The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023)

Name: The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023)
Brand: Springer
Price: 171.19 EUR
Availability: OnlineOnly

Muhammad Younas Irfan Awan Salima Benbernou Dana Petcu(Herausgeber*in)

Springer (Verlag)

Erschienen am 30. August 2023

X, 141 Seiten

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978-3-031-42317-8 (ISBN)

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Beschreibung

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Inhalt

Intro
Preface
Organization
Contents
Block Chain Systems
Distributed Ledger Technology for Collective Environmental Action
1 Introduction
2 Literature Background
3 Design Science Research Methodology
4 DLT Prototype Construction and Evaluation
4.1 Prototype Design Components
4.2 Prototype Functional Logic Components
4.3 Prototype Evaluation
5 Discussion of Empirical Findings
6 Conclusions
References
Moving Towards Blockchain-Based Methods for Revitalizing Healthcare Domain
1 Introduction
2 Blockchain Technology Fundamentals
2.1 Key Concepts
2.2 Blockchain Taxonomy
3 Blockchain Technology in Service of Healthcare
4 Related Works
4.1 Research Methodology
4.2 Our Research Foresight Regarding Healthcare Challenges
4.3 Blockchain Adoption in Healthcare Domain
5 Discussion
6 Our Forthcoming Proposition
7 Conclusion
References
Design of a Tokenized Blockchain Architecture for Tracking Trade in the Global Defense Market
1 Introduction
2 Related Work
3 Value of Blockchain for Trades in Defense Market
4 Design and Implementation of a NFT Based Decentralized Architecture
4.1 System Design
4.2 Implementation and Testing
5 Conclusion
References
Requirements for Interoperable Blockchain Systems: A Systematic Literature Review
1 Introduction
1.1 Research Problem
1.2 Key Contributions
2 Blockchain Interoperability Overview
2.1 Related Studies
3 Methodology
4 Results and Discussion
4.1 Technical and semantic interoperability requirements.
4.2 Organizational Interoperability Requirements
4.3 Legal Interoperability Requirements
5 Conclusion
References
Deep Learning and Healthcare Applications
PENN: Phase Estimation Neural Network on Gene Expression Data
1 Introduction
2 Related Work
3 Method
3.1 Objective Function of PENN
4 Results
4.1 Dataset
4.2 Experiments
4.3 Implementation
5 Conclusion
References
MRIAD: A Pre-clinical Prevalence Study on Alzheimer's Disease Prediction Through Machine Learning Classifiers
1 Introduction
2 Related Work
3 Research Methodology
3.1 Development and Testing Approach
3.2 Data Source
3.3 Data Preprocessing
3.4 Feature Selection
4 Results and Discussion
5 Conclusions
References
Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment
1 Introduction
2 Related Work
3 Methodology
3.1 Dataset
3.2 Deep Learning Manifold Alignment Method
4 Experimental Results
5 Conclusion and Future Work
References
Machine Learning and Commercial Systems
YOLOv5 for Automatic License Plate Recognition in Smart Cities
1 Introduction
2 Applications of ALPR
2.1 Use Cases of ALPR
2.2 Object Detection with Deep Learning Techniques
3 Related Work
4 Experimentation and Results
4.1 Methodology
4.2 Results
5 Conclusion
References
An Investigation into Predicting Flight Fares in India Using Machine Learning Models
1 Introduction
2 Literature Review
2.1 Empirical Approach to Determine Changes of Airfares and Customer Behavior When Purchasing Flight Tickets
2.2 Statistical Approaches for Determining Changes in the Airfare
2.3 Supervised Machine Learning for Determining the Changes in the Airfares
3 Research Methodology
4 Design Specifications
5 Evaluation Results and Discussion
5.1 Ensemble Model Analysis
5.2 Basic Machine Learning Model Results
6 Conclusion and Future Work
References
Securing Internet of Things (IoT) Devices Through Distributed Ledger Technologies (DLTs) and World Wide Web Consortium (W3C) Standards
1 Introduction
2 Overview of IoT and DLTs
2.1 Overview of IoT
2.2 Overview of DLTs
3 DLT-Based Applications and Services for IoT
4 Proposed Architecture
5 Conclusion and Future Work
References
Analysis and Forecast of Energy Demand in Senegal with a SARIMA Model and an LSTM Neural Network
1 Introduction
2 Analysis of Woyofal Customers Database
3 Building a Forecasting Model of Electricity Demand
3.1 Forecasting Electricity Demand with a SARIMA Model
3.2 Forecasting Electricity Demand with an LSTM Neural Network
4 Deploying the Forecasting Model in a Web Application
5 Conclusion
References
Author Index

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