Machine Learning, Optimization, and Data Science

5th International Conference, LOD 2019, Siena, Italy, September 10–13, 2019, Proceedings

  • Giuseppe Nicosia
  • Panos Pardalos
  • Renato Umeton
  • Giovanni Giuffrida
  • Vincenzo Sciacca
Conference proceedings LOD 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11943)

Also part of the Information Systems and Applications, incl. Internet/Web, and HCI book sub series (LNISA, volume 11943)

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. Sean Tao
    Pages 1-12
  3. Shokoufeh Monjezi Kouchak, Ashraf Gaffar
    Pages 13-23
  4. Vittorio Mazzia, Angelo Tartaglia, Marcello Chiaberge, Dario Gandini
    Pages 24-35
  5. Patrik Reizinger, Bálint Gyires-Tóth
    Pages 45-57
  6. Valentina Ghidini, Alan Perotti, Rossano Schifanella
    Pages 58-70
  7. Gail Gilboa-Freedman, Alon Patelsky, Tal Sheldon
    Pages 106-117
  8. Dave McEwan, Jose Nunez-Yanez
    Pages 118-129
  9. Aljohara Almulhim, Vachik S. Dave, Mohammad Al Hasan
    Pages 130-142
  10. Abbas Haider, Hui Wang, Bryan Scotney, Glenn Hawe
    Pages 143-153
  11. Clint P. George, Wei Xia, George Michailidis
    Pages 205-218
  12. Elaheh Sobhani, Pierre Comon, Christian Jutten, Massoud Babaie-Zadeh
    Pages 219-231
  13. David N. Prata, Marcelo L. Rocha, Leandro O. Ferreira, Rogério Nogueira
    Pages 244-258
  14. Robert A. Kłopotek, Mieczysław A. Kłopotek
    Pages 259-271
  15. Anastasia Stepanova, Sergei V. Mironov, Sergei Sidorov, Alexey Faizliev
    Pages 296-307
  16. Rishabh Agrawal, Paridhi Kothari
    Pages 308-320
  17. Mustafa Mısır
    Pages 321-334
  18. Zelda B. Zabinsky, Giulia Pedrielli, Hao Huang
    Pages 335-346
  19. Jaqueline S. Angelo, Eduardo Krempser, Helio J. C. Barbosa
    Pages 347-359
  20. Giorgio Sartor, Carlo Mannino, Lukas Bach
    Pages 384-395
  21. José Eduardo H. da Silva, Lucas A. M. de Souza, Heder S. Bernardino
    Pages 396-408
  22. Cristina Mayr, Claudio Risso, Eduardo Grampín
    Pages 409-421
  23. Nicole Rosenstock, Juan Piccini, Guillermo Rela, Franco Robledo, Pablo Romero
    Pages 422-433
  24. Guillermo Rela, Franco Robledo, Pablo Romero
    Pages 434-444
  25. Toshimitsu Aritake, Noboru Murata
    Pages 472-483
  26. Hong-Bo Xie, Caoyuan Li, Richard Yi Da Xu, Kerrie Mengersen
    Pages 484-495
  27. Li-Chia Yeh, Chung-Chin Lu
    Pages 496-507
  28. Marius Geitle, Roland Olsson
    Pages 521-530
  29. Federico Bianchi, Alberto Castellini, Pietro Tarocco, Alessandro Farinelli
    Pages 553-565
  30. Carola Gajek, Alexander Schiendorfer, Wolfgang Reif
    Pages 566-578
  31. Tinkle Chugh, Alma Rahat, Pramudita Satria Palar
    Pages 579-591

About these proceedings


This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.


artificial intelligence big data data analytics data mining data science deep reinforcement learning evolutionary algorithms genetic algorithms global optimization learning algorithms machine learning machine learning techniques multiobjective optimization neural networks optimization reinforcement learning

Editors and affiliations

  1. 1.University of CambridgeCambridgeUK
  2. 2.University of FloridaGainesvilleUSA
  3. 3.Harvard UniversityCambridgeUSA
  4. 4.Università di CataniaCataniaItaly
  5. 5.AlmawaveRomeItaly

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-37598-0
  • Online ISBN 978-3-030-37599-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site