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Machine Learning, Optimization, and Data Science: 8th International Conference, LOD 2022, Certosa di Pontignano, Italy, September 1822, 2022, Revised Selected Papers, Part II 1st ed. 2023 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 582 pages, height x width: 235x155 mm, weight: 920 g, 152 Illustrations, color; 33 Illustrations, black and white; XXIV, 582 p. 185 illus., 152 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 13811
  • Izdošanas datums: 10-Mar-2023
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031258908
  • ISBN-13: 9783031258909
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Machine Learning, Optimization, and Data Science: 8th International Conference, LOD 2022, Certosa di Pontignano, Italy, September 1822, 2022, Revised Selected Papers, Part II 1st ed. 2023Palielināt
  • Formāts: Paperback / softback, 582 pages, height x width: 235x155 mm, weight: 920 g, 152 Illustrations, color; 33 Illustrations, black and white; XXIV, 582 p. 185 illus., 152 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 13811
  • Izdošanas datums: 10-Mar-2023
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031258908
  • ISBN-13: 9783031258909
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031258909.html
Keywords:
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.
Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting.- Intelligent Robotic Process Automation for Supplier Document Management on E-Procurement Platforms.- Batch Bayesian Quadrature with Batch Updating Using Future Uncertainty Sampling.- Sensitivity analysis of Engineering Structures Utilizing Artificial Neural Networks and Polynomial.- Inferring Pathological Metabolic Patterns in Breast Cancer Tissue from Genome-Scale Models.- Deep Learning.- Machine Learning.- Reinforcement Learning.- Neural Networks.- Deep Reinforcement Learning.- Optimization.- Global Optimization.- Multi-Objective Optimization.- Computational Optimization.- Data Science.- Big Data.- Data Analytics.- Artificial Intelligence.- Detection of Morality in Tweets based on the Moral Foundation Theory.- Matrix completion for the prediction of yearly country and industry-level CO2 emissions.- A Benchmark for Real-Time Anomaly Detection Algorithms Applied in Industry 4.0.- A Matrix Factorization-based Drug-virus Link Prediction Method for SARS CoV.- Drug Prioritization.- Hyperbolic Graph Codebooks.- A Kernel-Based Multilayer Perceptron Framework to Identify Pathways Related to Cancer Stages.- Loss Function with Memory for Trustworthiness Threshold Learning: Case of Face and Facial Expression Recognition.- Machine learning approaches for predicting Crystal Systems: a brief review and a case study.- LS-PON: a Prediction-based Local Search for Neural Architecture Search.- Local optimisation of Nystrm samples through stochastic gradient descent.- Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting.- Intelligent Robotic Process Automation for Supplier Document Management on E-Procurement Platforms.- Batch Bayesian Quadrature with Batch Updating Using Future Uncertainty Sampling.- Sensitivity analysis of Engineering Structures Utilizing Artificial Neural Networks and Polynomial.- Inferring Pathological Metabolic Patterns in Breast Cancer Tissue from Genome-Scale Models.- Deep Learning.- Machine Learning.- Reinforcement Learning.- Neural Networks.- Deep Reinforcement Learning.- Optimization.- Global Optimization.- Multi-Objective Optimization.- Computational Optimization.- Data Science.- Big Data.- Data Analytics.- Artificial Intelligence.