Big data intelligence and computing : international conference, DataCom 2022, Denarau Island, Fiji, December 8-10, 2022, proceedings / / edited by Ching-Hsien Hsu [and four others]

• Pubblicazione: Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]; ©2023
• Edizione: 1st ed. 2023.
• Descrizione fisica: 1 online resource (570 pages)
• Disciplina: 005.7
• Soggetto topico: Artificial intelligence; Big data
• Persona (resp. second.): HsuChing-Hsien
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Big Data Algorithm and Systems -- Tiansuan Constellation -- 1 Introduction -- 2 Related Work -- 2.1 Design Overview -- 2.2 Edge Computing and AI in Space -- 3 Method -- 3.1 Datasets -- 3.2 Model Design -- 3.3 Hardware -- 4 Experimental Results -- 4.1 The Comparison -- 4.2 Future Work -- 5 Conclusion -- References -- Adopting a Deep Learning Split-Protocol Based Predictive Maintenance Management System for Industrial Manufacturing Operations -- 1 Introduction -- 2 Literature Reviews -- 3 Overview of Predictive Maintenance and Industries 4.0 -- 4 Strategy to Adopt in Predictive Maintenance -- 4.1 Application of Machine Learning Methods to Predict Predictive Maintenance (PdM) -- 5 Pillars of the Total Predictive Maintenance in Industries 4.0 -- 6 Quantum Computing and Digital Manufacturing 4.0 -- 7 Reliability-Centered Maintenance (RCM) -- 7.1 Benefits of the RCM Approach -- 7.2 Key Principles of Reliability-Centered Maintenance (RCM) -- 8 Intelligent Asset Management (IAM and RCM-Controlled Predictive Maintenance) -- 8.1 Wings Within the IAM -- 8.2 Benefits that Could Be Achieved from AIN -- 9 System Architectures and PDM in Industries 4.0 -- 10 AI-Enabled Split-Migration Architecture -- 11 Implementation Details -- 12 Conclusion -- References -- A Digital Twin for Bus Operation in Public Urban Transportation Systems -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Simulation of Urban Mobility (SUMO) -- 3.2 Configuring SUMO to Reproduce Observed Traffic -- 3.3 Bus Operation Digital Twin (BODIT) -- 4 Experiments -- 4.1 Description of the Scenario -- 4.2 Experimental Setup -- 5 Results -- 6 Conclusion and Future Work -- References -- Predicting Residential Property Valuation in Major Towns and Cities on Mainland Fiji -- 1 Introduction -- 2 Literature Review. ; 2.1 Property Valuation Approaches -- 2.2 Feature Selection of Properties -- 2.3 Machine Learning Models for Property Predictions -- 3 Methodology -- 3.1 Data Analysis -- 3.2 Data Pre-processing -- 3.3 Data Visualization -- 4 Results and Discussion -- 4.1 Evaluation on Test Data -- 5 Conclusion -- 6 Limitations -- 7 Future Work -- References -- Designing an AI-Driven Talent Intelligence Solution: Exploring Big Data to Extend the TOE Framework -- 1 Introduction -- 2 Background of the Study -- 2.1 Ethical Implications of AI in Talent Management -- 2.2 The Application of AI in Talent Management -- 2.3 Career Services in Higher Education -- 2.4 Theories in Technological Adoption -- 3 Design Science Methodology -- 4 Proposed AI Oriented TM Approach -- 5 Discussion and Conclusion -- References -- Development of Bilingual Chatbot for University Related FAQs Using Natural Language Processing and Deep Learning -- 1 Introduction -- 2 Background -- 2.1 What is a Chatbot and How it Works? -- 3 Methodology -- 3.1 Existing Studies -- 4 Proposed Chatbot -- 4.1 NLP Techniques Used -- 4.2 Chatbot Application Development on Web -- 5 Advantages of