Transforming Management with AI, Big Data, and IoT

✍ Scribed by Fadi Al-Turjman; Satya Prakash Yadav; Manoj Kumar; Vibhash Yadav; Thompson Stephan

• Publisher: Springer Nature
• Tongue: English
• Leaves: 315
• Category: Library

✦ Synopsis

This book discusses the effect that artificial intelligence (AI) and Internet of Things (IoT) have on industry. The authors start by showing how the application of these technologies has already stretched across domains such as law, political science, policy, and economics and how it will soon permeate areas of autonomous transportation, education, and space exploration, only to name a few. The authors then discuss applications in a variety of industries. Throughout the volume, the authors provide detailed, well-illustrated treatments of each topic with abundant examples and exercises. This book provides relevant theoretical frameworks and the latest empirical research findings in various applications. The book is written for professionals who want to improve their understanding of the strategic role of trust at different levels of the information and knowledge society, that is, trust at the level of the global economy, of networks and organizations, of teams and work groups, of information systems and, finally, trust at the level of individuals as actors in the networked environments. Presents research in various industries and how artificial intelligence and Internet of Things is changing the landscape of business and management; Includes new and innovative features in artificial intelligence and IoT that can help in raising economic efficiency at both micro and macro levels; Examines case studies with tried and tested approaches to resolution of typical problems in each application of study.

