Big Data Intelligence for Smart Applications (Studies in Computational Intelligence, 994)

✍ Scribed by Youssef Baddi (editor), Youssef Gahi (editor), Yassine Maleh (editor), Mamoun Alazab (editor), Loai Tawalbeh (editor)

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 343
• Edition: 1st ed. 2022
• Category: Library

✦ Synopsis

Today, the use of machine intelligence, expert systems, and analytical technologies combined with Big Data is the natural evolution of both disciplines. As a result, there is a pressing need for new and innovative algorithms to help us find effective and practical solutions for smart applications such as smart cities, IoT, healthcare, and cybersecurity.
This book presents the latest advances in big data intelligence for smart applications. It explores several problems and their solutions regarding computational intelligence and big data for smart applications. It also discusses new models, practical solutions,and technological advances related to developing and transforming cities through machine intelligence and big data models and techniques. This book is helpful for students and researchers as well as practitioners.

✦ Table of Contents

Preface
Contents
About the Editors
Data Quality in the Era of Big Data: A Global Review
1 Introduction
2 Big Data Characteristics
3 Big Data Quality
4 Big Data Value Chain
4.1 Data Generation
4.2 Data Acquisition
4.3 Data Pre-processing
4.4 Data Storage
4.5 Data Analysis
4.6 Data Visualization
4.7 Data Exposition
5 Research Methodology
6 Big Data Quality Approaches
7 Data Quality and Big Data Applications Domains
8 Conclusion
References
Adversarial Machine Learning, Research Trends and Applications
1 Introduction
2 Literature Review
2.1 The Tolerance for False Positives Versus False Negatives
3 Black Versus White-Box Attacks
4 Defenses Against Adversarial Attacks
4.1 Adversary Attacks and Language Comprehension
4.2 White Versus Black-Box Attacks and Defenses
5 Sequence Generative Models
6 Adversarial Training Techniques
7 Generation Models/Tasks/Applications
7.1 Next-Word Prediction
7.2 Dialog Generation
7.3 Neural Machine Translation
8 Text Generation Metrics
9 Memory-Based Models
10 Summary and Conclusion
References
Multi-agent Systems for Distributed Data Mining Techniques: An Overview
1 Introduction
2 Distributed Data Mining
2.1 DDM Approach
2.2 DDM Information Sharing
3 Multi-agent System
3.1 MAS Features
3.2 MAS Applications
3.3 MAS Platform
3.4 MAS with Distributed Data Mining
3.5 Key Characteristics of DDM with MAS
3.6 DDM with MAS Approaches
4 Conclusion
References
Time Series Data Analysis Using Deep Learning Methods for Smart Cities Monitoring
1 Introduction
2 Times Series Basic Concepts
2.1 Autoregressive Model (AR)
2.2 Moving Average Model
2.3 Autoregressive Moving Average Model
3 Machine Learning-Based Methods
3.1 Artificial Neural Networks
3.2 Big Data Analytics
3.3 Deep Learning Algorithms
4 Implementing an LSTM to Forecast the Traffic Noise
4.1 Introduction
4.2 Materials and Methods
4.3 Recurrent Neural Network (RNN)
4.4 Results and Discussion
5 Conclusions
References
A Low-Cost IMU-Based Wearable System for Precise Identification of Walk Activity Using Deep Convolutional Neural Network
1 Introduction
2 Related Works
3 System Details
3.1 Deployment Details
3.2 Data Acquisition
3.3 Data Standardization and Feature Extraction
3.4 Data Processing
4 Learning Techniques
4.1 kNN Classifier
4.2 SVM Classifier
4.3 GNB Classifier
4.4 DT Classifier
4.5 CNN Classifier
5 Results and Discussion
5.1 Experimental Setup
5.2 Evaluation Metrics
5.3 Results of Classification
5.4 CNN Learning Curve
5.5 Comparison with Related Works
6 Conclusion and Future Scope
References
Facial Recognition Application with Hyperparameter Optimisation
1 Introduction
2 Problem Statement
3 Theoretical Framework
3.1 Artificial Neural Network
3.2 K-Fold Cross-Validation
3.3 Back-Propagation
3.4 Grid Search
3.5 Random Search
4 Related Work
5 Methodology
6 Part 1
6.1 Designing a Solution
6.2 MLPClassifier
