LEADER 06225nam 22007575 450 001 996587861903316 005 20240214180843.0 010 $a3-031-53966-4 024 7 $a10.1007/978-3-031-53966-4 035 $a(MiAaPQ)EBC31161637 035 $a(Au-PeEL)EBL31161637 035 $a(DE-He213)978-3-031-53966-4 035 $a(EXLCZ)9930378469200041 100 $a20240214d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning, Optimization, and Data Science$b[electronic resource] $e9th International Conference, LOD 2023, Grasmere, UK, September 22?26, 2023, Revised Selected Papers, Part II /$fedited by Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos M. Pardalos, Renato Umeton 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (503 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14506 311 $a3-031-53965-6 327 $aIntegrated 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. 330 $aThis book constitutes the refereed proceedings of the 9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023, which took place in Grasmere, UK, in September 2023. The 72 full papers included in this book were carefully reviewed and selected from 119 submissions. The proceedings also contain 9 papers from and the Third Symposium on Artificial Intelligence and Neuroscience, ACAIN 2023. The contributions focus on the state of the art and the latest advances in the integration of machine learning, deep learning, nonlinear optimization and data science to provide and support the scientific and technological foundations for interpretable, explainable and trustworthy AI. . 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v14506 606 $aInformation technology$xManagement 606 $aComputer networks 606 $aElectronic digital computers$xEvaluation 606 $aComputer systems 606 $aArtificial intelligence 606 $aMachine learning 606 $aComputer Application in Administrative Data Processing 606 $aComputer Communication Networks 606 $aSystem Performance and Evaluation 606 $aComputer System Implementation 606 $aArtificial Intelligence 606 $aMachine Learning 615 0$aInformation technology$xManagement. 615 0$aComputer networks. 615 0$aElectronic digital computers$xEvaluation. 615 0$aComputer systems. 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 14$aComputer Application in Administrative Data Processing. 615 24$aComputer Communication Networks. 615 24$aSystem Performance and Evaluation. 615 24$aComputer System Implementation. 615 24$aArtificial Intelligence. 615 24$aMachine Learning. 676 $a005.3 700 $aNicosia$b Giuseppe$0241374 701 $aOjha$b Varun$01726448 701 $aLa Malfa$b Emanuele$01726449 701 $aLa Malfa$b Gabriele$01726450 701 $aPardalos$b Panos M$0318341 701 $aUmeton$b Renato$01726451 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996587861903316 996 $aMachine Learning, Optimization, and Data Science$94132119 997 $aUNISA