✦ Table of Contents

Preface
Contents
Artificial Intelligence for Smart Data Storage in Cloud-Based IoT
1 Introduction
2 Cloud-Based Data Storage
2.1 Public Cloud Storage
2.2 Private Cloud Storage
2.3 Hybrid Cloud
2.4 Community Cloud
3 Role of IoT in Cloud Storage
4 Role of AI in Smart Data Storage
5 Applications of AI, IoT, and Cloud in Various Sectors
5.1 Health-Care Sector
5.2 Agriculture Sector
5.3 Transportation Sector
5.4 Telecommunication Sector
5.5 Smart City and Home
6 Conclusions
References
Big Data Analytics and Big Data Processing for IOT-Based Sensing Devices
1 Introduction
1.1 Data Volume
1.2 Data Variety
1.3 Data Velocity
1.4 Data Veracity
2 Outline of IoT Along with Big Data
2.1 Internet of Things (IoT)
2.2 Big Data
3 Big Data Analytics
4 Variety of Data Types
4.1 Network Data (Online)
4.2 Mobile and IoT Data
4.3 Geography Data
4.4 Spatial–Temporal Data
4.5 Streaming as well as Real-Time Data
4.6 Visual Data
4.7 Data’s Associated Challenges
5 Role of Big Data in IoT
5.1 Recent Advances in IoT-Based Big Data and Analytics
6 Trending Big Data Tools
6.1 Apache Spark
6.2 Apache Kafka
6.3 Flink
6.4 Hadoop
7 Algorithm of Machine Learning Used in Big Data Analytics
7.1 K-Nearest Neighbors (KNN)
7.2 Naive Bayes
7.3 Support Vector Machine
7.4 Linear Regression
7.5 K-means Algorithm
7.6 Principal Component Analysis (PCA)
7.7 Neural Network
7.8 IoT’s Data Analytic Algorithms
8 Types of Technologies of Big Data
8.1 Operational Big Data Technology
8.2 Analytical Big Data Technology
9 Trending Technologies of Big Data
9.1 Data Storage
9.1.1 MongoDB
9.1.2 RainStor
9.2 Data Mining
9.2.1 Presto
9.2.2 Rapid Miner
9.2.3 Elasticsearch
9.3 Data Analytics
9.3.1 Kafka
9.3.2 Spark
9.4 Data Visualization
9.4.1 Tableau
9.4.2 Plotly
10 Real-World Applications of Big Data
10.1 eBay (Ecommerce)
10.2 JP Morgan Chase (Banking and Finance)
11 Privacy and Data Security
12 Applications of Big Data
12.1 Behavior Prediction
12.2 Health-Care Analysis and Data Storage
12.3 Content Recommendation
12.4 Smart City
13 Future Work
14 Conclusion
References
Untangling E-Voting Platform for Secure and Enhanced Voting Using Blockchain Technology
1 Introduction
1.1 Blockchain Technology
1.2 Blockchain Working Principle
1.3 Blockchain Key Elements
1.4 Types of Blockchain
1.4.1 Private Blockchains
1.4.2 Public Blockchains
1.4.3 Hybrid Blockchain
1.4.4 Consortium Blockchain
1.5 How Secure Is Blockchain?
2 Literature Survey
3 Research Motivation
4 Possible Implications
5 Future Scope
6 Conclusion
References
Role of Artificial Intelligence in Agriculture: A Comparative Study
1 Introduction
1.1 Literature Review
1.2 How Can AI Bring Revolution in Farming?
2 Applications of Artificial Intelligence in Agriculture
2.1 Crop Monitoring
2.2 Pest Detection
2.3 Weed Management
2.4 Weather Forecasting
2.5 Soil Management
2.6 Field Monitoring
3 Comparative Study
3.1 Comparison Between Different Crop Management Techniques
3.2 Comparison Between Different Soil Management Techniques
3.3 Comparison Between Different Disease Management Techniques
3.4 Comparison Between Different Weed Management Techniques
4 Conclusion
References
Big Data: Related Technologies and Applications
1 Introduction
2 Literature Review
2.1 Components of Hadoop
2.1.1 Management Layer
2.1.2 Data Storage Layer
2.1.3 Data Querying Layer
2.1.4 Data Access Layer
2.1.5 Data Streaming
2.1.6 Data Processing Layer
2.1.7 Hadoop
2.1.8 Mahout
3 Applications of Big Data
4 Conclusion and Future Scope
References
Digital Marketing: Transforming the Management Practices
1 Introduction
1.1 Radial Benefit of Online Marketing
1.2 Objectives
2 Literature Review
3 Digital Marketing Generation
3.1 Categories/Tools of Digital Marketing
4 Lead Generation
5 Challenges in Digital Marketing
6 Research Methodology
7 Recommendations
8 Conclusion
Annexure 1: Questionnaire and Responses
References
Real-Time Parking Space Detection and Management with Artificial Intelligence and Deep Learning System
1 Introduction
1.1 Parking Occupancy Detection
1.2 Car Parking Availability Prediction Service Using LSTM
1.3 Additional Anomaly Detection Using Deep Learning
2 Related Work
3 Proposed Method
3.1 Parking Occupancy Detection
3.2 LSTM Model for Parking Space Prediction
3.2.1 Dataset
3.2.2 Model Structure
3.3 Deep Learning Model for Anomaly Detection
3.3.1 Dataset
3.3.2 Model
3.4 User End Application
4 Results and Conclusion
References
Credit Card Fraud Detection Techniques Under IoT Environment: A Survey
1 Introduction
2 Datasets
2.1 US-Based Dataset
2.2 German Bank Dataset
2.3 NUAA Database
3 Classification Algorithms
3.1 Support Vector Machine
3.2 Artificial Neural Networks
3.3 Artificial Immune System (AIS)
3.4 Fuzzy Logic
4 Class Imbalance
4.1 Boosting-Based Algorithms
4.2 Bagging-Based Algorithms
4.3 Changing Class Distributions
4.4 Cost-Sensitive Learning (CSL)
4.5 One-Class Learning (OCC)
5 Face Recognition Techniques
5.1 Dynamic Approach of Extracting Features
5.2 Colour Base Extraction
5.3 Cam Shift Algorithm
5.4 Convolutional Neural Networks
5.5 Face Recognition Library
6 Conclusion
References
Trustworthy Machine Learning for Cloud-Based Internet of Things (IoT)
1 Introduction
2 Related Work
