Machine Learning, Optimization, and Data Science [[electronic resource] ] : 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part II / / edited by Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos M. Pardalos, Renato Umeton

• Autore: Nicosia Giuseppe
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (503 pages)
• Disciplina: 005.3
• Soggetto topico: Information technology - Management; Computer networks; Electronic digital computers - Evaluation; Computer systems; Artificial intelligence; Machine learning; Computer Application in Administrative Data Processing; Computer Communication Networks; System Performance and Evaluation; Computer System Implementation; Artificial Intelligence; Machine Learning
• Altri autori: OjhaVarun La MalfaEmanuele La MalfaGabriele PardalosPanos M UmetonRenato
• Nota di contenuto: Integrated Human-AI Forecasting for Preventive Maintenance Task Duration Estimation -- Exploring Image Transformations with Diffusion Models: A Survey of Applications and Implementation Code -- Geolocation Risk Scores for Credit Scoring Models -- Social Media Analysis: The Relationship between Private Investors and Stock Price -- Deep learning model of two-phase fluid transport through fractured media: a real-world case study -- A Proximal Algorithm for Network Slimming -- Diversity in deep generative models and generative AI -- Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes -- kolopoly: Case Study on Large Action Spaces in Reinforcement Learning -- Alternating mixed-integer programming and neural network training for approximating stochastic two-stage problems -- Heaviest and densest subgraph computation for binary classification. A case study -- SMBOX: A Scalable and Efficient Method for Sequential Model-Based Parameter Optimization -- Accelerated Graph Integration with Approximation of Combining Parameters -- Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non- Visual Environments: A Comparison -- A hybrid steady-state genetic algorithm for the minimum conflict spanning tree problem -- Reinforcement learning for multi-neighborhood local search in combinatorial optimization -- Evaluation of Selected Autoencoders in the Context of End-User Experience Management -- Application of multi-agent reinforcement learning to the dynamic scheduling problem in manufacturing systems -- Solving Mixed Influence Diagrams by Reinforcement Learning -- Multi-Scale Heat Kernel Graph Network for Graph Classification -- Accelerating Random Orthogonal Search for Global Optimization using Crossover -- A Multiclass Robust Twin Parametric Margin Support Vector Machine with an Application to Vehicles Emissions -- LSTM noise robustness: a case study for heavy vehicles -- Ensemble Clustering for Boundary Detection in High-Dimensional Data -- Learning Graph Configuration Spaces with Graph Embedding in Engineering Domains -- Towards an Interpretable Functional Image-Based Classifier: Dimensionality -- Reduction of High-Density Di use Optical Tomography Data -- On Ensemble Learning for Mental Workload Classification -- Decision-making over compact preference structures -- User-Like Bots for Cognitive Automation: A Survey -- On Channel Selection for EEG-based Mental Workload Classification -- What Song Am I Thinking Of -- Path-Weights and Layer-Wise Relevance Propagation for Explainability of ANNs with fMRI Data -- Sensitivity Analysis for Feature Importance in Predicting Alzheimer?s Disease -- A Radically New Theory of how the Brain Represents and Computes with Probabilities.
• Sommario/riassunto: 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. .
• Titolo autorizzato: Machine Learning, Optimization, and Data Science
• ISBN: 3-031-53966-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996587861903316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 14506