Big Data Analytics

Name: Big Data Analytics | 9th International Conference, BDA 2021, Virtual Event, December 15-18, 2021, Proceedings
Brand: Springer
Price: 80.24 EUR
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9th International Conference, BDA 2021, Virtual Event, December 15-18, 2021, Proceedings

Satish Narayana Srirama Jerry Chun-Wei Lin Raj Bhatnagar Sonali Agarwal P. Krishna Reddy(Herausgeber*in)

Springer (Verlag)

Erschienen am 1. Januar 2022

XIII, 355 Seiten

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978-3-030-93620-4 (ISBN)

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Inhalt

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
3.4 Limitations
4 Conclusions
References
Predictive Analytics for Recognizing Human Activities Using Residual Network and Fine-Tuning
1 Introduction
2 Related Work
3 Proposed Approach
3.1 Data Preprocessing
3.2 Data Augmentation
3.3 Improved ResNet-50 Implementation
3.4 Fine Tuning
4 Experiment and Results
5 Performance Comparison
6 Conclusion
References
DXML: Distributed Extreme Multilabel Classification
1 Introduction
2 Previous Work
3 Distributed Memory Implementation
3.1 Some More Detail on Training
3.2 Hybrid MPI and OpenMP Parallel Implementation
4 Numerical Experiments
5 Conclusion
References
An Efficient Distributed Coverage Pattern Mining Algorithm
1 Introduction
2 Related Work
3 About Coverage Patterns
4 Proposed Approach
4.1 Basic Idea
4.2 The DCPM Approach
4.3 An Illustrative Example
5 Performance Evaluation
5.1 Effect of Variations in MinRF
5.2 Effect of Varying maxOR on Data Shuffled
5.3 Effect of Variations in maxOR
5.4 Effect of Variations in NM
5.5 Effect of Variations in minCS
6 Conclusion
References
Outcomes of Speech to Speech Translation for Broadcast Speeches and Crowd Source Based Speech Data Collection Pilot Projects
1 Introduction
2 Speech to Speech Translation and Performance Measurement Platform for Broadcast Speeches and Talks
2.1 Challenges Faced While Building the SSMT Pipeline
3 CSTD-Telugu Corpus: Crowd-Sourced Approach for Large-Scale Speech Data Collection
3.1 Overview of the Pipeline
4 Conclusion and Future Work
References
Author Index

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