6.3 Experiments
6.4 Results
6.5 Analysis
7 Part 2
7.1 Grid Search
7.2 Random Search
7.3 Further Optimisation
8 Results
9 Analysis
10 Common Issues in Face Recognition
11 Conclusion
References
Internet-Assisted Data Intelligence for Pandemic Prediction: An Intelligent Framework
1 Introduction
1.1 The Role of Big Data in Smart Cities
2 Literature Review
3 Development of the Framework
3.1 Development of the SEIR Algorithm
3.2 Development of the A&P Algorithm
4 Experiment Results
4.1 Results of the Pandemic Prediction SEIR Algorithm
4.2 Results of the Air Quality Based Pandemic Prediction Algorithm
4.3 Results of Parking Data Based Pandemic Prediction Algorithm
5 Conclusion
References
NHS Big Data Intelligence on Blockchain Applications
1 Introduction
2 Background of Blockchain
2.1 About Blockchain
2.2 Blockchain for NHS
2.3 PBFT Algorithm in Application
3 Blockchain Security Applied Solution
3.1 Blockchain Algorithms
3.2 Anti-False Transaction
3.3 Anti-Alteration Integrity
4 Blockchain Security Problem-Solving
5 Evaluation
5.1 Discussion
5.2 Discussion of Blockchain Examples
5.3 Evaluation
6 Conclusions and Future Development
6.1 Summary
6.2 Suggestion for Future Development
References
Depression Detection from Social Media Using Twitter's Tweet
1 Introduction
2 Literature Review
3 Research Methodology
3.1 Data Collection and Preparation
3.2 Data Preprocessing
3.3 Word Analysis
3.4 Tokenization
3.5 Extract Features
3.6 Depression Detection
3.7 Output
4 Experimental Results and Discussions
4.1 Performance Metrics
4.2 Depression Prediction Performance
4.3 Comparison the Algorithms' Results
5 Limitations and Future Research Direction
6 Conclusions
References
A Conceptual Analysis of IoT in Healthcare
1 Introduction
2 Related Work
3 Wireless Body Area Network
4 Proposed Model
4.1 Data Sets and Feature Selection
4.2 Selection of Algorithm
4.3 Fusion of Deep Neural Networks and Fuzzy Logic
4.4 Implementation
4.5 Parallelization of Machine Learning Algorithms
5 Conceptual Analysis of Proposed Model
5.1 Algorithms
5.2 Results and Discussion
6 Discussion on Research Questions
7 Conclusion and Future Work
References
Securing Big Data-Based Smart Applications Using Blockchain Technology
1 Introduction
2 Research Methodology
3 An Overview of Blockchain Technology
4 Blockchain in the Service of Big Data
4.1 Healthcare Field
4.2 Banking Field
4.3 Smart Applications
4.4 Game Theory
4.5 Internet of Things
4.6 Big Data and Blockchain in the Service of VANETs
4.7 The Fifth Generation Based Applications (5G)
5 Discussion
6 Conclusion
References
Overview of Blockchain-Based Privacy Preserving Machine Learning for IoMT
1 Introduction
2 Related Works
3 Preliminaries
3.1 Machine Learning
3.2 Homomorphic Cryptosystem
3.3 Differential Privacy
3.4 Blockchain
4 System Overview & Model Construction
4.1 System Model
4.2 Threat Model
4.3 Data Sharing via Blockchain
4.4 Model Construction
5 Experimental Setup & Result Analysis
6 Conclusion
References
Big Data Based Smart Blockchain for Information Retrieval in Privacy-Preserving Healthcare System
1 Introduction
2 Related Work
3 Proposed Framework
3.1 Design of Multi-transaction Mode Consortium Blockchain
3.2 Proposed Blockchain Based Privacy Algorithm
3.3 Enhanced Storage Model of Improved Redis Cache
4 Experimental Evaluations of the Proposed RS-IMTMCB-PIR Scheme
5 Conclusion
References
Classification of Malicious and Benign Binaries Using Visualization Technique and Machine Learning Algorithms
1 Introduction
2 Related Works
3 Methodology
3.1 PE as Grayscale Image
3.2 Database
3.3 Local and Global Image Descriptors
4 Proposed Solution
5 Results and Discussion
6 Conclusion
References
FakeTouch: Machine Learning Based Framework for Detecting Fake News
1 Introduction
2 Methodology
2.1 Collect Data
2.2 Data Pre-processing
2.3 Features Extraction
2.4 Model Generation
3 Evaluation Metrics
3.1 Performance Parameters
4 Results and Discussion
4.1 Environments and Tools
4.2 Result Analysis
4.3 Comparative Discussion
5 Conclusion
References