Nội dung chi tiết: Deep learning based approach for water crystal classification
Deep learning based approach for water crystal classification
VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOANTH1 HIENDEEP LEARNING-BASED APPROACHFOR WATER CRYSTAL CLASSIFICATIONMAS Deep learning based approach for water crystal classificationSTER THESISMajor: Computer ScienceHA NO1 - 2021VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDoan Thi HienDEEP LEARNING-BASED APPROACH FOR WATER CRYSTAL CLASSIFICATIONMASTER THESISMajor: Computer ScienceSupervisor:Dr. Tran Quoc LongCo-supervisor:Dr. Frederic AndresHA NOI Deep learning based approach for water crystal classification - 2021AbstractAlmost (he earth’s surface area is covered by water. As it is pointed out in the 2020 edition of (he World Water Development Report, cl
Deep learning based approach for water crystal classification
imate change challenges the sustain- ability of water resources. It is important to monitor the quality of water to preserve sustainable water resourcVIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOANTH1 HIENDEEP LEARNING-BASED APPROACHFOR WATER CRYSTAL CLASSIFICATIONMAS Deep learning based approach for water crystal classificationquality. 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 l Deep learning based approach for water crystal classificationearning 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 gr
Deep learning based approach for water crystal classification
oups. Using the 5K EPP dataset human observations and past researches on snow crystal classification, we provided a simple set of visual labels to namVIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOANTH1 HIENDEEP LEARNING-BASED APPROACHFOR WATER CRYSTAL CLASSIFICATIONMAS Deep learning based approach for water crystal classificationhe labeled dataset. The classification achieved high accuracy when fine-tuning the ResNet pretrained model.Keywords: Water crystal, Deep learning, Fine-tuning, Supervised, Classification.iiiAcknowledgements1would first like to thank my thesis supervisor Dr. Tran Quoc Long, Head of the Depart- ment o Deep learning based approach for water crystal classificationf Computer Science al the University of Engineering and Technology. Thanks for his insightful comments both in my work and in this thesis, for his sup
Deep learning based approach for water crystal classification
port, and many motivating discussions.Ĩ also want to acknowledge my co-supervisor Dr. Frederic Andres from the National Institute of Informatics, JapaVIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOANTH1 HIENDEEP LEARNING-BASED APPROACHFOR WATER CRYSTAL CLASSIFICATIONMAS Deep learning based approach for water crystal classificationI could not achieve today result.Besides, I have been very privileged to get to know and to collaborate with many other great collaborators.Finally, 1 must express my very profound gratitude to my family for providing me with unfailing support and continuous encouragement throughout my years of stud Deep learning based approach for water crystal classificationy and through the process of researching and writing this thesis. This accomplishment would not have been possible without them.ivDeclarationI declare
Deep learning based approach for water crystal classification
(hat the thesis has been composed by myself and that the work has not be submitted for any other degree or professional qualification. I confirm thatVIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOANTH1 HIENDEEP LEARNING-BASED APPROACHFOR WATER CRYSTAL CLASSIFICATIONMAS Deep learning based approach for water crystal classificationf the 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 to 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( Deep learning based approach for water crystal classifications) and material(s).1 certify that, to the best of my knowledge, my thesis does not infringe upon anyone’s copyright nor violate any proprietary' right
Deep learning based approach for water crystal classification
s and that any ideas, techniques, quota- tions, or any other material from the work of other people included in my thesis, pub- lished or otherwise, aVIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOANTH1 HIENDEEP LEARNING-BASED APPROACHFOR WATER CRYSTAL CLASSIFICATIONMAS Deep learning based approach for water crystal classificationertify that 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 learning based approach for water crystal classificationVIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYDOANTH1 HIENDEEP LEARNING-BASED APPROACHFOR WATER CRYSTAL CLASSIFICATIONMAS