01746nam 2200409 450 991071729020332120220301152333.0(CKB)5470000002529320(OCoLC)1300782301(EXLCZ)99547000000252932020220301d2011 ua 0engurcn|||||||||txtrdacontentcrdamediacrrdacarrierSimulation of the effects of groundwater withdrawals on water-level altitudes in the Sparta Aquifer in the Bayou Meto Grand-Prairie Area of eastern Arkansas, 2007-37 /by Brian R. Clark, Drew A. Westerman, and D. Todd Fugitt ; prepared in cooperation with the Arkansas Natural Resources CommissionReston, Virginia :U.S. Department of the Interior, U.S. Geological Survey,2011.1 online resource (iii, 9 pages) color illustrations, color mapsScientific investigations report ;2011-5215Includes bibliographical references (page 9).Water tableArkansasGrand Prairie RegionGroundwaterArkansasGrand Prairie RegionSparta Aquifer (Ark. and La.)Water tableGroundwaterClark Brian R.33368Westerman Drew A.Fugitt D. T.Geological Survey (U.S.),Arkansas Natural Resources Commission.GPOGPOBOOK9910717290203321Simulation of the effects of groundwater withdrawals on water-level altitudes in the Sparta Aquifer in the Bayou Meto Grand-Prairie Area of eastern Arkansas, 2007-373512724UNINA11265nam 2200529 450 99655055650331620230929034705.03-031-43471-4(MiAaPQ)EBC30745866(Au-PeEL)EBL30745866(EXLCZ)992823456260004120230929d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierKnowledge Discovery, Knowledge Engineering and Knowledge Management 14th International Joint Conference, IC3K 2022, Valletta, Malta, October 24-26, 2022, Revised Selected Papers /Frans Coenen [and six others], editorsFirst edition.Berlin, Germany :Springer,[2023]©20231 online resource (368 pages)Communications in Computer and Information Science Series ;Volume 1842Print version: Coenen, Frans Knowledge Discovery, Knowledge Engineering and Knowledge Management Cham : Springer,c2023 9783031434709 Includes bibliographical references and index.Intro -- Preface -- Organization -- Contents -- Knowledge Discovery and Information Retrieval -- Electrocardiogram Two-Dimensional Motifs: A Study Directed at Cardio Vascular Disease Classification -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Cardiovascular Disease Classification Model Generation -- 4.1 Data Cleaning (Stage 1) -- 4.2 Motif and Discord Extraction (Stage 2) -- 4.3 Feature Selection (Stage 3) -- 4.4 Data Augmentation (Stage 4) -- 4.5 Feature Vector Generation (Stage 5) -- 4.6 Classification Model Generation (Stage 6) -- 4.7 Classification Model Usage (Stage 7) -- 5 Evaluation -- 5.1 Data Sets -- 5.2 Most Appropriate Feature Selection and Data Augmentation Techniques (Objective 1) -- 5.3 Most Appropriate Conflict Resolution Technique (Objective 2) -- 5.4 Operation Using Additional Features (Objective 3) -- 5.5 Comparison of 1D and 2D Motifs Discovery Approaches (Objective 4) -- 6 Conclusion -- References -- Degree Centrality Definition, and Its Computation for Homogeneous Multilayer Networks Using Heuristics-Based Algorithms -- 1 Motivation -- 1.1 Differences with the KDIR Conference Paper -- 2 Relevant Work -- 3 Decoupling Approach for Multilayer Networks -- 4 Degree Centrality for Graphs and Homogeneous MLNs -- 4.1 Impact of Layer Information on Accuracy -- 5 Accuracy of Degree Centrality Heuristics -- 5.1 First Heuristic for Accuracy (DC-A1) -- 5.2 Second Heuristic for Accuracy (DC-A2) -- 6 Heuristics for Precision -- 6.1 Heuristic 1 for Precision (DC-P1) -- 6.2 Heuristic 2 for Precision (DC-P2) -- 7 Data Sets and Computation Environments -- 7.1 Data Sets -- 7.2 Computation Environments Used -- 8 Discussion of Experimental Results -- 9 Conclusions and Future Work -- References -- A Dual-Stage Noise Training Scheme for Breast Ultrasound Image Classification -- 1 Introduction.1.1 Image Data Predicament in Medical Area -- 1.2 Related Work -- 1.3 Contributions -- 2 Speckle Noise -- 3 