Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part I

This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023, which took place in Grasmere, UK, in September 2023.  The 72 full papers included in this book were carefully reviewed and selected from 119 submissions. The proceedings also contain 9 papers from and the Third Symposium on Artificial Intelligence and Neuroscience, ACAIN 2023. The contributions focus on the state of the art and the latest advances in the integration of machine learning, deep learning, nonlinear optimization and data science to provide and support the scientific and technological foundations for interpretable, explainable and trustworthy AI. 

Table of Contents:
Consensus-based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning.- Leverage Mathematics? capability to compress and generalize as application of ML Embedding extraction from LLMs and its adaptation in the Automotive Space.- Speeding up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks.- 38 Flocking Method for Identifying of Neural Circuits in Optogenetic Datasets.- A Machine Learning Approach for Source Code Similarity via Graph-focused Features.- Knowledge distillation with Segment Anything (SAM) model for Planetary Geological Mapping.- ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation.- Perceptrons Under Veri able Random Data Corruption.- 104 Dynamic Soaring in Uncertain Wind Conditions: Polynomial Chaos Expansion Approach.- Solving Continuous Optimization Problems with a new Hyperheuristic Framework.-Benchmarking Named Entity Recognition Approaches for Extracting Research Infrastructure Information from Text.- Genetic Programming with Synthetic Data for Interpretable Regression Modelling and Limited Data.- A FastMap-Based Framework for E ciently Computing Top-K Projected Centrality.- Comparative analysis of machine learning models for time-series forecasting of Escherichia coli contamination in Portuguese shell sh production areas.- The Price of Data Processing Gail Gilboa Freedman.- Reward Shaping for Job Shop Scheduling.- A 3D Terrain Generator: Enhancing Robotics Simulations with GANs.- Hybrid Model for Impact Analysis of Climate Change on Droughts in Indian Region.- Bilevel Optimization by Conditional Bayesian Optimization.- Few-Shot Learning for Character Recognition in Persian Historical Documents.- ProVolOne ? Protein Volume Prediction Using a Multi-Attention, Multi-Resolution Deep Neural Network andFinite Element Analysis.- A data-driven monitoring approach for diagnosing quality degradation in a glass containerprocess.- Exploring emergent properties of recurrent neural networks using novel energy function formalism.- Co-Imagination of Behaviour & Morphology of Agents.- An Evolutionary Approach to Feature Selection and Classification.- "It Looks All the Same to Me": Cross-index Training for Long-term Financial Series Prediction.- U-FLEX: Unsupervised Feature Learning with Evolutionary eXploration.- Improved Filter-Based Feature Selection Techniques Based on Correlation and Clustering Techniques.- Deep Active Learning with Concept Drifts for detection of Mercury's Bow Shock and Magnetopause Crossings.- Modeling Primacy, Recency, and Cued recall in serial memory task using on-center o -surround recurrent neural network.- Joining Emission Data from Diverse Economical Activity Taxonomies with Evolution Strategies.- GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators.- Can Complexity Measures and Instance Hardness Measures Reflect the Actual Complexity of Microarray Data.- Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learning.- Real-Time Emotion Recognition in Online Video Conferences for Medical Consultations.- Attentive perturbation: extending pre x tuning to large language models inner representations.- SoftCut: a fully di erentiable relaxed graph cut approach for deep learning image segmentation.