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Machine Learning, Optimization, and Data Science

8th International Conference, LOD 2022, Certosa di Pontignano, Italy, September 18–22, 2022, Revised Selected Papers, Part II

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Giuseppe Nicosia

    1. University of Catania, Catania, Italy

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  2. Varun Ojha

    1. University of Reading, Reading, UK

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  3. Emanuele La Malfa

    1. University of Oxford, Oxford, UK

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  4. Gabriele La Malfa

    1. University of Cambridge, Cambridge, UK

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  5. Panos Pardalos

    1. University of Florida, Gainesville, USA

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  6. Giuseppe Di Fatta

    1. Free University of Bozen-Bolzano, Bolzano, Italy

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  7. Giovanni Giuffrida

    1. University of Catania, Catania, Italy

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  8. Renato Umeton

    1. Dana-Farber Cancer Institute, Boston, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13811)

Included in the following conference series:

Conference proceedings info: LOD 2022.

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Table of contents (42 papers)

  1. Front Matter

    Pages i-xxiv
    Download chapter PDF

  2. Pooling Graph Convolutional Networks for Structural Performance Prediction

    • Lorenz Wendlinger, Michael Granitzer, Christofer Fellicious
    Pages 1-16

  3. A Game Theoretic Flavoured Decision Tree for Classification

    • Mihai-Alexandru Suciu, Rodica-Ioana Lung
    Pages 17-26

  4. Human-Centric Machine Learning Approach for Injection Mold Design: Towards Automated Ejector Pin Placement

    • Robert Jungnickel, Johanna Lauwigi, Vladimir Samsonov, Daniel Lütticke
    Pages 27-31

  5. A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs

    • Ross A. J. McLaren, Kehinde Oluwatoyin Babaagba, Zhiyuan Tan
    Pages 32-46

  6. Multi-omic Data Integration and Feature Selection for Survival-Based Patient Stratification via Supervised Concrete Autoencoders

    • Pedro Henrique da Costa Avelar, Roman Laddach, Sophia N. Karagiannis, Min Wu, Sophia Tsoka
    Pages 47-61

  7. Smart Pruning of Deep Neural Networks Using Curve Fitting and Evolution of Weights

    • Ashhadul Islam, Samir Brahim Belhaouari
    Pages 62-76

  8. Biologically Inspired Unified Artificial Immune System for Industrial Equipment Diagnostic

    • Galina Samigulina, Zarina Samigulina
    Pages 77-92

  9. A Nonparametric Pooling Operator Capable of Texture Extraction

    • V. Vigneron, H. Maaref
    Pages 93-107

  10. Strategic Workforce Planning with Deep Reinforcement Learning

    • Yannick Smit, Floris den Hengst, Sandjai Bhulai, Ehsan Mehdad
    Pages 108-122

  11. Enhanced Association Rules and Python

    • Petr Máša, Jan Rauch
    Pages 123-138

  12. Generating Vascular Networks: A Reinforcement Learning Approach

    • João Braz Simões, Rui Travasso, Ernesto Costa, Tiago Baptista
    Pages 139-153

  13. Expressive and Intuitive Models for Automated Context Representation Learning in Credit-Card Fraud Detection

    • Kanishka Ghosh Dastidar, Wissam Siblini, Michael Granitzer
    Pages 154-168

  14. Digital Twins: Modelling Languages Comparison

    • Abdul Wahid, Jiafeng Zhu, Stefano Mauceri, Lei Li, Minghua Liu
    Pages 169-178

  15. Algorithms that Get Old: The Case of Generative Deep Neural Networks

    • Gabriel Turinici
    Pages 179-187

  16. A Two-Country Study of Default Risk Prediction Using Bayesian Machine-Learning

    • Fabio Incerti, Falco J. Bargagli-Stoffi, Massimo Riccaboni
    Pages 188-192

  17. Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies

    • Bram De Cooman, Johan Suykens, Andreas Ortseifen
    Pages 193-218

  18. A Bee Colony Optimization Approach for the Electric Vehicle Routing Problem with Drones

    • Nikolaos A. Kyriakakis, Themistoklis Stamadianos, Magdalene Marinaki, Nikolaos Matsatsinis, Yannis Marinakis
    Pages 219-233

  19. Best Practices in Flux Sampling of Constrained-Based Models

    • Bruno G. Galuzzi, Luca Milazzo, Chiara Damiani
    Pages 234-248

  20. EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python

    • Aayush Kumar, Jimiama M. Mase, Divish Rengasamy, Benjamin Rothwell, Mercedes Torres Torres, David A. Winkler et al.
    Pages 249-264
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Other volumes

  1. Machine Learning, Optimization, and Data Science

  2. Machine Learning, Optimization, and Data Science

Keywords

About this book

This two-volume set, LNCS 13810 and 13811,  constitutes the refereed proceedings of the 8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, together with the papers of the Second Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022.
The total of 84 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 226 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Editors and Affiliations

  • University of Catania, Catania, Italy

    Giuseppe Nicosia, Giovanni Giuffrida

  • University of Reading, Reading, UK

    Varun Ojha

  • University of Oxford, Oxford, UK

    Emanuele La Malfa

  • University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  • University of Florida, Gainesville, USA

    Panos Pardalos

  • Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  • Dana-Farber Cancer Institute, Boston, USA

    Renato Umeton

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