Search

Browse Subject Areas

For Authors

Submit a Proposal

Hyper-Intelligent Networks

Exploring the Future of Connectivity for Society 5.0

Edited by Pradosh Kumar Gantayat, Sandeep Kumar Panda, S. Balamurugan and Sheng-Lung Peng
Series: Industry 5.0 Transformation Applications
Copyright: 2026   |   Expected Pub Date:2025/10/30
ISBN: 9781394315604  |  Hardcover  |  
518 pages

One Line Description
Prepare for the next technological frontier with this essential, multidisciplinary
guide that delves into hyper-intelligent networks, providing a comprehensive
overview of how AI and machine learning are revolutionizing telecommunications,
healthcare, and other vital sectors with cognitive, autonomous connectivity.

Audience
Research scholars, IT professionals, network administrators, cybersecurity experts, government research agencies, and engineering students.

Description
As we stand on the brink of a new technological frontier, the convergence of artificial intelligence, machine learning, and network infrastructure promises to revolutionize the way we harness the power of digital ecosystems. The vision of a hyper-intelligent network transcends conventional notions of connectivity and represents a paradigm shift where networks evolve from mere conduits of data to dynamic entities with cognitive capabilities, adaptability, and autonomous decision-making abilities. This book delves into the emerging field of hyper-intelligent networks, exploring how these networks are poised to revolutionize various sectors, including telecommunications, healthcare, finance, and transportation. It provides a comprehensive overview of the theoretical foundations, practical applications, and future implications of hyper-intelligent networks, offering a deeper understanding of this cutting-edge technology. Written by leading experts in the fields of artificial intelligence and networking, the book has a multidisciplinary approach that combines theoretical insights with real-world case studies and practical examples. It is suitable for both technical professionals seeking to deepen their understanding of hyper-intelligent networks and non-technical readers interested in the potential impact of these technologies on society.
Readers will find the book:
• Discusses different applications of hyper-intelligent networks in various industries;
• Introduces the innovative potential of hyper-intelligent networks;
• Presents state-of-the-art analyses and real-world case studies demonstrating hyper-intelligent networks;
• Provides a comprehensive look at applications of hyper-intelligent networks across a number of industries.

Back to Top
Author / Editor Details
Pradosh Kumar Gantayat, PhD is an Assistant Professor at the Department of Artificial Intelligence and Data Science, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education (IFHE), Deemed to be University, Hyderabad, Telangana, India. He has published more than 25 papers in international journals, conferences, and book chapters and holds three Indian patents. His research focuses on security issues in computer networks, soft computing, AI, and optimization algorithms.

Sandeep Kumar Panda, PhD is a Professor Department of Artificial Intelligence and Data Science, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education (IFHE), Deemed to be University, Hyderabad, Telangana, India. He has published more than 70 articles in and international journals and conferences and edited six books. His research interests include blockchain technology, W3, Metaverse, the Internet of Things, AI, and cloud computing.

S. Balamurugan, PhD is the Director of Research and Development at Intelligent Research Consultancy Services. He has published 48 books, more than 300 articles in international journals and conferences, and 55 patents. His research interests include artificial intelligence, augmented reality, Internet of Things, big data, and cloud and wearable Computing.

Sheng-Lung Peng, PhD is a Professor and the Director of the Department of Creative Technologies and Product Design at the National Taipei University of Business, Taiwan. He has edited several special issues of journals and published more than 100 research papers. His research interests focus on designing and analyzing algorithms for bioinformatics, combinatorics, data mining, and other network areas.