3 Machine Learning and Internet of Things (IoT)
3.1 Machine Learning
3.2 Internet of Things (IoT)
4 Trustworthy Machine Learning for Cloud-Based IoT
5 Conclusion
References
A Novel αβEvolving Agent Architecture for Designing and Development of Agent-Based Software
1 Introduction
2 Literature Review
3 αβEvolving Agent (αβEA) Architecture
3.1 Working of αβEA Agent
4 Experiment
5 Result
6 Conclusion
References
Software-Defined Network (SDN) for Cloud-Based Internet of Things
1 Introduction
2 SDN (Software-Defined Network)
2.1 History of SDN Principles
2.2 Need of SDN
2.3 Architecture of SDN
2.4 Implementation of Issues Related to Security Using Software-Defined Network
2.5 NFV (Network Function Virtualization)
2.6 SDN’s Relationship with NFV
2.7 Benefits of SDN-NFV Architecture on Cost and Energy Parameter
3 Cloud-Based Internet of Things (Cloud IoT)
4 Integration of SDN with Cloud IoT
4.1 Software-Defined IoT Principles
4.2 SDN-IoT Units Conceptual Model
4.3 Classification of Units
4.4 Software-Defined IoT Unit Automatic Composition
5 Benefits of SDN for Cloud IoT
6 Limitations of SDN-Enabled Cloud IoT
7 Conclusion and Future Scope
References
Malware Discernment Using Machine Learning
1 Introduction
2 Malware Definition
3 Latest Trends and Attacks
3.1 Ransomware Exploring New Techniques for Process Code Injection
3.2 Info-Stealer Hidden in the Phishing Emails!
4 Types of Malware
4.1 Virus
4.2 Worms
4.3 Spyware
4.4 Trojans
4.5 Ransomware
5 Malware Detection Techniques
5.1 Signature Based
5.2 Susceptible to Evasion
5.3 Zero-Day Attacks
5.4 Heuristic Based
5.5 Machine Learning Based
6 Malware Analysis Techniques
6.1 Static Analysis
6.2 Dynamic Analysis
6.2.1 Cuckoo Sandbox
6.2.2 Limon Sandbox
6.2.3 Hybrid Analysis
7 Machine Learning
7.1 Supervised Learning
7.1.1 The Multiple Methods of Supervised Learning
Regression
Classification
Binary Classification
Naive Bayesian Model
Decision Trees
Random Forest Model
Neural Networks
Support Vector Machines
7.1.2 Pros and Cons of Supervised Learning
7.2 Semi-Supervised Learning
7.3 Unsupervised Learning
8 Machine Learning in Malware Detection
8.1 Dataset Collection
8.2 Features Extraction
8.3 Features Selection
8.4 Training of Classifier
9 Conclusion
References
Automating Index Estimation for Efficient Options Trading Using Artificial Intelligence
1 Introduction
2 Constraint Programming and the CLP Scheme
2.1 The Binomial Option Pricing Formula
2.2 The Black-Scholes Model
3 Complexity in Options Market
3.1 The Architecture and Environment of Agent-Based Model
3.2 Agent Strategies (Zero Intelligence Plus Model)
3.2.1 Model Parameters
3.2.2 Optimizing Strategies Using Monte Carlo Simulation
3.2.3 Process of Strategy Formation
3.2.4 Enhancement of Model Based on Delta-Gamma Parameters
4 Estimating the Price Movement with Normal Distribution Curve of Underlying Asset
References
Artificial Intelligence, Big Data Analytics and Big Data Processing for IoT-Based Sensing Data
1 Introduction
1.1 Big Data
1.2 Internet of Things (IoT)
1.3 Artificial Intelligence
2 Applications of Artificial Intelligence
3 Applications of IoT
4 Technologies and Methods Supporting Big Data and IoT and Artificial Intelligence
4.1 Deep Learning (DL)
4.2 Cloud Computing
4.3 Machine Learning
4.4 Computational Techniques
5 Big Data Analytics
6 IoT-Based Sensing Data
7 Big Data Processing
8 Architectural Design of Big Data Analytics and IoT-Based Sensing Data and Processing
8.1 Data Collection Sources
8.2 Big Data Analytics and Processing
8.3 Big Data Analytics Platforms and Tools
8.3.1 Big Data Analytics Techniques
8.3.2 Big Data Processing Framework
8.4 IoT Sensing Data
9 Limitations and Challenges in IoT-Based Sensing Data
10 Conclusion
References
Technological Developments in Internet of Things Using Deep Learning
1 Introduction
2 Internet of Things (IoT)
3 Deep Learning
4 Deep Learning in Internet of Things (IoTs) with Different Applications
4.1 Cyber Security
4.2 Agriculture
4.3 Healthcare
4.4 Block Chain
5 Conclusion
References
Machine Learning Models for Sentiment Analysis of Tweets: Comparisons and Evaluations
1 Introduction
1.1 Motivation and Our Contributions
1.2 Chapter Structure
2 Impact of Social Media
3 Related Work
3.1 Critical Analysis
4 Design and Methodology
4.1 Dataset
4.2 Feature Extraction Techniques
4.2.1 Bag of Words
4.2.2 TF-IDF
4.3 Machine Learning Models
4.3.1 Logistic Regression
4.3.2 Decision Tree
4.4 Performance Evaluation Metric
5 Performance Evaluation
5.1 Configuration Settings
5.2 Dataset Overview
5.3 Data Pre-Processing
5.4 Data Visualisation
5.5 Feature Engineering
5.5.1 Bag of Words
5.5.2 TF-IDF
6 Experimental Results and Performance Comparison
7 Conclusions
7.1 Future Directions
References
Secure and Enhanced Crowdfunding Solution Using Blockchain Technology
1 Introduction
2 Blockchain
3 Literature Survey
4 Conventional Vs Blockchain Crowdfunding
4.1 Conventional Crowdfunding
4.2 Blockchain Crowdfunding
5 Result and Analysis
5.1 Project Architecture
5.2 Modules
5.2.1 Minimum Contribution
5.2.2 Single Campaign
5.2.3 Request Module
5.3 Technology
5.3.1 Frontend
5.3.2 Metamask
5.3.3 Ethereum
Why Is Ethereum Used?
5.4 Result
5.5 Future Scope
6 Conclusion
References
Index