Big Data Analytics : 9th International Conference, BDA 2021, Virtual Event, December 15-18, 2021, Proceedings / / edited by Satish Narayana Srirama, Jerry Chun-Wei Lin, Raj Bhatnagar, Sonali Agarwal, P. Krishna Reddy

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
• Edizione: 1st ed. 2021.
• Descrizione fisica: 1 online resource (360 pages)
• Disciplina: 006.312
• Soggetto topico: Data mining; Artificial intelligence; Computer engineering; Computer networks; Application software; Data structures (Computer science); Information theory; Data Mining and Knowledge Discovery; Artificial Intelligence; Computer Engineering and Networks; Computer and Information Systems Applications; Data Structures and Information Theory
• Persona (resp. second.): SriramaSatish Narayana <1978->
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Medical and Health Applications -- MAG-Net: Multi-task Attention Guided Network for Brain Tumor Segmentation and Classification -- 1 Introduction -- 2 Literature Review -- 3 Proposed Work -- 3.1 Encoder -- 3.2 Decoder -- 3.3 Classification -- 4 Experiment and Results -- 4.1 Dataset Setup -- 4.2 Training and Testing -- 4.3 Results -- 5 Conclusion -- References -- Smartphone Mammography for Breast Cancer Screening -- 1 Introduction -- 2 Related Work -- 3 System Description -- 4 Simulation -- 5 Results -- 6 Conclusion and the Future Work -- References -- Bridging the Inferential Gaps in Healthcare -- 1 Introduction -- 2 Digital Health -- 3 Digital Twin -- 3.1 Patient Digital Twin -- 3.2 Physician Digital Twin -- 4 Digital Triplet -- 5 Artificial Intelligence and Related Technologies -- 6 Knowledge Graphs -- 7 Conclusion -- References -- 2AI& -- 7D Model of Resistomics to Counter the Accelerating Antibiotic Resistance and the Medical Climate Crisis -- 1 Introduction -- 2 Related Work -- 3 The Root Cause of Antibiotic Resistance -- 3.1 Solving the Antibiotic Misuse Crisis -- 3.2 Antibiotic Overuse and Underuse -- 4 The Solution to Contain Antibiotic Resistance -- 4.1 Diseasomics Knowledge Graph -- 4.2 Categorical Belief Knowledge Graph -- 4.3 Vector Embedding Through Node2Vec -- 4.4 Probabilistic Belief Knowledge Graph -- 4.5 De-escalation (Site-Specific and Patient-Specific Resistance) -- 4.6 The Right Automated Documentation -- 5 Conclusion -- References -- Tooth Detection from Panoramic Radiographs Using Deep Learning -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Annotation -- 3.3 Data Preprocessing -- 3.4 Object Detection Model -- 3.5 Performance Analysis -- 4 Experimental Results -- 4.1 Localization Loss -- 4.2 Total Loss -- 4.3 Learning Rate. ; 4.4 Steps Per Epoch -- 5 Comparative Study -- 5.1 Comparison with Clinical Experts -- 5.2 Comparison with Other Works -- 6 Conclusion -- References -- Machine/Deep Learning -- Hate Speech Detection Using Static BERT Embeddings -- 1 Introduction -- 1.1 BERT -- 1.2 Attention in Neural Networks -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Static BERT Embedding Matrix -- 4 Experiments -- 4.1 Choice of Dataset -- 4.2 Neural Network Architectures and Testing Environment -- 5 Results and Discussion -- 6 Conclusion -- References -- Fog Enabled Distributed Training Architecture for Federated Learning -- 1 Introduction -- 2 Related Work -- 3 Decentralized Federated Learning -- 3.1 Architecture -- 3.2 Online Training and Data Privacy -- 4 Evaluation and Results -- 4.1 Docker Based Fog Federation Framework -- 4.2 FMCW Radar Dataset for Federated Learning -- 4.3 Results and Analysis -- 5 Conclusions and Future Work -- References -- Modular ST-MRF Environment for Moving Target Detection and Tracking Under Adverse Local