Back to Top

Table of Contents
Preface
Acknowledgement
Part 1: Artificial Intelligence and Blockchain in Hyper Intelligent Network
1. Introduction to Hyper-Intelligent Networks
Mohammed Kaleem, P. Pavan Kumar, Pradosh Kumar Gantayat and Sandeep Kumar Panda
1.1 A Comprehensive Overview
1.2 Definition of Hyper-Intelligent Networks
1.3 Key Components of Hyper-Intelligent Networks
1.3.1 Artificial Intelligence (AI)
1.3.2 Machine Learning (ML)
1.3.3 Internet of Things (IoT)
1.3.4 Big Data Analytics (BDA)
1.3.5 Edge Computing
1.4 Literature Review
1.5 Applications of Hyper-Intelligent Networks
1.5.1 Smart Cities
1.5.2 Healthcare
1.5.3 Industrial Automation
1.5.4 Autonomous Vehicles
1.5.5 Financial Services
1.6 Challenges of Hyper-Intelligent Networks
1.6.1 Data Security and Privacy
1.6.2 Interoperability
1.6.3 Ethical Considerations
1.6.4 Reliability and Resilience
1.6.5 Scalability
1.7 Future Trends in Hyper-Intelligent Networks
1.7.1 Quantum Computing
1.7.2 5G Technology
1.7.3 Augmented Reality (AR) and Virtual Reality (VR)
1.7.4 Blockchain
1.8 Conclusions
References
2. Hyper-Intelligence in Machine Learning: Unleashing the Next Evolution of AI
Tejinder Kaur, Kiran Rani Panwar, Ankita, Mukesh Soni, Neelam Oberoi and Pokkuluri Kiran Sree
2.1 Introduction to Hyper-Intelligence
2.1.1 Defining Hyper-Intelligence
2.1.2 Historical Context
2.2 Theoretical Foundations
2.2.1 Autonomous Learning
2.2.2 Complex Problem Solving
2.2.3 Contextual Understanding
2.3 Core Technologies
2.3.1 Advanced Neural Networks
2.3.2 Reinforcement Learning (RL)
2.3.3 Meta-Learning
2.3.4 Quantum Computing
2.4 Practical Applications
2.4.1 Healthcare
2.4.2 Autonomous Systems
2.4.3 Financial Services
2.4.4 Natural Language Processing (NLP)
2.5 Ethical and Societal Implications
2.5.1 Bias and Fairness
2.5.2 Transparency
2.5.3 Accountability
2.5.4 Privacy Concerns
2.6 Future Prospects
2.6.1 Human–AI Collaboration
2.6.2 Continuous Learning
2.6.3 Societal Transformation
2.7 Case Studies
2.8 Prospective Research Avenues
2.9 Core Technologies
2.9.1 Advanced Neural Networks
2.9.2 Reinforcement Learning
2.9.3 Pioneering Efforts
2.9.4 Neuroscience-Inspired AI
2.10 Collaborative AI
2.10.1 Breakthrough Innovations
2.10.2 Self-Supervised Learning
2.11 Challenges and Future Directions
2.11.1 Future Directions of Artificial Intelligence
2.12 Future Directions
References
3. Edge Computing and Its Role in Hyper-Intelligent Networks
Shivani Kumari, Anukrity Gupta, Aashish Sahu, Simran Dalmia, Mamoon Rashid and Hitesh Mohapatra
3.1 Introduction
3.2 Literature Review
3.3 Proposed Study
3.3.1 Critical Analysis of Wireless Communication Protocols
3.3.2 Choosing the Right Protocol for Your Smart Home Needs
3.3.3 Smart Home Communication Requirements
3.3.4 Procedure for Connection of Wireless Communication Protocol
3.4 Future Trends
3.5 Conclusion
References
4. Emerging Horizons: Exploring Blockchain-Driven Hyper-Intelligent Network in Healthcare
Subasish Mohapatra, Subhadarshini Mohanty and Ayaskanta Mishra
4.1 Introduction
4.2 Understanding Blockchain
4.2.1 Role of Consensus in Blockchain
4.2.2 Criteria of Selecting a Consensus Model
4.3 Understanding Federated Learning
4.4 Related Works
4.5 Simulating Blockchain
4.5.1 Simulating Using Simulators
4.5.2 Architecture Design
4.5.3 Creating A Blockchain
4.6 Conclusion
References
5. Med3: Blockchain-Based Privacy Preservation of Patient Health Records in Hyper-Intelligent Network
Rohit Saxena, Vishal Nagar, Satyasundara Mahapatra and Atul Gupta
