proceedings of the Pacific Rim International Conferences on Artificial Intelligence (PRICAI). Nanjing, China. Page: 82-90. August 28-31, 2018, Springer's Lecture Notes in Artificial Intelligence: Volume 11012.
    • Kwankmaon Dittakan, Nawanol Theera-Ampornpunt and Pattaporn Boodliam, Non-destructive Grading of Pattavia Pineapple using Texture Analysis, Wireless Personal Multimedia Communications 2018 (WPMC'18), Chiang Rai, Thailand, 25-28 November 2018, pp 144-149. IEEE 2018.
    • Sophal Chan and Kwankamon Dittakan, Osteoarthritis Stages Classification to Human Joint Imagery using Texture Analysis: A Comparative Study on Ten Texture Descriptors, The Second International Conference on Recent Trends in Image Processing & Pattern Recognition (rtip2r 2018), Solapur, Maharastra State, India, 21-22 December 2018. Springer's Lecture Notes in Communications in Computer and Information Science: Volume 1036.
    • Kwankamon Dittakan, Nawanol Theera-Ampornpunt, Waraphon Witthayarat, Sararat Hinnoy, Supawit Klaiwan, and Thunyathorn Pratheep, Banana Cultivar Classification using Scale Invariant Shape Analysis. Proceedings of the 2nd International Conference on Information Technology (InCIT 2017). Nakhon Pathom, Thailand, 2-3 November 2017, Page: 171-176. IEEE 2017.
    • Kwankamon Dittakan, Frans Coenen, Early Detection of Osteoarthritis Using Local Binary Patterns: A Study Directed at Human Joint Imagery, The 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2016), Phuket, Thailand, 22-26 August 2016, Page: 93-105., Springer's Lecture Notes in Artificial Intelligence, volume 9810.
    • Korawit Prutsachainimmit, Tinakarn Janthong and Wanatip Tuanropi. “A Development of Inpatient Monitoring System: A Case Study of Patong Hospital, Phuket, Thailand.” The 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2020), 2020.
    • Korawit Prutsachainimmit, Thosalpol Hemna and Udom Nakaew. “Prototype Development of Smart Locker System for Collaborative Used.” 41st Electrical Engineering Conference (EECON-41), 2018.
    • Korawit Prutsachainimmit, and Winai Nadee. "Towards data extraction of dynamic content from JavaScript Web applications." Information Networking (ICOIN), 2018 International Conference on. IEEE, 2018.
    • Adisak Intana, Monchanok Thongthep, Phatcharee Thepnimit, Phaplak Saethapan and Tanawat Monpipat, "SYNTest: Prototype of Syntax Test Case Generation Tool," 2020 - 5th International Conference on Information Technology (InCIT), Chonburi, Thailand, 2020, pp. 259-264, DOI: 10.1109/InCIT50588.2020.9310968.
    • ขวัญกมล ดิฐกัญจน์, ณัฎฐ์นรี นวลนิ่ม, ฐิติภรณ์ สว่างวงศ์​และ ธัญธร ประทีม. 2560. การนับเม็ดยาต่างขนาดและรูปร่างด้วยเทคนิคการประมวลผลภาพ, การประชุมวิชาการระดับประเทศด้านเทคโนโลยีสารสนเทศ (National Conference on Information Technology) ครั้งที่ 9, ศาลายา, นครปฐม. หน้า 51-56. 1 - 2 พฤศจิกายน พ.ศ. 2560
    • Frans Coenen and Kwankamon Dittakan, Image Representation for Image Mining: A Study Focusing on Mining Satellite Images for Census Data Collection, Knowledge Discovery, Knowledge Engineering and Knowledge Management, Springer Communications in Computer and Information Science 914, pp3-27.

