Big Data Analytics in Astronomy, Science, and Engineering : 11th International Conference on Big Data Analytics, BDA 2023, Aizu, Japan, December 5–7, 2023, Proceedings / / edited by Shelly Sachdeva, Yutaka Watanobe

• Autore: Sachdeva Shelly
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (324 pages)
• Disciplina: 005.3
• Soggetto topico: Application software; Database management; Computer networks; Computer and Information Systems Applications; Database Management; Computer Communication Networks
• Altri autori: WatanobeYutaka
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Data Management and Visualization -- AI-Based Assistance for Management of Oral Community Knowledge in Low-Resource and Colloquial Kannada Language -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Low-Resource ASR Model -- 3.2 N-Gram Fuzzy Search -- 3.3 Named Entity Recognition Using Active Learning -- 3.4 Knowledge Retrieval Using Question-Answering -- 4 Conclusions and Future Work -- References -- Topic Modeling Applied to Reddit Posts -- 1 Introduction -- 2 Related Works -- 2.1 Reddit as a Data Source -- 2.2 Topic Modeling -- 2.3 Evaluation of Topic Models -- 3 Methods of Topic Modeling -- 3.1 Latent Dirichlet Allocation -- 3.2 Non-negative Matrix Factorization -- 3.3 BERTopic -- 3.4 Evaluation -- 4 Data Acquisition and Preparation -- 4.1 Data Preprocessing -- 5 Developed Solution -- 6 Experiments with Reddit Data -- 6.1 Experiment 1: Connections Between Extracted Topics and Real-Life Events -- 6.2 Experiment 2: Comparison of the Selected Topics with Google Trends -- 7 Concluding Remarks -- References -- Twitter Sentiment Analysis in Resource Limited Language -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Dataset and Preprocessing -- 3.2 Recap of BERT Model -- 3.3 Experiment Design -- 4 Results and Observations -- 4.1 RQ1: Best Approach -- 4.2 RQ2: Best Model Performance -- 4.3 RQ3: Best Classifier -- 5 Future Work and Conclusion -- References -- Querying Healthcare Data in Knowledge-Based Systems -- 1 Introduction -- 1.1 OpenEHR as a Knowledge-Based System -- 1.2 Archetype as a Knowledge Graph Structure -- 1.3 Archetype Definition Language -- 1.4 Query Language -- 1.5 Query Builder -- 2 Related Work -- 2.1 Query Languages for EHRs -- 2.2 Existing Query Interfaces -- 2.3 Knowledge Graph in EHRs -- 3 Querying Knowledge-Based System -- 4 Implementation. ; 4.1 Implementation Details -- 4.2 Software and Hardware Configurations -- 5 Results -- 6 Conclusions -- References -- IGUANER - DIfferential Gene Expression and fUnctionAl aNalyzER -- 1 Introduction -- 2 Related Work -- 3 Components of the Iguaner Platform -- 4 The IGUANER Workflow -- 4.1 The Differential Gene Expression Analysis -- 4.2 Functional Analysis -- 5 Results -- 6 Conclusions -- References -- Data Science: Architectures and Systems -- Boosting Diagnostic Accuracy of Osteoporosis in Knee Radiograph Through Fine-Tuning CNN -- 1 Introduction -- 2 Literature Survey -- 3 Materials and Methods -- 3.1 Investigation Design and Dataset -- 3.2 Data Pre-processing -- 3.3 Transfer Learning with Fine Tuning -- 4 Results -- 4.1 Experimental Setup -- 4.2 Prediction Performance -- 4.3 Confusion Matrix -- 5 Conclusion -- References -- VLSI Implementation of Reconfigurable Canny Edge Detection Algorithm -- 1 Introduction -- 1.1 Image Processing -- 2 Related Works -- 3 Reconstructed Canny Edge Detection Algorithm -- 4 Results and Discussion -- 4.1 Experimental Setup -- 4.2 Simulation of Traditional Canny Algorithm -- 4.3 Simulation of Reconfigurable Canny Edge Detection Algorithm -- 4.4 Edge Detected Results -- 4.5 Comparison of the Design Characteristics -- 5 Conclusion -- References -- DMC Approach for Modeling Viral Transmission over Respiratory System -- 1 Introduction -- 2 System Model -- 3 Proposed Probabilistic Measures for Viral Transmission -- 4 Numerical Analysis -- 5 Conclusions -- References -- Machine Learning in Particle Physics -- 1 Introduction -- 2 Early Days and the Engine of Change -- 3 Computing in Particle Physics Today -- 4 Early Machine Learning: Classification, Regression, and Density Estimation -- 5 An Example of ML: Detector Simulation -- 6 Recent Methods in ML -- 7 Newer Methods from ML Used in Particle Physics. ; 8 New Paths in ML for Particle Physics -- 9 Conclusion -- References -- Data Science and Applications -- A Robust Ensemble Machine Learning Model with Advanced Voting Techniques for Comment Classification -- 1 Introduction -- 2 Background and Related Work -- 2.1 Text Classification -- 2.2 Programming Code Classification -- 2.3 Social Media Comment Classification -- 2.4 Ensemble Techniques for Classification -- 3 Supervised Machine Learning Algorithms -- 4 Ensemble Model -- 4.1 Hard-Voting (Max-Voting) -- 4.2 Averaging -- 4.3 Soft-Voting (Weighted-Averaging) -- 5 Methodology -- 5.1 Text Preprocessing -- 5.2 Proposed Ensemble Model -- 5.3 Evaluation Metrics -- 5.4 Experimental Environment -- 6 Experimental Result -- 7 Conclusion -- References -- Vector-Based Semantic Scenario Search