5.1 Introduction
5.2 Motivation
5.3 Related Work
5.4 Problem Formulation
5.5 Architecture of the Proposed Healthcare System
5.6 Results and Discussion
5.7 Conclusions and Future Work
References
6. Deep Learning for Hyper-Intelligent Networks
P. Sree Lakshmi and P. Rohini
6.1 Introduction
6.1.1 Key Characteristics
6.1.2 Essential Role in Society 5.0
6.2 Importance of Deep Learning in Hyper-Intelligent Networks
6.2.1 Key Contributions of Deep Learning
6.3 Related Work
6.4 Fundamentals of Deep Learning
6.4.1 Neural Networks and Deep Learning
6.4.2 Types of Deep Learning Models
6.5 Applications of Deep Learning in Hyper-Intelligent Networks
6.5.1 Network Traffic Prediction
6.5.2 Network Security and Intrusion Detection
6.5.3 Quality-of-Service Optimization
6.5.4 Autonomous Network Management
6.6 Key Technologies and Techniques
6.7 Challenges and Limitations of Hyper-Intelligent Networks
6.8 Research Directions and Prospects for the Future
6.9 Case Study: Application of Hyper-Intelligent Networks in Smart Cities
6.10 Conclusion
References
7. Machines Learning in Networking
Tejinder Kaur, Ankita, Anima Bag, Debabrata Dansana, Anitha D.B. and Kajal Jain
7.1 Introduction
7.1.1 Scalability
7.1.2 Security
7.1.3 Core Concepts
7.1.4 Challenges and Considerations
7.2 Networking Challenges and Machine Learning Solution
7.3 Bandwidth Management
7.3.1 Importance of Bandwidth Management
7.3.2 Challenges in Bandwidth Management
7.3.3 Machine Learning for Traffic Prediction
7.4 Challenges and Future Directions
Conclusion
References
Part 2: Privacy and Security of Healthcare in Hyper Intelligent Network
8. Security and Privacy in Hyper-Intelligent Networks
Abhishek Panda, Animesh Jha, Gourav Vardhan Panigrahi, Saahen Sriyan Mishra and Hitesh Mohapatra
8.1 Introduction
8.1.1 Issues with Current Scenario
8.1.2 Ethical and Regulatory Challenges Required in Smart City Development
8.2 Literature Review
8.3 Proposed Study
8.3.1 Limitations on IaaS in the Context of Cloud Security
8.3.2 Limitations on PaaS in the Context of Cloud Security
8.3.3 Limitations on SaaS in the Context of Cloud Security
8.3.4 Other Key Security Concerns for Cloud Computing
8.3.5 Access Control Challenges
8.3.6 Comparative Analysis of Existing Models
8.3.7 Findings from Comparative Analysis
8.4 Conclusion
References
9. Internet-of-Things Integrated Blockchain–Based Supply Chain Management Across Various Industries
Dileep Kumar Murala, Sandeep Kumar Panda, Santosh Kumar Swain and Pradosh Kumar Gantayat
9.1 Introduction
9.2 Literature Review
9.3 Blockchain Technology
9.3.1 Blockchain Features
9.3.2 Processes in the Supply Chain
9.4 IoT in the Supply Chain
9.4.1 The Implications of the IoT on the Supply Chain
9.5 Implementing a Blockchain Supply Chain
9.5.1 Supply Chain and Logistics Blockchain and IoT Applications
9.5.2 Blockchain and IoT Benefits for Supply Chain and Logistics
9.6 Supply Chain IoT and Blockchain Use Cases
9.7 Challenges in Traditional Supply Chain
9.8 Conclusion and Feature Directions
References
10. Hyper-Intelligent Networks in Healthcare
Bibekananda Mohanty
10.1 Introduction
10.2 Overview of Traditional Healthcare Systems
10.3 Challenges Faced by Healthcare Systems
10.4 Personalized Medicine and Treatment Recommendations through Hyper-Intelligent Networks in Healthcare
10.5 Predictive Analytics for Disease Prevention and Early Detection through Hyper-Intelligent Networks in Healthcare
