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© 2019

Big Data Analysis and Deep Learning Applications

Proceedings of the First International Conference on Big Data Analysis and Deep Learning

  • Thi Thi Zin
  • Jerry Chun-Wei Lin

Benefits

  • Gathers selected papers presenting cutting-edge findings in the fields of big data analysis, machine learning, and system monitoring

  • Brings together new techniques and systems proposed by prominent researchers, scholars, and practitioners

  • Provides applicable big data methods and deep learning theories, and discusses their use in real-world situations

Conference proceedings ICBDL 2018

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 744)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Big Data Analysis

  3. Deep Learning and its Applications

    1. Front Matter
      Pages 77-77
    2. Noritaka Shigei, Hiroki Urakawa, Yoshihiro Nakamura, Masahiro Teramura, Hiromi Miyajima
      Pages 79-85
    3. May Phyo Khaing, Mukunoki Masayuki
      Pages 86-93
    4. Su Wit Yi Aung, Soe Soe Khaing, Shwe Thinzar Aung
      Pages 94-103
  4. Data Mining and its Applications

    1. Front Matter
      Pages 113-113
    2. Myint Myint Sein, Saw Zay Maung Maung, Myat Thiri Khine, K-zin Phyo, Thida Aung, Phyo Pa Pa Tun
      Pages 115-122
    3. Youcef Djenouri, Jerry Chun-Wei Lin, Djamel Djenouri, Asma Belhadi, Philippe Fournier-Viger
      Pages 123-129
    4. May Phyo Thu, Khine Moe Nwe, Kyar Nyo Aye
      Pages 139-148

About these proceedings

Introduction

This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities. 

Keywords

Big Data Analysis Machine Learning Monitoring System Image Processing Conventional Neural Networks Industrial Information

Editors and affiliations

  • Thi Thi Zin
    • 1
  • Jerry Chun-Wei Lin
    • 2
  1. 1.Faculty of EngineeringUniversity of MiyazakiMiyazakiJapan
  2. 2.Department of Computing, Mathematics, and PhysicsWestern Norway University of Applied Sciences (HVL)BergenNorway

About the editors

Thi Thi Zin received her Ph.D. degree in the field of Image Processing in 2007 from Osaka City University, Japan. From 2007 to 2009, she was awarded a postdoctoral research fellowship of Japan Society for the Promotion of Science (JSPS). After the JSPS, she worked as a specially appointed researcher and specially appointed assistant professor at the University of Tokyo and Osaka City University until 2013. In 2016, she became a professor at the Faculty of Engineering, University of Miyazaki, Japan. Her research interests include human behavior understanding, intelligent transportation systems, cow behavior analysis, health care monitoring systems, and image recognition. She is a member of IEEE.

Jerry Chun-Wei Lin is currently an associate professor at the School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), China. He has published more than 200 research papers (90 SCI papers) and international conferences. His research interests include data mining, soft computing, artificial intelligence, social computing, multimedia and image processing, and privacy-preserving and security technologies. He is the Editor-in-Chief of the journal Data Science and Pattern Recognition (DSPR), associate editor of Journal of Network Intelligence, associate editor of Journal of Internet Technology (JIT), and the editorial board member of Intelligent Data Analysis (IDA). He is also the leader of SPMF project, which provides more than 130 data mining algorithms and has been widely cited in many different applications.

Bibliographic information

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