Conditions -- 1 Introduction -- 1.1 Data Collection and Pre-processing -- 1.2 Medium Transmission Channel Estimation -- 1.3 Intensity Value Prior -- 2 Machine Learning Assisted ST-MRF Environment for Moving Target Tracking -- 2.1 Expectation Maximization Algorithm -- 2.2 Clustering Assisted Edge-Preserving ROI Segmentation -- 3 Conclusion -- References -- Challenges of Machine Learning for Data Streams in the Banking Industry -- 1 Introduction -- 1.1 Background -- 2 Banking Information Systems -- 2.1 Online Learning Use Cases in the Banking Sector -- 2.2 Categorization of Information System Data Sources -- 2.3 Banking Sector Applications and Use Cases -- 2.4 Challenging Use Cases of Online Learning in the Banking Sector -- 3 Literature Review on IT Stream Learning -- 3.1 Learning Methods from IT Logs: Anomaly Detection and Log Mining. ; 3.2 Pattern Mining from Graph Data Streams -- 3.3 Streaming Frameworks for Mining IT and DevOps Events -- 4 Data Science Challenges for IT Data Stream Learning -- 4.1 Multiple Data Streams Mining for Anomaly Detection -- 4.2 Online Learning from Heterogeneous Data Streams -- 5 Data Engineering in Applying Models in Production -- 5.1 Model Governance Challenges Regarding Banks Regulations -- 5.2 Engineering Challenges for Deploying Online Learning Models -- 6 Conclusion -- References -- A Novel Aspect-Based Deep Learning Framework (ADLF) to Improve Customer Experience -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Design -- 5 Implementation -- 6 Results and Discussion -- 7 Conclusion and Future Work -- References -- IoTs, Sensors, and Networks -- Routing Protocol Security for Low-Power and Lossy Networks in the Internet of Things -- 1 Introduction -- 1.1 The Role of Big Data in IoT -- 1.2 The RPL Protocol -- 1.3 The Cooja Simulator -- 2 Related Works -- 3 Problem Statement -- 4 Methodology -- 4.1 Implementing the SHA Encryption -- 4.2 Methodology Followed -- 4.3 Running the Cooja Simulator -- 4.4 Simulating the Unencrypted RPL Protocol -- 4.5 Simulating the Unencrypted RPL Protocol -- 5 Results and Discussions -- 6 Future Work -- 7 Conclusion -- References -- MQTT Protocol Use Cases in the Internet of Things -- 1 Introduction -- 2 Use Case 1: Home Automation Using Node-Red -- 2.1 Setup of Virtual Server in AWS and Interconnecting Node-Red, MQTT Box, Mosquitto Broker and AWS -- 2.2 The Home Automation System in Node-Red -- 2.3 Big Data in Home Automation -- 2.4 Measurement of Message Throughput and Message Speed Through Nodes -- 2.5 Throughput of the Message Transmission -- 3 Use Case 2: Vehicular Network -- 3.1 Connecting 100 Vehicles and Analysis of Statistics in the Dashboard in AWS Simulator -- 3.2 Big Data in a Vehicular Network. ; 4 Justifications to Prove MQTT is More Efficient than Other Protocols -- 4.1 Use Cases Basis -- 4.2 Comparative Analysis of MQTT, CoAP and HTTP -- 5 Features of MQTT -- 5.1 Security -- 5.2 QoS -- 5.3 Last Will Message -- 6 Conclusion -- References -- Large-Scale Contact Tracing, Hotspot Detection, and Safe Route Recommendation -- 1 Introduction -- 2 Related Works -- 3 Contact Tracing -- 3.1 Intuition Behind t/2 Mins -- 3.2 How Lat/long Distances Map to Circular d m? -- 3.3 Static Case -- 3.4 Dynamic Case -- 4 Potential Hotspot Detection -- 5 Safe Route Recommendation -- 6 Complexity Analysis -- 7 Empirical Demonstration -- 7.1 Contact Tracing Experiment -- 7.2 Hotspot Detection Experiment -- 7.3 Safe Route Recommendation Experiment -- 8 Conclusion and Future Work -- References -- Current Trends in Learning from Data Streams -- 1 Introduction -- 2 The Importance of Forgetting -- 3 Learning Rare Cases -- 3.1 ChebyUS: