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Master thesis Computer science: Deep learning-based approach for water crystal classification

Depending on the origin of the water and the formation process, crystals are divided into three main types: snow crystals, ice crystals, and water crystals. From the shape of the crystal, the purity and the texture level are clearly reflected, then it enables us to assess the quality of the water. | VIETNAM NATIONAL UNIVERSITY HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY DOAN THI HIEN DEEP LEARNING-BASED APPROACH FOR WATER CRYSTAL CLASSIFICATION MASTER THESIS Major Computer Science HA NOI - 2021 Abstract Almost the earth s surface area is covered by water. As it is pointed out in the 2020 edition of the World Water Development Report climate change challenges the sustain- ability of water resources. It is important to monitor the quality of water to preserve sustainable water resources. Quality of water can be related to the water crystal struc- ture solid-state of water methods to understand water crystal help to improve water quality. First step water crystal exploratory analysis has been initiated under cooper- ation with the Emoto Peace Project EPP . The 5K EPP Dataset has been created as the first world-wide small dataset of water crystals. Our research focused on reducing inherent limitations when fitting machine learning models to the 5K EPP dataset. One major result is the classification of water crystals and how to split our small dataset into most related groups. Using the 5K EPP dataset human observations and past researches on snow crystal classification we provided a simple set of visual labels to name water crystal shapes with 12 categories. A deep learning-based method has been used to auto- matically do the classification task with a subset of the labeled dataset. The classification achieved high accuracy when fine-tuning the ResNet pretrained model. Keywords Water crystal Deep learning Fine-tuning Supervised Classification. iii Acknowledgements I would first like to thank my thesis supervisor Dr. Tran Quoc Long Head of the Depart- ment of Computer Science at the University of Engineering and Technology. Thanks for his insightful comments both in my work and in this thesis for his support and many motivating discussions. I also want to acknowledge my co-supervisor Dr. Frederic Andres from the Na- tional Institute of Informatics Japan for .