Intelligent Systems and Sustainable Computational Models Concepts, Architecture, and Practical Applications

The fields of intelligent systems and sustainability have been gaining momentum in the research community. They have drawn interest in such research fields as computer science, information technology, electrical engineering, and other associated engineering disciples. The promise of intelligent systems applied to sustainability is becoming a reality due to the recent advancements in the Internet of Things (IoT), Artificial Intelligence, Big Data, blockchain, deep learning, and machine learning. The emergence of intelligent systems has given rise to a wide range of techniques and algorithms using an ensemble approach to implement novel solutions for complex problems associated with sustainability.

Intelligent Systems and Sustainable Computational Models: Concepts, Architecture, and Practical Applications explores this ensemble approach towards building a sustainable future. It explores novel solutions for such pressing problems as smart healthcare ecosystems, energy efficient distributed computing, affordable renewable resources, mitigating financial risks, monitoring environmental degradation, and balancing climate conditions. The book helps researchers to apply intelligent systems to computational sustainability models to propose efficient methods, techniques, and tools. The book covers such areas as:

• Intelligent and adaptive computing for sustainable energy, water, and transportation networks
• Blockchain for decentralized systems for sustainable applications, systems, and infrastructure
• IoT for sustainable critical infrastructure
• Explainable AI (XAI) and decision-making models for computational sustainability
• Sustainable development using edge computing, fog computing and cloud computing
• Cognitive intelligent systems for e-learning
• Artificial Intelligence and machine learning for large scale data
• Green computing and cyber physical systems

Real-time applications in healthcare, agriculture, smart cities, and smart governance.

By examining how intelligent systems can build a sustainable society, the book presents systems solutions that can benefit researchers and professionals in such fields as information technology, health, energy, agricultural, manufacturing, and environmental protection.

1. Smart Power Management in Data Centers using Machine Learning Techniques
Ashoka D V, Rekha P M, and Sudha P. R

2. Exploring the Power of Deep Learning and Big Data in Flood Forecasting: State-of-the-Art Techniques and Insights
Selva Jeba G and Chitra P

3. Storage Management Techniques for Medical Internet of Things (MIoT)
S U Muthunagai, M S Girija, B Praveen Kumar, and R Anitha

4. A Study on Trending Technologies for IoT Use Cases Aspires to Build Sustainable Smart Cities
Mangayarkarasi Ramaiah, R. Mohemmed Yousuf, R. Vishnukumar, and Adla Padma

5. Hydro-Meteorological Disasters Prediction Using Deep Learning Techniques
Kaviya P and Chitra P

6. Assessment of ICT for Sustainable Developments with Reference to Fog and Cloud Computing
Shilpa H.K, Girija D.K., Rashmi M, and Yogeesh N

7. XAI for Computational Sustainability-Concepts, Opportunities, Challenges, and Future Directions
B. Prabadevi, M. Pradeepa, and S. Kumaraperumal

8. Edge Computing Based Intrusion Detection Systems: A Review of Applications, Challenges, and Opportunities
Posham Uppamma and Sweta Bhattacharya

9. Recent Progress in IoT security-based Challenges: A Brief Review
Suranjeet Chowdhury Avik, Abdullahi Chowdhury, Ranesh Naha, Shahriar Kaisar, Arunkumar Arulappan, and Aniket Mahanti

10. An Approach to Smart Targeted Advertising Using a Deep Convolution Neural Network
Gayathri A, Ruby D, Manikandan N, and T Gopalakrishnan

11. Text Classification of Customer and Salesperson Conversations to Predict Sales Using Ensemble Models
T. Chellatamilan and Neel Rakesh Choksi

12. Sentimental Analysis on Amazon Book Reviews: A Deep Learning Approach
Vijayalakshmi A, Koesha Sinha , Debopriya Bose

13. A Deep LSTM Recurrent Learning Approach for Sentiment Analysis of Movie Reviews
Khanaghavalle G R, Rajalakshmi V , R Jayabhaduri, A Kala, and P Sharon Femi

14. Cognitive Intelligent Personal Learning Assistants for Enriching Personalised Learning
D. Ramalingam, Mahalakshmi Dharmalingam

15. Natural Language Processing for Fake News Detection using Hybrid Deep Learning Techniques
B. Valarmathi, Aditya Kocherlakota, Yuvraj Das, Aritam, N.Srinivasa Gupta, and V. Mohanraj

16. A Comparative Analysis of Deep Learning Models for Fake News Detection and Popularity Prediction of Articles
Jayanthi Devaraj

17. An Internet of Things (IoT) based Smart Maternity Healthcare Services
Vinothiyalakshmi P, Pallavi V, Rajganesh N, and Adityavignesh V

18. Internet of Things (IoT) based Smart Maternity Healthcare Services
Vinothiyalakshmi P, Pallavi V, Rajganesh N, and Adityavignesh V

19. Medical Image Analysis with Vision Transformers for Downstream Tasks and Clinical Report Generation
Evans Kotei,, and Ramkumar Thirunavukarasu

20. Ensemble Embedding and Convolutional Neural Network based Big Data Framework for Structure Prediction of Proteins
Leo Dencelin Xavier, Ramkumar Thirunavukarasu, Rajganesh Nagarajan, and Mohamed Uvaze Ahamed Ayoobkhan

21. Deep Learning based Automated Diagnosis and Prescription of Plant Diseases
R. K. Kapila Vani, P. Geetha, D. Abhishek, K. Gokul Krishna, and V. Akaash

22. