12844nam 22008055 450 991052006040332120251107172703.03-030-93620-110.1007/978-3-030-93620-4(MiAaPQ)EBC6838842(Au-PeEL)EBL6838842(CKB)20275198300041(PPN)259385174(OCoLC)1290718862(DE-He213)978-3-030-93620-4(EXLCZ)992027519830004120211217d2021 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierBig 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 Reddy1st ed. 2021.Cham :Springer International Publishing :Imprint: Springer,2021.1 online resource (360 pages)Information Systems and Applications, incl. Internet/Web, and HCI,2946-1642 ;13147Print version: Srirama, Satish Narayana Big Data Analytics Cham : Springer International Publishing AG,c2021 9783030936198 Includes bibliographical references and index.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&amp -- 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.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.Information Systems and Applications, incl. Internet/Web, and HCI,2946-1642 ;13147Data miningArtificial intelligenceComputer engineeringComputer networksApplication softwareData structures (Computer science)Information theoryData Mining and Knowledge DiscoveryArtificial IntelligenceComputer Engineering and NetworksComputer and Information Systems ApplicationsData Structures and Information TheoryData 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.006.312Srirama Satish Narayana1978-MiAaPQMiAaPQMiAaPQBOOK9910520060403321Big data analytics1523196UNINA