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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 I

  • 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 13810)

Included in the following conference series:

Conference proceedings info: LOD 2022.

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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.

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Table of contents (43 papers)

  1. Front Matter

    Pages i-xxiv
    Download chapter PDF

  2. Detection of Morality in Tweets Based on the Moral Foundation Theory

    • Luana Bulla, Stefano De Giorgis, Aldo Gangemi, Ludovica Marinucci, Misael Mongiovì
    Pages 1-13

  3. Matrix Completion for the Prediction of Yearly Country and Industry-Level CO\(_2\) Emissions

    • Francesco Biancalani, Giorgio Gnecco, Rodolfo Metulini, Massimo Riccaboni
    Pages 14-19

  4. A Benchmark for Real-Time Anomaly Detection Algorithms Applied in Industry 4.0

    • Philip Stahmann, Bodo Rieger
    Pages 20-34

  5. A Matrix Factorization-Based Drug-Virus Link Prediction Method for SARS-CoV-2 Drug Prioritization

    • Yutong Li, Xiaorui Xu, Sophia Tsoka
    Pages 35-47

  6. Hyperbolic Graph Codebooks

    • Pascal Mettes
    Pages 48-61

  7. A Kernel-Based Multilayer Perceptron Framework to Identify Pathways Related to Cancer Stages

    • Marzieh Soleimanpoor, Milad Mokhtaridoost, Mehmet Gönen
    Pages 62-77

  8. Loss Function with Memory for Trustworthiness Threshold Learning: Case of Face and Facial Expression Recognition

    • Stanislav Selitskiy, Natalya Selitskaya
    Pages 78-92

  9. Machine Learning Approaches for Predicting Crystal Systems: A Brief Review and a Case Study

    • Gaetano Settembre, Nicola Corriero, Nicoletta Del Buono, Flavia Esposito, Rosanna Rizzi
    Pages 93-107

  10. LS-PON: A Prediction-Based Local Search for Neural Architecture Search

    • Meyssa Zouambi, Julie Jacques, Clarisse Dhaenens
    Pages 108-122

  11. Local Optimisation of Nyström Samples Through Stochastic Gradient Descent

    • Matthew Hutchings, Bertrand Gauthier
    Pages 123-140Open Access

  12. Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting

    • Jiye Li, Bruno Kinder Almentero, Camille Besse
    Pages 141-155

  13. Intelligent Robotic Process Automation for Supplier Document Management on E-Procurement Platforms

    • Angelo Impedovo, Emanuele Pio Barracchia, Giuseppe Rizzo
    Pages 156-166

  14. Batch Bayesian Quadrature with Batch Updating Using Future Uncertainty Sampling

    • Kelly Smalenberger, Michael Smalenberger
    Pages 167-180

  15. Sensitivity Analysis of Engineering Structures Utilizing Artificial Neural Networks and Polynomial Chaos Expansion

    • Lukáš Novák, David Lehký, Drahomír Novák
    Pages 181-196

  16. Transformers for COVID-19 Misinformation Detection on Twitter: A South African Case Study

    • Irene Francesca Strydom, Jacomine Grobler
    Pages 197-210

  17. MI2AMI: Missing Data Imputation Using Mixed Deep Gaussian Mixture Models

    • Robin Fuchs, Denys Pommeret, Samuel Stocksieker
    Pages 211-222

  18. On the Utility and Protection of Optimization with Differential Privacy and Classic Regularization Techniques

    • Eugenio Lomurno, Matteo Matteucci
    Pages 223-238

  19. MicroRacer: A Didactic Environment for Deep Reinforcement Learning

    • Andrea Asperti, Marco Del Brutto
    Pages 239-252

  20. A Practical Approach for Vehicle Speed Estimation in Smart Cities

    • Silvio Barra, Salvatore Carta, Antonello Meloni, Alessandro Sebastian Podda, Diego Reforgiato Recupero
    Pages 253-267
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Other volumes

  1. Machine Learning, Optimization, and Data Science

  2. Machine Learning, Optimization, and Data Science

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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