001461433 000__ 06354cam\a2200757\i\4500
001461433 001__ 1461433
001461433 003__ OCoLC
001461433 005__ 20230503003352.0
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001461433 008__ 230315s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001461433 019__ $$a1372322498$$a1372398834
001461433 020__ $$a9783031255991$$q(electronic bk.)
001461433 020__ $$a3031255992$$q(electronic bk.)
001461433 020__ $$z9783031255984
001461433 0247_ $$a10.1007/978-3-031-25599-1$$2doi
001461433 035__ $$aSP(OCoLC)1372631889
001461433 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCF
001461433 049__ $$aISEA
001461433 050_4 $$aQ325.5
001461433 08204 $$a006.3/1$$223/eng/20230315
001461433 1112_ $$aLOD (Conference)$$n(8th :$$d2022 :$$cCertosa di Pontignano, Italy).
001461433 24510 $$aMachine learning, optimization, and data science :$$b8th International Workshop, LOD 2022, Certosa di Pontignano, Italy, September 19-22, 2022, revised selected papers.$$nPart I /$$cGiuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos Pardalos, Giuseppe Di Fatta, Giovanni Giuffrida, Renato Umeton, editors.
001461433 24630 $$aLOD 2022
001461433 264_1 $$aCham :$$bSpringer,$$c[2023]
001461433 264_4 $$c©2023
001461433 300__ $$a1 online resource (xxiv, 616 pages) :$$billustrations (chiefly color).
001461433 336__ $$atext$$btxt$$2rdacontent
001461433 337__ $$acomputer$$bc$$2rdamedia
001461433 338__ $$aonline resource$$bcr$$2rdacarrier
001461433 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v13810
001461433 500__ $$aInternational conference proceedings.
001461433 500__ $$aIncludes author index.
001461433 5050_ $$aExplainable 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.
001461433 506__ $$aAccess limited to authorized users.
001461433 520__ $$aThis 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.
001461433 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 15, 2023).
001461433 650_0 $$aMachine learning$$vCongresses.
001461433 650_0 $$aMathematical optimization$$vCongresses.
001461433 655_0 $$aElectronic books.
001461433 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001461433 655_7 $$aConference papers and proceedings.$$2lcgft
001461433 7001_ $$aNicosia, Giuseppe,$$eeditor.
001461433 7001_ $$aOjha, Varun,$$eeditor.
001461433 7001_ $$aLa Malfa, Emanuele,$$eeditor.
001461433 7001_ $$aLa Malfa, Gabriele,$$eeditor.
001461433 7001_ $$aPardalos, P. M.$$q(Panos M.),$$d1954-$$eeditor.
001461433 7001_ $$aDi Fatta, Giuseppe,$$eeditor.
001461433 7001_ $$aGiuffrida, Giovanni,$$eeditor.
001461433 7001_ $$aUmeton, Renato,$$eeditor.
001461433 77608 $$iPrint version:$$aNicosia, Giuseppe$$tMachine Learning, Optimization, and Data Science$$dCham : Springer,c2023$$z9783031255984
001461433 830_0 $$aLecture notes in computer science ;$$v13810.$$x1611-3349
001461433 852__ $$bebk
001461433 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-25599-1$$zOnline Access$$91397441.1
001461433 909CO $$ooai:library.usi.edu:1461433$$pGLOBAL_SET
001461433 980__ $$aBIB
001461433 980__ $$aEBOOK
001461433 982__ $$aEbook
001461433 983__ $$aOnline
001461433 994__ $$a92$$bISE