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Predictive Methods in Next-Generation Computing

Edited by R. Sathiyaraj, Rajesh Kumar Dhanaraj, K. Arun Kumar, Rutvij H. Jhaveri, and A. Mohamed Abbas
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394248797  |  Hardcover  |  
332 pages
Price: $225 USD
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One Line Description
Predictive Methods in Next-Generation Computing is essential for anyone looking to understand how next-generation computing technologies are driving predictive models to create smarter, safer, and more sustainable solutions across diverse fields.

Audience
Post-graduates, data engineers, researchers, academics, policymakers, and industry professionals in computer science, exploring smart and sustainable applications for intelligent systems

Description
Computing technologies are significantly evolving in all sectors, aiming to provide an automated, energy-efficient solution for various real-world problems with an advanced predictive model. This book explores computing technologies in various domains and provides novel strategies and designs for a smart, secure, and sustained environment for the future. Predictive Methods in Next-Generation Computing provides a realistic overview of various computer technologies that have made drastic advances in the field of smart applications, resulting in smart agriculture, healthcare, traffic management, and sustainability. The book comprehensively covers predictive models using artificial intelligence, machine learning, and Internet of Things and considers these applications for various smart applications to create a safe, smart environment for everyone. The chapters include real-world case studies, giving readers a glimpse into the benefits of integrating technology into predictive analytics to make the practice more automated, energy-efficient, safe, and profitable.

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Author / Editor Details
R. Sathiyaraj, PhD is an assistant professor in the Department of Computer Science and Engineering at GITAM University. He has authored two books, served as lead editor for two books, and published five patents and over 20 articles in refereed journals and international conferences. His research interests include machine learning, big data analytics, and intelligent systems.

T.V. Ramana, PhD is a professor in the Department of Computer Science and Engineering at JAIN University, where he heads the Cloud Technology and Information Security Group. He has published over 45 articles in international journals and conferences. His research interests include software engineering, computer system architecture, machine learning, and Internet of Things.

K. Arun Kumar, PhD is an assistant professor in the Department of Computer Science and Engineering at GITAM University. He has contributed over 15 articles to various reputed journals. His research focuses on wireless networks, data mining, deep learning, and big data.

Rajesh Kumar Dhanaraj, PhD is a professor at Symbiosis International University. He has contributed over 45 books on various technologies, 21 patents, and over 90 articles in various refereed journals and international conferences. His research interests include machine learning, cyber-physical systems, and wireless Sensor networks.

Mohamed Abbas, PhD works in the IT Department at the University of Technology and Applied Sciences, Sultanate of Oman. He has published 23 research articles in reputed international journals and presented 16 papers in international Conferences. Additionally, he has registered four patents, one of which was granted. His research is focused on artificial intelligence, big data, Internet of Things, and machine learning.