for Vehicular Traffic -- 1 Introduction -- 2 Related Work -- 2.1 Image Captioning -- 2.2 Vector Databases -- 3 Methodology -- 3.1 Dataset Description -- 3.2 System Architecture -- 4 Experimental Setup -- 4.1 Data Preparation -- 4.2 Experimental Results -- 5 Conclusions and Future Work -- References -- Searching for Short M-Dwarf Flares by Machine Learning Method -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Related Works -- 2 Method -- 2.1 Pretreatment -- 2.2 CNN Network Architecture -- 2.3 Fitting Flares -- 3 Results -- 4 Summary and Conclusion -- References -- Vayu Vishleshan: AQI Monitoring and Reduction Analysis -- 1 Introduction -- 1.1 Air Pollution -- 1.2 Air Quality Index (AQI) -- 1.3 AQI Calculation -- 1.4 Reducing Air Pollution -- 2 Literature Survey -- 3 Problem Statement -- 4 Proposed Vayu Vishleshan Framework -- 4.1 Monitoring of AQI -- 4.2 Communication System -- 4.3 Reduction of AQI -- 5 Results and Findings -- 5.1 Data Collection -- 5.2 Data Analysis and Visualization -- 6 Conclusion -- References. ; Efficient Knowledge Graph Embeddings via Kernelized Random Projections -- 1 Introduction -- 2 Related Works -- 2.1 Translational and Factorization-Based Models -- 2.2 Bilinear Models -- 3 Proposed Approach -- 3.1 Feature Extraction -- 3.2 Kernelized Random Projection for Link Prediction -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Setup -- 4.3 Results and Analysis -- 4.4 Ablation Study -- 4.5 Case Study on Large-Scale KG -- 5 Conclusion -- References -- Cyber Systems and Information Security -- From Silos to Unity: Seamless Cross-Platform Gaming by Leveraging Blockchain Technology -- 1 Introduction -- 2 Related Work -- 3 Blockchain Fundamentals in Gaming -- 4 The Transformative Potential of Blockchain in Gaming -- 4.1 True Ownership and Blockchain's Assurance of In-Game Assets -- 4.2 Unified Player Identity and Profile Management -- 4.3 Seamless Interactions Across Platforms -- 4.4 Bridging Gaming Platforms and Economies with Blockchain -- 5 Applying Blockchain to Gaming -- 5.1 Addressing Challenges with Blockchain -- 5.2 Registering a Digital Asset -- 5.3 Transferring an Asset Between Players -- 6 Potential Benefits and Limitations -- 7 The Road Ahead: Collaborative Efforts and Standardization -- 8 Summary and Conclusions -- References -- Analysis of Job Processing Data - Towards Large Cloud Infrastructure Operation Simulation -- 1 Introduction -- 2 Shaping the Data Model -- 3 Data Cleaning and Preprocessing -- 4 Clustering Cloud Center Data -- 4.1 Comparison of Clustering Algorithms -- 4.2 Clustering Workflow Data Using K-Means -- 5 Analysis of Clustering Results -- 6 Generation of the Synthetic Workflow Streams -- 7 Concluding Remarks -- References -- Exploring Approaches to Detection of Anomalies in Streaming Data -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 3.1 Paper Mill Dataset -- 3.2 EMCA's LogServer Dataset. ; 3.3 Data Splitting -- 3.4 Performance Assessment -- 3.5 Models Used in Experiments -- 3.6 Methodology -- 4 Experimental Results -- 4.1 Performance of Basic Models -- 4.2 Application of Gaussian Dropout and Self Attention -- 4.3 Application of Active Learning -- 4.4 Applying Change Statistics -- 4.5 Piecewise Aggregate Approximation -- 4.6 Attempt to Remove Trends from the Data -- 4.7 Summary of Experimental Results for the Paper Mill Dataset -- 4.8 LogServer Dataset Experiments -- 5 Concluding Remarks -- References -- Blockchain-Based Framework for Healthcare 5.0 -- 1 Introduction -- 1.1 Motivation -- 1.2 Research Contributions -- 2 Related Work -- 3 Blockchain-Enabled Framework Architecture -- 3.1 Blockchain -- 3.2 Key Management -- 3.3 Encryption and Validation Scheme -- 3.4 Smart Contract Layer -- 3.5 Medical Data Storage -- 4 Medical Information Exchange and Covid-19 Data Analysis -- 5 Results -- 6 Conclusions -- References -- Semantics for Resource Selection in Next Generation Internet of Things Systems -- 1 Introduction -- 2 Related Work -- 2.1 Next Generation IoT Applications -- 2.2 Resource Selection -- 2.3 Semantic Data Processing -- 3 Proposed Approach -- 3.1 Knowledge Representation -- 3.2 SPARQL Based Selection -- 3.3 Graph-Based Selection -- 3.4 Multicriteria Analysis Supported Selection -- 4 Concluding Remarks -- References -- Author Index.
• Sommario/riassunto: This book constitutes the proceedings of the 11th International Conference on Big Data Analytics in Astronomy, Science, and Engineering, BDA 2023, which took place in Aizu, Japan during December 5–7, 2023. The 19 full papers included in this book were carefully reviewed and selected from 55 submissions. They were organized in topical sections as follows: Data management and visualization; data science: architectures and systems; data science and applications; and cyber systems and information security. .
• Titolo autorizzato: Big Data Analytics in Astronomy, Science, and Engineering
• ISBN: 3-031-58502-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910855398803321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 14516