Methodology -- 3.1 Dataset Preparation -- 3.2 CNN Model Selection -- 3.3 Performance Metrics -- 3.4 A Dual-Stage Noise Training Scheme -- 4 Experiment Results -- 4.1 Stage 1 -- 4.2 Stage 2 -- 5 Conclusions -- References -- A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decision Support -- 1 Introduction -- 2 Optimization-Based Classification Models -- 2.1 A Multi-group Machine Learning Framework -- 2.2 A Multi-stage Multi-group Machine Learning Framework -- 2.3 Balancing Misclassification Levels vs Size of the Reserve Judgement Region -- 2.4 Applying Multi-stage BB-PSO/DAMIP to Real-World Problems -- 3 Results for Disease Diagnosis and Treatment Outcome prediction -- 3.1 Cardiovascular Disease -- 3.2 Diabetes -- 3.3 Alzheimer's Disease -- 3.4 Knee Osteoarthritis -- 4 Discussions -- References -- Comparative Assessment of Deep End-To-End, Deep Hybrid and Deep Ensemble Learning Architectures for Breast Cancer Histological Classification -- 1 Introduction -- 2 Material and Methods -- 2.1 Deep Learning and Transfer Learning -- 2.2 Experiment Configuration and Design -- 2.3 Data Preparation -- 2.4 Abbreviation -- 3 Results and Discussions -- 3.1 Overall Performance of the Deep End-to-End Architectures -- 3.2 Performance Comparison of Deep end-to-end Architectures -- 3.3 Comparison of Deep end-to-end, Hybrid and End-to-end Ensemble Learning Architectures -- 4 Threats of Validity -- 5 Conclusion and Future Work -- Appendix A: Deep Architectures four Performance Measures Validation Results -- References -- Knowledge Engineering and Ontology Development -- CIE: A Cloud-Based Information Extraction System for Named Entity Recognition in AWS, Azure, and Medical Domain -- 1 Introduction and Motivation.2 State of the Art in Science and Technology -- 2.1 Named Entity Recognition -- 2.2 ML and Deep Learning in Named Entity Recognition -- 2.3 Cloud Resource Management for Named Entity Recognition -- 2.4 Named Entity Recognition Frameworks -- 2.5 Related Research Projects -- 3 CIE Modeling and Implementation -- 3.1 CIE AWS Implementation -- 3.2 CIE Azure Implementation -- 4 Final Discussion and Conclusion -- References -- From Natural Language Texts to RDF Triples: A Novel Approach to Generating e-Commerce Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 3 Framework QART -- 3.1 Step A: Field Selection and Pre-processing -- 3.2 Step B: Text2Text Conversion -- 3.3 Step C: Text Triplifying -- 3.4 Implementation Aspects -- 4 Evaluating Template-Based Text Summarization -- 4.1 Setup and Procedures -- 4.2 Results -- 4.3 Discussion -- 5 Evaluating Automatic Text-to-Text Transformation -- 5.1 Setup and Procedures -- 5.2 Results -- 5.3 Discussion -- 6 Overall Discussion and Challenges -- 7 Conclusion -- References -- Situational Question Answering over Commonsense Knowledge Using Memory Nets -- 1 Introduction -- 2 Related Work -- 3 System Overview -- 3.1 Knowledge Engine -- 3.2 Semantic Parsing -- 3.3 Knowledge Extraction -- 3.4 Virtual Simulation -- 3.5 XAI -- 4 Evaluation -- 4.1 Instance Question Answering -- 4.2 Action Pattern Question Answering -- 5 Conclusion -- References -- Archives Metadata Text Information Extraction into CIDOC-CRM -- 1 Introduction -- 2 Related Work -- 3 Overview of Archives Metadata Representation in CIDOC-CRM -- 3.1 Extracted Information from ISAD(G) Elements -- 3.2 CIDOC-CRM Representation of the Events and Entities Extracted -- 4 The Extraction of Events and Entities from Semi-structured Text -- 4.1 Semantic Role Labelling Process Using GATE -- 4.2 Semantic Role Token Labelling Process Using BERT.5 Evaluation of the Extraction Process -- 5.1 Dataset -- 5.2 Evaluation Methodology and Results -- 5.3 Evaluation of the ANNIE Extraction Process -- 5.4 Evaluation of the BERT Extraction Process -- 6 Exploration of the Extracted Information -- 7 Conclusions