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Table of Contents
Preface
1. Introduction to Intelligent Computational Technologies
C. Geetha, Sajithra S., S. Srijayanthi, B. Reena, I. Subha and Sreelakshmi N.
1.1 Introduction
1.2 Literature Survey
1.3 Methodology
1.4 Simulation Metrics
1.4.1 Identification of E-Governance Adoption and Assessment Factors
1.4.2 Sample Data Collection Using Questionnaire
1.4.3 Respondent Details
1.4.4 Fuzzy Conjoint Model
1.4.4.1 Calculation of Weight for Each Respondent
1.4.4.2 Calculating the Similarity Degree
1.5 Computation
1.5.1 Computation of Fuzzy Vector of Responses
1.5.2 Computation of Similarity Degree
1.5.3 Result Analysis
1.5.4 Validation
1.5.5 Limitations and Future Study
1.6 Summary
Bibliography
2. Design of Smart and Sustainable Applications Using Intelligent Computational Techniques
R. Rajitha Jasmine, Jhansi Ida, P. Rajesh, K.V. Sreelekha, N. BanuPriya and Dilli Babu M.
2.1 Introduction
2.2 Background
2.2.1 Adoption Models and Theories
2.3 Methodology
2.3.1 Simulation Metrics
2.3.2 Identification of E-Governance Adoption and Assessment Factors
2.3.3 Sample Data Collection Using Questionnaire
2.3.4 Respondent Details
2.3.5 Fuzzy Conjoint Model
2.3.5.1 Calculating the Similarity Degree
2.4 Result Analysis
2.5 Conclusion
References
3. Intelligent Predictive Analysis for Sustainable Global Development
Rajalakshmi S., Swetha R., Vaishali N., P. Umaeswari, S. Senthil Kumar and S. D. Uma Mageswari
3.1 Introduction
3.2 Literature Survey
3.2.1 Concepts of ANN
3.3 Proposed Work
3.3.1 Layout of a Neural Network
3.3.2 Neural Network Structure
3.3.3 Back-Propagation Neural Network
3.3.4 SVM
3.3.5 Learning Sequence of NN
3.3.6 Forage Progressive Network
3.3.7 Neural Network Learning
3.3.8 Model Somatic Cell
3.3.9 Fabricated Visual Structure
3.3.10 Neuron Weight Adjustment
3.4 Results and Discussion
3.4.1 Normalization of Knowledge
3.4.2 Testing and Validation
3.4.3 Error Measures
3.4.4 Prediction Analysis
3.4.5 Overall Prediction Analysis
3.4.6 Actual and Predicted Value Analysis
3.4.7 Overall Actual and Predicted Value Analysis
3.5 Summary
References
4. Intelligent Transport System and Traffic Management Frameworks
S. Srijayanthi, T. Sethukarasi, Sankar R., T. Ramesh, Nayana B. M. and S. Vijayakumar
4.1 Introduction
4.2 Background Study
4.3 Methodology
4.3.1 Segmentation–Fuzzy Clustering
4.3.2 Fuzzy Clustering
4.4 Artificial Neural Network (ANN)
4.5 Proposed Methodology
4.5.1 Detection and Extraction of Traffic Sign
4.5.1.1 Image Extraction and Pre-Processing Using YCbCr
4.5.1.2 Active Appearance Model (AAM)
4.5.1.3 Extracting the Region of Interest
4.5.1.4 Edge Detection Using Sobel Operator
4.5.1.5 Segmentation Using Adaptive Fuzzy Clustering
4.5.1.6 Tracking the Detected Sign
4.5.2 Recognition of Traffic Sign
4.5.2.1 MTANN Training Model for Classification
4.5.2.2 Multiple MTANN Training Models
4.5.2.3 MTANN Classification
4.6 Results and Discussion
4.6.1 LiU Traffic Sign Database
4.6.2 Investigations of Various Classification Techniques
4.7 Summary
References
5. Internet of Things in Smart and Secure Applications Development-Based Sustainability
S. Vijayakumar, A. Thilagavathy, Sankar R., T. Ramesh, N. BanuPriya and S. Srijayanthi
5.1 Introduction
5.2 Literature Survey
5.3 Methodology
5.3.1 Module for the Database
5.3.2 Information Preparation Section
5.3.3 Database Module
5.3.4 Data Preprocessing Module
5.3.5 Optimal Feature Selection Module
5.3.6 Classification Module
5.4 Generative Adversarial Network
5.5 Datasets Used in This Work
5.5.1 NSL-KDD Dataset
5.5.2 CIC-DDoS Dataset
5.6 Performance Measures Used for Evaluation
5.7 Conclusion
References
6. Modern Application for Smart Applications in Traffic Management
S. Selvi, A. Jasmine Gilda, K.V. Sreelekha, N. Banu Priya, Sajithra S. and Lekshmi S. R.
