Predictive Methods in Next-Generation Computing

An Approach Toward Sustainability
1. Auflage 2026
ISBN: 978-1-394-24879-7
Verlag: Wiley

Buch, Englisch, 352 Seiten

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.

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.

Autoren/Hrsg.

Sathiyaraj, R.

Dhanaraj, Rajesh Kumar

Arun Kumar, K.

Jhaveri, Rutvij H

Mohamed Abbas, A.

Fachgebiete

• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze
• Geisteswissenschaften Architektur Städtebau, Stadtplanung (Architektur)

Inhaltsverzeichnis

Preface xv

1 Introduction to Intelligent Computational Technologies 1
C. Geetha, Sajithra S., S. Srijayanthi, B. Reena, I. Subha and Sreelakshmi N.

2 Design of Smart and Sustainable Applications Using Intelligent Computational Techniques 23
R. Rajitha Jasmine, Jhansi Ida, P. Rajesh, K.V. Sreelekha, N. BanuPriya and Dilli Babu M.

3 Intelligent Predictive Analysis for Sustainable Global Development 47
Rajalakshmi S., Swetha R., Vaishali N., P. Umaeswari, S. Senthil Kumar and S. D. Uma Mageswari

4 Intelligent Transport System and Traffic Management Frameworks 65
S. Srijayanthi, T. Sethukarasi, Sankar R., T. Ramesh, Nayana B. M. and S. Vijayakumar

5 Internet of Things in Smart and Secure Applications Development-Based Sustainability 83
S. Vijayakumar, A. Thilagavathy, Sankar R., T. Ramesh, N. BanuPriya and S. Srijayanthi

6 Modern Application for Smart Applications in Traffic Management 107
S. Selvi, A. Jasmine Gilda, K.V. Sreelekha, N. Banu Priya, Sajithra S. and Lekshmi S. R.

7 Artificial Intelligence and Deep Learning in Healthcare: Evaluation, Opportunities, Challenges and Future Prospects Technologies in Healthcare Systems 125
Rakesh Ahuja, Gurpreet Kour Khalsa, Vijaita Kashyap, Himanshi Babbar, Ravi Kumar Sachdeva and G. S. Pradeep Ghantasala

8 Heart CAP: Heart Disease Classification: Autoencoders and Principal Components 149
Kerenalli Sudarshana, Vamsidhar Y. and Santhosh Kumar D. K.

9 Application of Intelligent Computational Techniques in the Development of Smart Cities 175
K. Kalaivani, S. Arun, K. Ulagapriya, A. Saritha, B. Shanthini and R. Rani Hemamalini

10 Security and Privacy Issues in Data Processing with Predictive Models 193
T. Sethukarasi, A. Thilagavathy, Balasubramanian M., R. Rajitha Jasmine, Shilpa Murali and Anusha S. L.

11 SmartMed: A Blockchain-Based Intelligent System for Managing Patient Data 207
Swapnil Pawar, Yogesh Jadhav and Ashish Patel

12 Trinity: A Blockchain-Based Stablecoin Lending Protocol Using Decentralized Credit Default Swaps 233
Shyam Mohan J. S., Akshit Vig, Parameswaran T., Kota Harsha Surya Abhishek and Sathiyaraj R.

13 Renewable Energy Integration in Data Centers: Strategies and Challenges 271
Esha Dhamija, Kamali Singla, Deepali Gupta, Vidhu Kiran and Sapna Juneja

14 Optimizing Energy Efficiency in Computing Systems: An In-Depth Exploration: Analytics for Sustainable Global Resources 289
Esha Dhamija, Kamali Singla, Deepali Gupta, Sapna Juneja and Yonis Gulzar

Acknowledgment 303
References 303
About the Editors 309
Index 313

Über Autor(innen)

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.

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.

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.

Rutvij H. Jhaveri, PhD is an Associate Professor in the Department of Computer Science and Engineering at Pandit Deendayal Energy University. He has published more than 200 papers in a wide variety of areas in computer scienceand coedited four books. His research interests include software-defined networking, cybersecurity, and smart ecosystems.

A. 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.