the Proposed System -- 5.1 24/7 Availability -- 5.2 Instant Response -- 5.3 Consistency -- 5.4 Bilingual -- 6 Chatbot Testing -- 7 Limitations -- 8 Recommendations and Future Scope -- 9 Conclusion -- References -- Machine Learning Algorithms Performance Investigation in Fake News Detection -- 1 Introduction -- 2 Traditional Media -- 2.1 Printed Media -- 2.2 Radio -- 2.3 Television -- 3 Modern Media -- 4 Research Objective -- 5 Methodology -- 6 Literature Review -- 6.1 Naïve Bayes -- 6.2 Decision Tree -- 6.3 Random Forest -- 6.4 Support Vector Machine (SVM) -- 6.5 K-Nearest Neighbors (KNN) -- 6.6 Logistic Regression -- 7 Findings -- 7.1 Algorithm Category Best Suited for Fake News Detection. ; 7.2 Top 6 Algorithms for Fake News Detection In Recent Five Years -- 7.3 Best Algorithm Performer for Fake News Detection -- 7.4 Naïve Bayes -- 7.5 Decision Tree -- 7.6 Random Forest -- 7.7 Support Vector Machine (SVM) -- 7.8 K-Nearest Neighbor(KNN) -- 7.9 Logistic Regression -- 7.10 Experimental Results Summary -- 8 Discussion -- 9 Conclusion -- 10 Future Work -- References -- Big Data Privacy and Security -- Robust Graph Embedding Recommendation Against Data Poisoning Attack -- 1 Introduction -- 2 Related Work -- 2.1 Graph Graph-Data Attack Methods -- 2.2 Graph-Data Defend Methods -- 3 Preliminaries -- 4 Our Methods -- 4.1 Pre-process GNN Supervised Detection Structure -- 4.2 Pre-process GNN Supervised Detection Method -- 4.3 Optimization of the Model -- 4.4 Model Evaluation Metric -- 5 Experiments -- 5.1 Data-Sets -- 5.2 Baseline Model -- 5.3 Experiment Analysis -- 5.4 Parameter Analysis -- 6 Conclusion -- References -- A Secure Sharing Framework Based on Cloud-Chain Fusion for SWIM -- 1 Introduction -- 2 Data Sharing Analysis -- 2.1 ATM Data and Its Classification -- 2.2 Features of ATM Data Sharing -- 2.3 ATM Data Sharing Trends -- 2.4 Security Requirements of ATM Data Sharing -- 3 Preliminary Knowledge -- 3.1 Cloud Storage -- 3.2 Blockchain -- 3.3 Feasibility of Cloud-Chain Chain Fusion Architecture -- 3.4 Ciphertext Policy Attribute Based Encryption (CPABE) -- 4 The ATM Information Security Framework Based on Cloud-Chain Fusion -- 4.1 ATM Data Sharing Framework -- 4.2 Data Publishing and Discovery Mechanism (DPAD) -- 4.3 Operation Record and Traceability Mechanism -- 4.4 Data Security Protection Mechanism -- 5 Function and Security Analysis -- 6 Future Research Directions -- 6.1 Entities' Attributes Mining and Attribute-Authority Relation Mining Based on Deep Learning. ; 6.2 The Decentralized CPABE Method for Access Control Based on Homomorphic Encryption and Blockchain -- 6.3 The Data Privacy Protection Based on Computation Offloading and Secure Multi-party Computation -- 7 Conclusion -- References -- Privacy-Protective Distributed Machine Learning Between Rich Devices and Edge Servers Using Confidence Level -- 1 Introduction -- 2 Related Work -- 3 Research Issues and the Proposed Method -- 3.1 Research Issues -- 3.2 Proposed Method -- 4 Implementation and Evaluation of Proposed Model -- 4.1 Dataset -- 4.2 Experimental Environment -- 4.3 Preliminary Experiments -- 4.4 Experiment 1 (Used only Personal Data for Training on Edge Device) -- 4.5 Experiment 2 (Adding Four Individuals Different from the Edge Server) -- 4.6 Consideration About the Amount of Training and Data -- 5 Summary and Future Work -- References -- A Novel Approach of Securing Medical Cyber Physical Systems (MCPS) from DDoS