Research Grants

  • โครงการระบบสอดส่องคุณภาพพื้นผิวจราจรโดย Machine Learning ด้วยข้อมูลจาก sensor ในยานพาหนะผ่านโครงข่าย IoT ได้รับทุนจาก กองทุนวิจัยและพัฒนากิจการกระจายเสียง กิจการโทรทัศน์ และกิจการโทรคมนาคม เพื่อประโยชน์สาธารณะ ปี 2563 ระยะเวลา 18 เดือน งบประมาณ 7,856,689 บาท
  • โครงการการวิเคราะห์กิจกรรมของนักท่องเที่ยวโดยใช้ข้อมูลอินสตาแกรม กรณีศึกษาจังหวัดภูเก็ต หน่วยบริหารและจัดการทุนด้านการพัฒนากำลังคน และทุนด้านการพัฒนาสถาบันอุดมศึกษา การวิจัยและการสร้างนวัตกรรม (บพค.) รับจากทุนนักวิจัยใหม่ วท. ระยะเวลา 12 เดือน งบประมาณที่ได้รับ 250,000
  • โครงการตู้ล็อกเกอร์อัจฉริยะสําหรับป้องกันการติดเชื้อและส่งเสริมการเว้น ระยะห่างระหว่างบุคคล ได้รับทุนจากทุนโครงการวิจัยและนวัตกรรม ประจําปีงบประมาณ 2563 วิทยาลัยการคอมพิวเตอร์ ระยะเวลา 12 เดือน งบประมาณ 100,000 บาท
  • การพัฒนาระบบระบุจุดเสี่ยงสำหรับรถจักรยานยนต์ด้วยเซนเซอร์ในสมาร์ทโฟน ได้รับงบประมาณจาก สำนักงานการวิจัยแห่งชาติ ปี 2565 ระยะเวลา 12 เดือน งบประมาณ 3,000,000 บาท


Laberu : Crowdsourced Image Labeling Platform

Crowdsourced Image Labeling Platform Laberu is a crowdsourced data labeling platform that builds to accommodate and accelerate the data labeling process in the AI model development process. The platform allows the crowd to work on data labeling tasks using well-designed labeling tools, i.e., image classification, image labeling, image annotation, etc. Laberu also provides the mechanism for AI model developers to monitor the labeling tasks, verify and validate the labeling work, and report on various activities that help manage data labeling projects.

Smart UV Locker : A Smart Locker for Infection Prevention and Social Distancing Promotion

Smart UV Locker is an innovative prototype product powered by the Internet of Things technologies built for response to COVID-19 pandemics. The smart locker is designed to support collaborative use with intelligent features. One can keep the package and let another person take it by sending a one-time password to avoid direct contact and promote social distancing. The UV sterilizer helps disinfection packages arrive before the user’s hands. Users can use UVBOX mobile application to rent a locker, open a locker to store the item, and open the shared locker to get the item.

Smooth Street : Application of Machine Learning and IoT Vehicle Sensor Network for Road Surface Quality Monitoring

Smooth Street is a research project that AiiLab collaborates with Kasetsart University and is funded by the Office of The National Broadcasting and Telecommunications Commission. This project aims to explore the efficient way for road surface survey by applying machine learning and IoT sensors on ordinary vehecle. Our machine learning model can classify and detect the damage on the road surface and report the type of damage, location, date, and time to the data platform. The data platform helps visualize the road surface condition on an interactive map and calculate the road quality for alliance smart city projects.

Tourist Activity Analysis using Instagram Data : A Case Study of Phuket

Tourist activity analysis is considered an essential process for improving product and service quality in the tourism industry. The typical methodology is conducting surveys and interviews, which requires many human resources and is time-consuming. This project has discovered the possibility of analyzing tourist activity by leveraging Instagram data, which has a wider variety and colossal volume than standard survey methodology. We have collected Instagram data related to Phuket for six months during the high-season period. The data cleaning process separates the post of local citizens from tourists and filters advertisements out from the tourism-related posts. Since Instagram data mainly contain photos rather than text, image processing and deep learning technology, i.e., object detection, are applied to extract tourism insight, which can precisely analyze tourist activity. The knowledge related to data gathering and tourist activity analysis from this project can be used in other tourist destinations and extended to the country level.



Artificial Intelligence Innovation Laboratory,
College of Computing, Prince of Songkla University Phuket Campus
80 M.1 Vichitsongkram Road Kathu, Phuket 83120

Email Us

Call Us

+66 7627 6000 ext. 6712