Chebyshev-Based Under-Sampling -- 3.2 ChebyOS: Chebyshev-Based Over-Sampling -- 3.3 Experimental Evaluation -- 4 Learning to Learn: Hyperparameter Tunning -- 4.1 Dynamic Sample Size -- 4.2 Stream-Based Implementation -- 4.3 Experimental Evaluation -- 5 Conclusions -- References -- Fundamentation -- Diagnostic Code Group Prediction by Integrating Structured and Unstructured Clinical Data -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Data Preparation and Preprocessing -- 3.2 Feature Engineering -- 3.3 Disease Group Prediction Models -- 3.4 Model Ensembling -- 4 Results and Analysis -- 4.1 Baseline Models and Experimental Setup -- 4.2 Results -- 4.3 Discussions -- 5 Conclusion and Future Work -- References -- SCIMAT: Dataset of Problems in Science and Mathematics -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 3.1 Existing DeepMind Datasets -- 3.2 Our New Datasets -- 3.3 Sample Question in Mathematics. ; 3.4 Sample Questions in Science -- 4 Experimental Results and Analysis -- 4.1 Transformer Architecture and Char2Char Encoding -- 4.2 Computational Resources Used -- 4.3 Dataset Organization and Generation -- 4.4 Evaluation Criterion and Splitting of Train and Test -- 4.5 Comparison of Train and Test Accuracy -- 4.6 Discussion of Test Accuracy for Generated Datasets -- 5 Conclusion -- References -- Rank-Based Prefetching and Multi-level Caching Algorithms to Improve the Efficiency of Read Operations in Distributed File Systems -- 1 Introduction -- 2 Related Work -- 3 Proposed Algorithms -- 3.1 Architecture -- 3.2 Rank-Based Prefetching -- 3.3 Multi-level Caching -- 3.4 Reading from the DFS -- 3.5 Writing to DFS -- 4 Experimental Results -- 4.1 Parameters -- 4.2 Experimental Setup -- 4.3 Simulation Results -- 5 Conclusion -- References -- Impact-Driven Discretization of Numerical Factors: Case of Two- and Three-Partitioning -- 1 Introduction -- 2 Related Work -- 3 Motivation -- 4 Our Approach -- 4.1 Key Intuition -- 4.2 Step Function -- 4.3 Definitions -- 4.4 Method -- 5 Evaluation -- 5.1 Data Sets -- 5.2 Results and Discussion -- 6 Conclusion -- References -- Towards Machine Learning to Machine Wisdom: A Potential Quest -- 1 Introduction -- 2 Intelligence -- 2.1 Human Intelligence -- 2.2 Artificial Intelligence -- 3 Wisdom -- 3.1 Natural Wisdom: Human Wisdom -- 3.2 Artificial Wisdom: Beyond Artificial Intelligence -- 4 Transition Scope from Artificial Intelligence to Artificial Wisdom Systems -- 4.1 Principles of Artificial Wisdom Systems -- 5 Challenges -- 6 Conclusions -- References -- Pattern Mining and data Analytics -- Big Data over Cloud: Enabling Drug Design Under Cellular Environment -- 1 Introduction -- 2 Materials and Methods -- 3 Results and Discussion -- 3.1 Spark-Based Processing of MD Simulation Data -- 3.2 Benchmarks and Insights. ; 3.3 Framework for Cloud-Based MD Simulation Service.
• Sommario/riassunto: This book constitutes the proceedings of the 8th International Conference on Big Data Analytics, BDA 2021, which took place during December 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 full and 3 short papers included in this volume were carefully reviewed and selected from 41 submissions. The contributions were organized in topical sections named as follows: medical and health applications; machine/deep learning; IoTs, sensors, and networks; fundamentation; pattern mining and data analytics.
• Titolo autorizzato: Big data analytics
• ISBN: 3-030-93620-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910520060403321
• Lo trovi qui: Univ. Federico II
• Serie: Information Systems and Applications, incl. Internet/Web, and HCI, . 2946-1642 ; ; 13147