Deep learningbased approach for water crystal classification
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Deep learningbased approach for water crystal classification
VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOAN THI HIENDEEP LEARNING-BASED APPROACH FOR WATER CRYSTAL CLASSIFICATIONM Deep learningbased approach for water crystal classification MASTER THESISMajor: Computer ScienceHA NOI -2021VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDoan Thi HienDEEP LEARNING-BASED APPROACH FORWATER CRYSTAL CLASSIFICATIONMASTER THESISMajor: Computer ScienceSupervisor:Dr. Tran Quoc LongCo-supervisor:Dr. Frederic AndresHA NOI Deep learningbased approach for water crystal classification - 2021AbstractAlmost the earth's surface area is covered by waler. As it is pointed out in the 2020 edition of the World Waler Development Report, clDeep learningbased approach for water crystal classification
imate change challenges the sustainability of waler resources. Il is important to monitor the quality of waler to preserve sustainable water resourcesVIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOAN THI HIENDEEP LEARNING-BASED APPROACH FOR WATER CRYSTAL CLASSIFICATIONM Deep learningbased approach for water crystal classification ity. First step, waler crystal exploratory analysis has been initialed under cooperation with the Emoto Peace Project (EPP), rhe 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 learnin Deep learningbased approach for water crystal classification g 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.Deep learningbased approach for water crystal classification
Using the 5K EPP dataset human observations and past researches on snow crystal classification, we provided a simple set of visual labels to name wateVIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOAN THI HIENDEEP LEARNING-BASED APPROACH FOR WATER CRYSTAL CLASSIFICATIONM Deep learningbased approach for water crystal classification led dataset. The classification achieved high accuracy when fine-tuning the ResNet pretrained model.Keywords: Waler crystal. Deep learning. Kine-tuning, Supervised. Classification.iiiAcknowledgements1would first like 10 thank my thesis supervisor Dr. Tran Quoc Long. Head of the Department of Compute Deep learningbased approach for water crystal classification r Science al the University of Engineering and Technology. Thanks for his insightful comments both in my work and in this thesis, for his support, andDeep learningbased approach for water crystal classification
many motivating discussions.1 also want to acknowledge my co-supervisor Dr. Frederic Andres from the National Institute of Informatics. Japan for offVIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOAN THI HIENDEEP LEARNING-BASED APPROACH FOR WATER CRYSTAL CLASSIFICATIONM Deep learningbased approach for water crystal classification ot achieve today result.Besides. I have been very privileged to get to know and to collaborate with many other great collaborators.Finally. I must express my very profound gratitude to my family for providing me u ith unfailing support and continuous encouragement throughout my years of study and th Deep learningbased approach for water crystal classification rough the process of researching and writing this thesis. This accomplishment would not have been possible without them.ivDeclarationI declare that thDeep learningbased approach for water crystal classification
e diesis has been composed by myself and that the work has not Ik* submilled for any other degree or professional qualification. I confirm that the woVIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOAN THI HIENDEEP LEARNING-BASED APPROACH FOR WATER CRYSTAL CLASSIFICATIONM Deep learningbased approach for water crystal classification other authors to this work have been explicitly indicated below. I confirm that appropriate credit has been given within this thesis where reference has been made Io the work of others.This study was conceived by all of the authors. I carried out the main idea(s) and implemented all the model(s) and Deep learningbased approach for water crystal classification material(s).1 certify that, to the best of my knowledge, my thesis does not infringe upon anyone’s copyright nor violate any proprietary rights and tDeep learningbased approach for water crystal classification
hat any ideas, techniques, quotations. or any other material from the work of other people included in my thesis, published or otherwise, are fully acVIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOAN THI HIENDEEP LEARNING-BASED APPROACH FOR WATER CRYSTAL CLASSIFICATIONM Deep learningbased approach for water crystal classification I have obtained a written permission from the copyright owner(s) to include such material(s) in my thesis and have fully authorship to improve these materials.Master studentDoan Thi HienV Deep learningbased approach for water crystal classification VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOAN THI HIENDEEP LEARNING-BASED APPROACH FOR WATER CRYSTAL CLASSIFICATIONMGọi ngay
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