Machine Learning, Optimization, and Data Science
Springer International Publishing (Verlag)
978-3-031-82483-8 (ISBN)
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The three-volume set LNAI 15508-15510 constitutes the refereed proceedings of the 10th International Conference on Machine Learning, Optimization, and Data Science, LOD 2024, held in Castiglione della Pescaia, Italy, during September 22-25, 2024.
This year, in the LOD Proceedings decided to also include the papers of the fourth edition of the Symposium on Artificial Intelligence and Neuroscience (ACAIN 2024).
The 79 full papers included in this book were carefully reviewed and selected from 127 submissions. The LOD 2024 proceedings focus on machine learning, deep learning, AI, computational optimization, neuroscience and big data that includes invited talks, tutorial talks, special sessions, industrial tracks, demonstrations and oral and poster presentations of refereed papers.
.- Exploring Explainable Machine Learning for Enhanced Ship Performance Monitoring.
.- Identifying Potential Key Point of Sale Customers Using Network Centrality.
.- Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning.
.- Predicting Multiple Sclerosis Worsening Using Stratification Based and Time Dependent Variables Extracted from Routine Visits Data.
.- Predicting University Dropout Rates Using Machine Learning: UniCt case.
.- Investigating on Gradient Regularization for Testing Neural Networks.
.- SKIE SRL: Structured Key Information Extraction from Business Documents using Statistical Relational Learning.
.- Leveraging LLM powered Systems to Accelerate Mycobacterium tuberculosis Research Step One: From Documents to the Vectorstore.
.- Vegvisir: Probabilistic model (VAE) for viral T cell epitope prediction.
.- Tiny Long Short Term Memory Model for Resource Constrained Prediction of Battery Cycle Life.
.- Compact Artificial Neural Network Models for Predicting Protein Residue RNA Base Binding.
.- FWin transformer for dengue prediction under climate and ocean influence.
.- ENGinnSAND: A Reference Dataset for Monocular Depth Prediction of Line Structures.
.- Topological Layering of Mouse Brain Activity in Light Sheet Microscopy Datasets.
.- A Constraint Based Savings Algorithm for the Traveling Salesman Problem.
.- Gaussian process interpolation with conformal prediction: methods and comparative analysis.
.- Using embeddings of pre trained models for cross database dysarthria detection: supervised vs. self supervised approach.
.- Personality Profiling for Literary Character Dialogue Agents with Human Level Attributes.
.- Integrating Logit Space Embeddings for Reliable Out of Distribution Detection.
.- A Computational Framework for Identifying Salient Moments in Motion Capture Data.
.- Machine Learning for the Evaluation of the Nephrops Norvegicus Population.
.- Enhancing Cluster Based Topic Models through Parametric Dimensionality Reduction with Transformer Encoders.
.- Enhancing Arrhythmia Detection Using an Ensemble of Transformer Models for Heartbeat Classification.
.- Rapidly Computing Approximate Graph Convex Hulls via FastMap.
.- Deep Gaussian mixture model for unsupervised image segmentation.
.- Address Classification in E commerce Logistics Using Federated Learning.
| Erscheint lt. Verlag | 7.3.2025 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | X, 389 p. 107 illus. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | artificial general intelligence • Artificial Intelligence • Big Data • cognitive science • Computational Neuroscience • Computational Optimization • data analytics • Data Science • Deep learning • Deep Reinforcement Learning • Foundation Models • generative artificial intelligence • Global Optimization • Large Language Models • machine learning • Multi-Objective Optimization • Neural networks • Neuroscience • Optimization • Reinforcement Learning |
| ISBN-10 | 3-031-82483-0 / 3031824830 |
| ISBN-13 | 978-3-031-82483-8 / 9783031824838 |
| Zustand | Neuware |
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