10.6 Remote Patient Monitoring and Telemedicine through Hyper-Intelligent Networks in Healthcare
10.7 Hyper-Intelligent Networks in Shaping the Future of Healthcare
References
11. AI-Driven Remote Health Monitoring for Predicting Diabetes and Heart Diseases Using ULMCSO and PGND Models
Soumya Ranjan Mishra and Sachikanta Dash
11.1 Introduction
11.2 Related Work
11.3 Proposed Methodology
11.3.1 Normalization of Datasets with Pre-Processing
11.3.2 Feature Selection Using ULMCSO Model
11.3.3 Classifying PGND
11.4 Outcome and Conversation
11.5 Conclusion
References
12. Cloud Manufacturing and Intelligent Network Importance in Healthcare Applications
Ravi Prasad Thati and Pranathi Kakaraparthi
12.1 Introduction
12.2 Key Components
12.3 Benefits of Cloud Manufacturing
12.4 Importance of Healthcare Applications
12.4.1 Integrating Cloud Manufacturing in the Healthcare Sector
12.4.2 Efficient Resource Utilization Optimized Resource Allocation
12.4.3 Improved Data Management and Security
12.4.4 Innovation and Research Accelerate Research
12.4.5 Cooperation Platform
12.4.6 Regulatory Compliance and Reporting Simplify Compliance
12.4.7 Data Review and Traceability
12.5 Fundamentals of Cloud Manufacturing Technical Foundation
12.5.1 Internet of Things (IoT)
12.5.2 Big Data
12.5.3 Artificial Intelligence (AI) and Machine Learning (ML)
12.5.4 Traditional Healthcare Manufacturing
12.5.5 Pharmaceutical Manufacturing
12.5.6 High Cost and Low Efficiency
12.5.7 The Supply Chain is Complex Supply Chain Characteristics
12.5.8 Limited Flexibility and Scalability the Production System is Not Flexible
12.6 Quality Control and Assurance Task
12.6.1 Integration of Cloud Manufacturing in Healthcare
12.6.2 Cloud Manufacturing Platforms
12.6.3 Telemedicine and Remote Healthcare Services
12.6.4 Working
12.7 Economical Aspect
12.7.1 Improved Patient Involvement
12.7.2 Customized Prosthetics and Orthotics
12.7.3 Working
12.7.4 Outcomes
12.7.5 Drug Production and Quality Control Context
12.7.6 Smart Factory
12.8 Conclusion
References
13. Analysis on E-Services Platform in Context of Intelligent Cities
Aryan Kaushal, Animesh Singh, Ankit Raj, Sanskar Garg and Hitesh Mohapatra
13.1 Introduction
13.2 Literature Review
13.3 Research Gap
13.4 Comparative Analysis of Existing Work
13.4.1 Findings from Comparative Analysis
13.4.2 Analysis
13.4.3 Future Scope
13.5 Conclusion
References
Part 3: Future Trends and Applications in Hyper Intelligent Network
14. Foundations and Advancements in Hyper-Intelligent Networks
D. Krishna Madhuri and Madhusmita Majhi
14.1 History of Hyper-Intelligent Networks
14.2 Introduction to Hyper-Intelligent Networks
14.3 The Limitations of Manual Fault Management
14.3.1 Scalability Challenges
14.3.2 Limited Proactive Capabilities
14.3.3 Human Error
14.4 The Advantages of Intelligent Fault Management
14.4.1 Enhanced Automation
14.4.2 Scalability and Adaptability
14.4.3 Predictive Maintenance
14.4.4 Intelligent Alerting
14.5 Concept of Intelligent Network
14.5.1 Key Components of IN
14.5.2 Benefits of Intelligent Networks (INs)
14.5.3 Applications of Intelligent Networks (INs)
14.5.4 Limitations of Intelligent Networks (INs)
14.6 Devices of Intelligent Networks
14.7 Understanding the Difference between IN and HIN
14.7.1 Intelligence Level
14.8 The Function of Artificial Intelligence in HIN
14.8.1 Network Management and Optimization
14.8.2 Security Enhancement
14.8.3 Fault Management and Self-Healing
14.9 Types of Intelligent Networks
