Sakchan Luangmaneerote

tsakchan@gmail.com

Introducing myself, I am a PhD graduate from the University of Southampton with a degree in Computer Science. My research interests lie in the fields of Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, and Big Data. I have also been actively involved in teaching and mentoring others in deploying AI in practice, developing web applications, and setting up Linux servers. Throughout my academic journey, I have gained extensive knowledge and experience in designing and implementing complex AI systems, developing scalable and efficient algorithms, and analyzing large datasets. My expertise also includes creating and deploying deep learning models, natural language processing techniques, and data visualization tools. Apart from my research and teaching endeavors, I am also passionate about staying up-to-date with the latest trends and advancements in the field of AI. I am committed to applying my knowledge and skills to solve real-world problems and make a positive impact on society. I am excited about the prospect of working on cutting-edge research projects, collaborating with other experts, and contributing to the development of innovative solutions in the field of AI.

Experience

Education

Skills

• Mobile-First, Responsive Design
• Cross Browser Testing & Debugging
• Cross Functional Teams
• Agile Development & Scrum

Interests

I am passionate about applying AI knowledge to help people. I have experience in machine learning and deep learning, and I am excited to share some of my projects and research with you.

Project

Fish Species Classification using Deep Learning

In this project, I developed a deep-learning model to help farmers quickly and accurately classify fish species. The model was trained on a large dataset of fish images, and it learned to recognize key characteristics of different fish species, such as their shape, size, and color. With this model, farmers can simply take a photo of a fish and input it into the system. The model then analyzes the photo and outputs the most likely fish species, saving farmers time and effort in manual classification. This technology has the potential to improve the efficiency of fish farming and ensure the correct classification of fish species for scientific and commercial purposes.

Watermelon Ripeness Detection using Deep Learning

In this project, I developed a machine learning model to help farmers detect the ripeness of watermelon. The model was trained on a dataset of images of watermelon, with annotations indicating whether each watermelon was ripe or unripe. The model was trained using a convolutional neural network (CNN), a type of deep learning model commonly used for image classification. The CNN learned to identify key visual features of ripe and unripe watermelon, such as color, texture, and shape. With this model, farmers can take a photo of a watermelon and input it into the system. The model then analyzes the photo and outputs a prediction of whether the watermelon is ripe or unripe. This technology has the potential to save farmers time and effort in manually inspecting each watermelon, and can help them harvest watermelons at the optimal ripeness for maximum flavor and yield. Future work on this project could involve expanding the dataset to include more diverse images of watermelon, as well as exploring the use of other machine learning techniques, such as object detection, to improve the accuracy of the model.

Research publication

• [1] S. Luangmaneerote and B. Nabumroong, “Studying Characteristics of Huay Saneng and Predicting The Water Levels by Comparison with Other Regression Models,” Information Technology Journal, vol. 17, no. 2, 2021.
  [2] S. Luangmaneerote and A. Chaiwachiragompol, “Interesting Attributes of Student Performance Using Machine Learning Models Based on Family and Educational Backgrounds in the Faculty of Agriculture and Technology at Rajamangala University of Technology Isan,” Information Technology Journal, vol. 17, no. 2, 2021.
  [3] S. Luangmaneerote, E. Zaluska, and L. Carr, “Survey of existing fingerprint countermeasures,” in 2016 International Conference on Information Society (i-Society), 2016, pp. 137–141.
  [4] W. Hiranrat, J. Tanuma, S. Sakaew, and S. Luangmaneerote, “Matching Online GameBased Learning (MOGBL) about Counting, Addition and Subtraction in case of Lower Elementary Student with Intellectual Disabilities,” Information Technology Journal, vol. 15, no. 2, pp. 23–34, 2019.
  [5] S. Luangmaneerote and J. Tasuntia, “Clustering Student Performance Based on Family and Educational Backgrounds: A Case Study of the Faculty of Agriculture and Technology at Rajamangala University of Technology Isan,” in The 8th International Conference on Technical Education (ICTechEd8), Bangkok, Thailand, 2021, pp. 375–382.
  [6] S. Luangmaneerote and L. C. Zaluska, “Inhibiting Fingerprinting Technique with FPprevention,” 2017.
  [7] S. Luangmaneerote, E. Zaluska, and L. Carr, “Inhibiting browser fingerprinting and tracking,” in 2017 ieee 3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids), 2017, pp. 63–68.

My book

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  Software Engineering with Artificial Intelligence

  Download the book here.

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