6.1 Introduction
6.2 Related Work
6.3 Delimited Spaces: Proposed Method
6.3.1 Bag of Features (BoF)
6.4 Implementation Details
6.4.1 Datasets
6.4.2 Experiment 2: Original and Split Dictionaries
6.4.3 Experiment 3: Feature Fusion
6.4.4 Results: Delimited Spaces
6.5 Non-Delimited Spaces: Proposed Method
6.5.1 Background Subtraction for Hypothesis Generation
6.5.2 Results: Non-Delimited Spaces
6.6 Conclusion
References
7. Artificial Intelligence and Deep Learning in Healthcare: Evaluation, Opportunities, Challenges and Future Prospects Technologies in Healthcare Systems
Rakesh Ahuja, Gurpreet Kour Khalsa, Vijaita Kashyap, Himanshi Babbar, Ravi Kumar Sachdeva and G. S. Pradeep Ghantasala
7.1 Introduction
7.1.1 Artificial Intelligence
7.1.2 Deep Intelligence
7.2 Applications of AI and Deep Learning in Healthcare
7.2.1 Medical Imaging
7.2.2 Personalized Medicine
7.2.3 Precision Diagnostics
7.2.4 Predictive Analytics
7.2.5 Drug Discovery
7.2.6 Drug Development
7.2.7 Remote Patient Monitoring
7.2.8 Electronic Health Records
7.3 Development of Deep Learning and Artificial Intelligence in Healthcare Sector
7.4 Analytics of Healthcare Data Through AI And DL
7.4.1 Machine Learning Models
7.4.1.1 Supervised Learning
7.4.1.2 Unsupervised Learning
7.4.2 Reinforcement Learning
7.4.3 Natural Language Processing (NLP)
7.4.4 Machine Vision
7.4.5 Data Mining
7.4.6 Artificial Neural Networks
7.4.7 Fuzzy Logic
7.4.8 Expert System
7.5 Deep Learning Models
7.5.1 Convolutional Neural Networks (CNNs)
7.5.2 Network Based on Long Short-Term Memory (LSTM)
7.5.3 Recurrent Neural Networks (RNNs)
7.5.4 Generative Adversarial Network (GANs)
7.5.5 Radial Basis Function Networks (RBFNs)
7.5.6 Multilayer Perceptrons (MLPs)
7.5.7 Self-Organizing Maps (SOMs)
7.6 Potential of AI and Deep Learning Models in Healthcare
7.7 The Rise of AI and DL in Drug Discovery
7.8 Application of AI in Drug Discovery
7.9 Challenges of AI And DL Models
7.10 Future Vision in Developing Rural Health
7.10.1 Telemedicine
7.10.2 Disease Prediction and Prevention
7.10.3 Resource Allocation
7.10.4 Personalized Medicine
7.11 Conclusion
References
8. Heart CAP: Heart Disease Classification: Autoencoders and Principal Components
Kerenalli Sudarshana, Vamsidhar Y. and Santhosh Kumar D. K.
8.1 Introduction
8.2 Related Study
8.3 Model Architecture
8.3.1 Cleveland Heart Disease Dataset
8.3.2 Data Preparation
8.3.3 Feature Scaling
8.3.4 Dimensionality Reduction Techniques
8.3.4.1 Principal Component Analysis (PCA)
8.3.4.2 Autoencoder Architecture
8.4 Results
8.4.1 Classification Performance
8.4.2 Analysis of Principal Component and Feature Coefficients
8.4.3 Analysis of Autoencoder Features
8.4.4 Analysis of Receiver Operating Characteristics
8.5 Conclusion
References
9. Application of Intelligent Computational Techniques in the Development of Smart Cities
K. Kalaivani, S. Arun, K. Ulagapriya, A. Saritha, B. Shanthini and R. Rani Hemamalini
9.1 Introduction
9.1.1 Organization of the Chapter
9.2 Motivation and Justification
9.3 Iris Recognition System
9.4 Algorithm for Iris Recognition System
9.5 Block Diagram of Iris Recognition System
9.5.1 Edge Detection
9.5.2 Variance
9.5.3 Support Vector Machine
9.6 Performance Analysis of Various Biometric Method
9.6.1 Performance Evaluation
9.6.1.1 False Acceptance Ratio (FAR)
9.6.1.2 False Rejection Ratio (FRR)
9.6.2 Result and Discussion
9.7 Summary
Bibliography
10. Security and Privacy Issues in Data Processing with Predictive Models
T. Sethukarasi, A. Thilagavathy, Balasubramanian M., R. Rajitha Jasmine, Shilpa Murali and Anusha S. L.
10.1 Introduction
10.2 Literature Survey
10.3 System Model
10.3.1 Cryptic Framework
10.3.2 Prediction Framework
10.4 System Algorithm
10.5 Results and Discussion
10.5.1 Examining the Security Model’s Performance
