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001440294 020__ $$a9783030889425$$q(electronic bk.)
001440294 020__ $$a3030889424$$q(electronic bk.)
001440294 020__ $$z9783030889418
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001440294 0247_ $$a10.1007/978-3-030-88942-5$$2doi
001440294 035__ $$aSP(OCoLC)1275356908
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001440294 049__ $$aISEA
001440294 050_4 $$aQ174$$b.I5625 2021
001440294 08204 $$a501$$223
001440294 1112_ $$aInternational Conference on Discovery Science$$n(24th :$$d2021 :$$cOnline)
001440294 24510 $$aDiscovery science :$$b24th international conference, DS 2021, Halifax, NS, Canada, October 11-13, 2021 : proceedings /$$cCarlos Soares, Luis Torgo (eds.).
001440294 24630 $$aDS 2021
001440294 264_1 $$aCham :$$bSpringer,$$c[2021]
001440294 264_4 $$c©2021
001440294 300__ $$a1 online resource :$$billustrations
001440294 336__ $$atext$$btxt$$2rdacontent
001440294 337__ $$acomputer$$bc$$2rdamedia
001440294 338__ $$aonline resource$$bcr$$2rdacarrier
001440294 347__ $$atext file
001440294 347__ $$bPDF
001440294 4901_ $$aLecture notes in computer science. Lecture notes in artificial intelligence ;$$v12986
001440294 4901_ $$aLNCS sublibrary: SL7 - Artificial intelligence
001440294 500__ $$aInternational conference proceedings.
001440294 500__ $$aIncludes author index.
001440294 5050_ $$aApplications -- Automated Grading of Exam Responses: An Extensive Classification Benchmark -- Automatic human-like detection of code smells -- HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM -- Predicting reach to find persuadable customers: improving uplift models for churn prevention -- Classification -- A Semi-Supervised Framework for Misinformation Detection -- An Analysis of Performance Metrics for Imbalanced Classification -- Combining Predictions under Uncertainty: The Case of Random Decision Trees -- Shapley-Value Data Valuation for Semi-Supervised Learning -- Data streams -- A Network Intrusion Detection System for Concept Drifting Network Traffic Data -- Incremental k-Nearest Neighbors Using Reservoir Sampling for Data Streams -- Statistical Analysis of Pairwise Connectivity -- Graph and Network Mining -- FHA: Fast Heuristic Attack against Graph Convolutional Networks -- Ranking Structured Objects with Graph Neural Networks -- Machine Learning for COVID-19 -- Knowledge discovery of the delays experienced in reporting covid19 confirmed positive cases using time to event models -- Multi-Scale Sentiment Analysis of Location-Enriched COVID-19 Arabic Social Data -- Prioritization of COVID-19 literature via unsupervised keyphrase extraction and document representation learning -- Sentiment Nowcasting during the COVID-19 Pandemic -- Neural Networks and Deep Learning -- A Sentence-level Hierarchical BERT Model for Document Classification with Limited Labelled Data -- Calibrated Resampling for Imbalance and Long-Tails in Deep learning -- Consensus Based Vertically Partitioned Multi-Layer Perceptrons for Edge Computing -- Controlling BigGAN Image Generation with a Segmentation Network -- GANs for tabular healthcare data generation: a review on utility and privacy -- Preferences and Recommender Systems -- An Ensemble Hypergraph Learning framework for Recommendation -- KATRec: Knowledge Aware aTtentive Sequential Recommendations -- Representation Learning and Feature Selection -- Elliptical Ordinal Embedding -- Unsupervised Feature Ranking via Attribute Networks -- Responsible Artificial Intelligence -- Deriving a Single Interpretable Model by Merging Tree-based Classifiers -- Ensemble of Counterfactual Explainers. Riccardo Guidotti and Salvatore Ruggieri -- Learning Time Series Counterfactuals via Latent Space Representations -- Leveraging Grad-CAM to Improve the Accuracy of Network Intrusion Detection Systems -- Local Interpretable Classifier Explanations with Self-generated Semantic Features -- Privacy risk assessment of individual psychometric profiles -- The Case for Latent Variable vs Deep Learning Methods in Misinformation Detection: An Application to COVID-19 -- Spatial, Temporal and Spatiotemporal Data -- Local Exceptionality Detection in Time Series Using Subgroup Discovery -- Neural Additive Vector Autoregression Models for Causal Discovery in Time Series -- Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data.
001440294 506__ $$aAccess limited to authorized users.
001440294 520__ $$aThis book constitutes the proceedings of the 24th International Conference on Discovery Science, DS 2021, which took place virtually during October 11-13, 2021. The 36 papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: applications; classification; data streams; graph and network mining; machine learning for COVID-19; neural networks and deep learning; preferences and recommender systems; representation learning and feature selection; responsible artificial intelligence; and spatial, temporal and spatiotemporal data.
001440294 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 18, 2021).
001440294 650_0 $$aScience$$xPhilosophy$$vCongresses.
001440294 650_0 $$aDiscoveries in science$$vCongresses.
001440294 650_0 $$aResearch$$xAutomation$$vCongresses.
001440294 650_0 $$aMachine learning$$vCongresses.
001440294 650_0 $$aData mining$$vCongresses.
001440294 650_6 $$aDécouvertes scientifiques$$vCongrès.
001440294 650_6 $$aRecherche$$xAutomatisation$$vCongrès.
001440294 650_6 $$aApprentissage automatique$$vCongrès.
001440294 650_6 $$aExploration de données (Informatique)$$vCongrès.
001440294 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001440294 655_7 $$aConference papers and proceedings.$$2lcgft
001440294 655_7 $$aActes de congrès.$$2rvmgf
001440294 655_0 $$aElectronic books.
001440294 7001_ $$aSoares, Carlos,$$eeditor.
001440294 7001_ $$aTorgo, Luís,$$eeditor.
001440294 77608 $$iPrint version:$$z3030889416$$z9783030889418$$w(OCoLC)1267585788
001440294 830_0 $$aLecture notes in computer science ;$$v12986.
001440294 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence.
001440294 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence.
001440294 852__ $$bebk
001440294 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-88942-5$$zOnline Access$$91397441.1
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001440294 980__ $$aBIB
001440294 980__ $$aEBOOK
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