14.9.1 Artificial Intelligence (AI) Networks
14.9.2 Machine Learning Networks
14.9.3 Cognitive Networks
14.9.4 Self-Organizing Networks (SON)
14.9.5 Software-Defined Networks (SDN)
14.9.6 Intent-Based Networks (IBN)
14.9.7 Edge Computing Networks
14.9.8 5G Networks (and Beyond)
14.9.9 IoT Networks
14.10 Operations of Hyper-Intelligent Networks
14.11 Summary
References
15. Reinforcement Learning in Network Optimization
Jagini Naga Padmaja and Nadimpalli Madana Kailash Varma
15.1 Introduction to Reinforcement Learning in Networking
15.1.1 Definition and Importance
15.1.2 What is Machine Learning?
15.1.3 Machine Learning’s Significance in Networking
15.1.4 Historical Background
15.1.4.1 Evolution of Networking Technologies
15.1.4.2 Traditional Networking
15.1.4.3 Development of the Internet
15.1.4.4 Modern Networking
15.1.4.5 Advancements in Network Security
15.1.4.6 5G and Beyond
15.1.5 Growth of Machine Learning Applications in Networking
15.2 Machine Learning Techniques for Network Optimization
15.2.1 Traffic Prediction and Analysis
15.2.2 Resource Allocation and Management
15.2.3 Quality-of-Service (QoS) Enhancement
15.3 Security Enhancements through Machine Learning
15.3.1 Financial Transactions
15.3.2 Identity Theft
15.3.3 Response Systems that are Automated
15.3.4 Intrusion Detection Systems
15.3.4.1 Signature-Based vs. Anomaly-Based Detection
15.3.4.2 Anomaly Detection
15.3.5 Machine Learning Models for Intrusion Detection
15.3.5.1 Decision Trees
15.3.5.2 Support Vector Machine
15.3.6 Secure Authentication Mechanisms
15.3.6.1 Behavioral Biometrics
15.3.6.2 Multi-Factor Authentication (MFA)
15.3.6.3 Integrating Machine Learning with Conventional Authentication Techniques
15.4 Machine Learning in Wireless Networks
15.4.1 Cognitive Radio Networks
15.4.1.1 Techniques for Spectral Sensing
15.4.1.2 ML Models: CNN and RNN
15.4.2 Dynamic Spectrum Management
15.4.3 5G and Beyond
Conclusion
Bibliography
16. Amharic Multi-Class Sentiment Analysis Model: Leveraging Deep Learning for Emotion Detection in Text
Rojer Berhanu, Minakhi Rout, Suresh Chandra Satapathy and Junali Jasmine Jena
16.1 Introduction
16.2 Related Works
16.3 Proposed Approach
16.4 Data Preprocessing and Implementation
16.4.1 Dataset Description
16.4.2 Data Preprocessing
16.4.3 Model Implementation
16.5 Result and Discussion
16.6 Conclusion and Future Work
References
17. Retail Sales Prediction Using Intelligent Time-Series Analysis
Vuttaradi Akarsh
17.1 Introduction
17.2 Literature Review
17.3 Predicting Future Sales
17.4 Proposed Methodology
17.4.1 Data Visualization
17.4.2 Exploratory Data Analysis (EDA)
17.5 Understanding Categorical Data
17.5.1 One-Hot Encoding
17.5.2 Label Encoding
17.5.3 Ordinal Encoding
17.5.4 Modeling
17.5.5 Autoregressive Model
17.5.6 SARIMA
17.5.7 Applying SARIMA
17.6 ARIMA Method vs. Other Methods
17.7 Comparing the ARIMA Model with Other Models for Sales Time-Series Forecasting
17.8 Results
17.9 Future Work
Conclusion
Bibliography
18. Technologically Enhanced Anti-Theft Door Locking and Alert System Based on IoT
Sumanta Chatterjee, Jui Pattnayak and Pabitra Kumar Bhunia
18.1 Introduction
18.2 Literature Review
18.3 Idea of the Proposed System
18.4 Circuit Diagram
18.4.1 Components Required
18.5 Working Principle
18.6 Results
18.7 Conclusions and Future Scope
References
19. Leveraging Analytics for Efficient Network Operations
Amarendar Reddy Chittireddy, Vishnu Kurnala and Koti Tejasvi
19.1 Introduction