10.5.2 Duration of Key Generation
10.5.3 Performance Analysis of Prediction Model
10.5.3.1 Description of the Dataset
10.5.4 Performance Metrics
10.5.4.1 Sensitivity
10.5.4.2 Specificity
10.5.4.3 F-Measure
10.5.4.4 Diabetes Prediction
10.6 Summary of Contributions
References
11. SmartMed: A Blockchain-Based Intelligent System for Managing Patient Data
Swapnil Pawar, Yogesh Jadhav and Ashish Patel
11.1 Introduction
11.1.1 The Need for the Digitization of Medical Records
11.1.2 What is Blockchain?
11.1.3 How to Use Blockchain to Digitize Medical Records
11.2 Literature Survey
11.2.1 State-of-the-Art
11.2.2 Research Gap
11.3 SmartMed: Proposed System
11.4 SmartMed: Model Implementation
11.4.1 Software Requirements
11.4.2 User Registration
11.4.3 Login
11.4.4 Upload Records
11.4.5 View Records
11.4.6 Grant/Revoke Permissions
11.4.7 Encryption of Medical Records
11.4.8 Need for Encryption
11.4.9 Symmetric vs. Asymmetric Encryption
11.5 Result Analysis Using Hybrid Model
11.5.1 Scenario 1: Patient Uploads a Record
11.5.2 Scenario 2: Patient Views the Uploaded Record
11.5.3 Scenario 3: Patient Grants Access to His/Her Records to a Doctor
11.5.4 Scenario 4: Patient Revokes Access to His/Her Records from a Doctor
11.5.5 Scenario 5: Doctor Views the Patient Record
11.5.6 Scenario 6: Doctor Uploads a Record for the Patient
11.6 SmartMed: Performance Evaluation
11.6.1 Latency/Delay
11.6.2 Resource Utilization
11.7 Conclusion and Perspective
References
12. Trinity: A Blockchain-Based Stablecoin Lending Protocol Using Decentralized Credit Default Swaps
Shyam Mohan J. S., Akshit Vig, Parameswaran T., Kota Harsha Surya Abhishek and Sathiyaraj R.
12.1 Introduction
12.1.1 Background and Related Work
12.2 Motivation and Problem Statement
12.3 Methodology
12.3.1 Option Fees Exchanged Among the Participants
12.4 Discussions
12.4.1 Stakeholders in Protocol
12.4.2 Tokens in Protocol
12.4.3 Components of the Protocol
12.4.4 Option Price Calculation
12.4.5 Risk Management and Protocol Tokens/Reserve Currency
12.4.5.1 Use of Protocol Tokens/Reserve Currency in Risk Management
12.4.5.2 Managing Liquidation in Protocol
12.4.5.3 Liquidation Management
12.4.5.4 Calculation of dCDS Holder Accrued Amount During Withdrawal/Close
12.4.6 Utilization of Protocol Tokens as Reserve Currency
12.5 Conclusion
Appendix
References
13. Renewable Energy Integration in Data Centers: Strategies and Challenges
Esha Dhamija, Kamali Singla, Deepali Gupta, Vidhu Kiran and Sapna Juneja
13.1 Introduction
13.2 Renewable Generation Technologies
13.2.1 Photovoltaic Solar
13.2.2 Wind Power
13.2.3 Geothermal Heat
13.3 Impacts of Supply Variability
13.3.1 Power Distribution
13.3.2 Cooling Systems
13.3.3 IT Equipment
13.4 Network Architecture
13.4.1 Renewable Energy Integration Methods
13.4.2 Task Time Management
13.4.3 Integrated Architectures
13.5 Proposed Smart and Sustainable Framework
13.5.1 Geo-Distributed Load Balancing
13.6 Conclusions
References
14. Optimizing Energy Efficiency in Computing Systems: An In-Depth Exploration: Analytics for Sustainable Global Resources
Esha Dhamija, Kamali Singla, Deepali Gupta, Sapna Juneja and Yonis Gulzar
14.1 Introduction
14.2 Energy-Efficient Hardware and Software in Computing Systems
14.3 Smart Solutions to Optimize Energy Efficiency in Computing Systems
14.3.1 Low-Power Processors
14.3.2 Low-Power Memory
14.3.3 On-Chip Interconnects
14.3.4 Hardware Accelerators
14.3.5 Operating Systems
14.3.6 Compilers and Runtimes
14.3.7 Algorithms and Applications
14.3.8 Energy-Efficient Data Centers
14.4 Augmentations of Artificial Intelligence (AI)/Machine Learning (ML)/IoT in Optimizing Energy Efficiency in Computing Systems
14.5 Sustainable Computing
14.6 Conclusions
Acknowledgment
References
About the Editors
Index

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