Attacks -- 1 Introduction -- 2 Related Work -- 3 System Model -- 4 Proposed Methodology -- 5 Results and Discussion -- 6 Conclusion -- References -- LTCS: Lightweight Tree-Chain Scheme for ADS-B Data Authentication -- 1 Introduction -- 2 Related Works -- 3 Our Proposed Scheme -- 4 Performance Analysis -- 5 Conclusion -- References -- Electronic and Remote Voting Systems: Solutions to Existing Problems - An Experimental Approach to Secure Voting Platforms on Endpoints -- 1 Introduction -- 2 Literature Review -- 3 Research Methodology -- 3.1 Why Do We Need to Secure Content Delivery Systems for Voting? -- 3.2 Is Multifactor Authentication not a Viable Option to Secure Platform Delivery? -- 3.3 Do Antivirus Web Browsers Provide Protection for Voting? -- 3.4 Are TLS, SSL, and HTTPS Are not Viable Options for Voting Platforms? -- 4 Experiment -- 4.1 Test Case Theories. ; 4.2 Test Case 1 - Configuration and Use of Antivirus Solutions for Protected Browser Mode -- 4.3 Test Case 2 - Using the Safe Exam Browser -- 5 Challenges Faced -- 6 Future Works and Recommendations -- 7 Conclusion -- References -- Research on the Mechanism of Cooperative Defense Against DDoS Attacks Based on Game Theory -- 1 Introduction -- 1.1 A Subsection Sample -- 2 Analysis of Research Status and Development Trends -- 2.1 Problems (Defect Analysis) -- 2.2 Analysis of Development Trend -- 3 Defense Against DDoS Attacks Based on Game Theory -- 3.1 Overall Design Idea -- 3.2 Collaborative Defense Scheme -- 3.3 SDN-Based Victim Side Defense Against DDoS Attacks -- 4 Conclusion -- References -- Detection of Malicious Node in VANETs Using Digital Twin -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Phase 1 -- 3.2 Phase 2 -- 4 Results and Discussion -- 5 Conclusion -- References -- Big Data Analytics and Applications -- The Storage and Sharing of Big Data in Air Traffic Management System -- 1 Introduction -- 2 The Architecture of Storage and Sharing for Aviation Big Data -- 2.1 New Aviation Network Architecture -- 2.2 Design of Policy Layer in New Aviation Network -- 2.3 Design of Policy Layer in New Aviation Network -- 3 Application Analysis of New Network Structure -- 4 Conclusion -- References -- Automated Recognition and Classification of Cat Pain Through Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Data Collection -- 3.1 Data Collection -- 4 Model Training -- 4.1 Basic Model Training -- 4.2 Enhanced Model Training -- 5 Conclusions and Future Work -- References -- Perceiving Airline Passenger Booking Lifecycle with the Utilization of Data Warehouse -- 1 Introduction -- 2 Research Methodology -- 3 Related Work -- 3.1 Data Warehouse Implementations -- 3.2 Airline Usecases -- 4 Challenges Whilst Implementing. ; 5 Benefits of Data Warehouses.
• Sommario/riassunto: This book constitutes the proceedings of the International Conference on Big Data Intelligence and Computing, DataCom 2022, which took place in Denarau Island, Fiji, in December 2022. The 30 full papers included in this volume were carefully reviewed and selected from 88 submissions. The papers detail big data analytics solutions, distributed computation paradigms, on-demand services, autonomic systems, and pervasive applications.
• Titolo autorizzato: Big data intelligence and computing
• ISBN: 9789819922338; 9789819922321
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996525670503316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 13864