and Future Work -- References -- Evolution of Computational Ontologies: Assessing Development Processes Using Metrics -- 1 Introduction -- 2 Related Work -- 3 Hypotheses on Ontology Evolution -- 4 Dataset Preparation and Analysis -- 5 Empirical Assessment of Hypotheses -- 5.1 Ontologies Grow During Their Lifetime (H1) -- 5.2 The Level of Change Decreases over Time (H2) -- 5.3 The Instances Are Introduced after the Initial Design (H3) -- 5.4 Ontology Complexity Increases with Rising Maturity (H4) -- 5.5 A Stereotypical Development Lifecycle Can Be Identified (H5) -- 6 Ontology Evolution or Revolution? -- 6.1 Most Ontologies Have Disruptive Change Events -- 6.2 The Size of Disruptive Change Events Varies -- 6.3 Disruptive Changes Come in Various Combinations -- 6.4 Sensitivity Analysis -- 7 Conclusion -- References -- System to Correct Toxic Expression with BERT and to Determine the Effect of the Attention Value -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Collecting Tweets -- 3.2 Preprocessing of Tweets -- 3.3 Creating a BERT Classifier -- 3.4 MASK Processing Conversion with BERT -- 3.5 Similarity Evaluation -- 4 Experimental Results -- 4.1 Classification Accuracy -- 4.2 Comparison of Various Patterns -- 4.3 Results of MASK Conversion by BERT -- 4.4 Results of the Three Evaluations -- 5 Conclusions -- References -- Knowledge Management and Information Systems -- Machine Learning Decision Support for Production Planning and Control Based on Simulation-Generated Data -- 1 Introduction -- 2 State of the Art -- 2.1 Production Planning and Control Systems.2.2 PPC Challenges and Possible Solutions -- 2.3 Fundamentals: Machine Learning -- 2.4 Related Work: Application of Machine Learning Within PPC -- 3 Framework for Development of an ML Decision Support System Based on Simulation Data -- 4 Case Study -- 4.1 Case Study Description -- 4.2 Results of the Case Study -- 5 Limitations -- 6 Conclusion and Outlook -- References -- FAIRification of CRIS: A Review -- 1 Introduction -- 2 Methodology -- 3 Results -- 3.1 FAIRification of CRIS -- 3.2 FAIRification of Workflows and Other Infrastructures -- 3.3 CRIS as an Input for RDM FAIRness Assessment -- 4 Discussion -- 4.1 Assessing the FAIRness of CRIS -- 4.2 Ecosystem -- 4.3 Factors for further FAIRification of CRIS -- 5 Conclusion -- Appendix 1: FAIR Principles -- Appendix 2 - Review Criteria -- References -- Measuring Augmented Reality and Virtual Reality Trajectory in the Training Environment -- 1 Introduction -- 2 Understanding the Terms -- 2.1 Defining Augmented Reality and Virtual Reality -- 2.2 History of Augmented Reality and Virtual Reality -- 2.3 Peer-Reviewed Research of Augmented and Virtual Reality -- 2.4 Virtual Reality in the Aviation Industry -- 3 Methodology in Measuring Effectiveness of VR Technology -- 4 Results of VR Technology versus Real-Life Simulation -- 4.1 The Timing of VR Technology Simulation in the Training Environment -- 4.2 VR Technology Simulation Resulting in Behavior Change -- 4.3 Findings in Non-VR Participants -- 5 Next Steps -- 6 Conclusion -- References -- DroNit Project: Improving Drone Usage for Civil Defense Applications -- 1 Introduction -- 2 Drone Types and Characteristics -- 3 Understanding the Needs of the Civil Defense of Niterói -- 3.1 Daily Activities and Roles of the Office -- 3.2 Current Use of Drones -- 3.3 Demands of Drone Usage -- 4 Challenges and Insights -- 4.1 Issues and Challenges.4.2 Recommendations for More Effective Drone Usage.Communications in computer and information science ;Volume 1842.Data miningCongressesInformation retrievalCongressesKnowledge managementCongressesData miningInformation retrievalKnowledge management006.3Coenen Frans1826-1904,MiAaPQMiAaPQMiAaPQBOOK996550556503316Knowledge discovery, knowledge engineering and knowledge management1935676UNISA