19.1.1 Overview of Network Management
19.1.1.1 Importance of Network Management
19.1.1.2 Evolution of Network Management Systems
19.1.2 Role of Data Analytics in Network Management
19.1.2.1 Definition and Scope
19.1.2.2 Historical Context and Evolution
19.2 Data Collection and Preparation
19.2.1 Sources of Network Data
19.2.1.1 Network Traffic Data
19.2.1.2 Device and Infrastructure Data
19.2.1.3 User and Application Data
19.2.2 Data Collection Methods
19.2.2.1 Passive Monitoring
19.2.2.2 Active Probing
19.2.3 Data Cleaning and Preprocessing
19.2.3.1 Handling Missing Data
19.2.3.2 Data Normalization
19.2.3.3 Data Anonymization and Privacy Concerns
19.3 Applications of Data Analytics in Network Management
19.3.1 Network Performance Monitoring
19.3.1.1 Real-Time Monitoring
19.3.1.2 Historical Performance Analysis
19.3.2 Fault Management
19.3.2.1 Fault Detection
19.3.2.2 Fault Isolation and Diagnosis
19.3.2.3 Fault Recovery
19.3.3 Security Management
19.3.3.1 Intrusion Detection Systems
19.3.3.2 Threat Analysis and Mitigation
19.3.3.3 Security Information and Event Management
19.3.4 Capacity Planning and Management
19.3.4.1 Demand Forecasting
19.3.4.2 Resource Allocation
19.3.4.3 Load Balancing
19.3.5 Quality-of-Service (QoS) Management
19.3.5.1 QoS Metrics
19.3.5.2 Service-Level Agreement (SLA) Management
19.3.5.3 Traffic Shaping and Prioritization
19.4 Big Data Analytics
19.4.1 Handling Large-Scale Network Data
19.4.2 Scalable Analytics Architectures
19.5 Challenges and Future Directions
19.5.1 Challenges in Data Analytics for Network Management
19.5.1.1 Data Quality and Integrity
19.5.1.2 Scalability Issues
19.5.1.3 Integration with Legacy Systems
19.5.2 Future Trends and Innovations
19.5.2.1 Emerging Technologies
19.5.2.2 The Vision for Society 5.0
19.6 Conclusion
References
20. Techniques to Mitigate the Threats in Cloud Security
Shifa Shah, Jaffar Amin Chacket and Amit Katoch
20.1 Introduction
20.2 Cloud Computing
20.3 Cloud Characteristics
20.4 Cloud Computing Deployment Models
20.4.1 Public Cloud
20.4.2 Private Cloud
20.4.3 Community Cloud
20.4.4 Hybrid Cloud
20.5 Cloud Computing Platforms
20.6 Related Work
20.6.1 Security Issues by Three Attack Vectors
20.6.2 Open Issues to Cloud
20.6.3 Current Cloud Attacks and Threats
20.7 Work Done by Various Researchers to Address the Open Issues
20.7.1 Network Based Attacks Countermeasures
20.7.1.1 Commercial Solution by Various Cloud Providers
20.7.2 Hardware Based Attacks Countermeasures
20.7.2.1 Commercial Countermeasures to Hardware Based Attacks
20.7.3 Hypervisor Attacks Countermeasures
20.7.3.1 To Secure Hypervisor Vector (Commercial Solutions)
20.7.4 Cryptographic Techniques for Data Security
20.7.4.1 Symmetric Key Algorithms
20.7.4.2 Asymmetric Key Algorithms
20.8 Cloud Computing Challenges
20.9 Future Scope and Conclusion
References
21. The Transformative Influence of Industry 4.0 and Hyper Artificial Intelligence on Human Resource Management
Rashmi Kumari and Sujata Priyambada Dash
21.1 Introduction
21.2 Methodology
21.2.1 Human Resource Management
21.2.2 Artificial Intelligence
21.2.3 Machine Learning
21.2.4 Deep Learning
21.2.5 Industry 4.0
21.3 Results and Discussion
21.4 AI and HRM
21.5 Industry 4.0 and HRM
21.5.1 Performance Management
21.5.2 Payroll System
21.5.3 Manpower Planning
21.5.4 Training and Development
21.5.5 Management by Objectives
21.6 Conclusion
21.7 Future Research Directions
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page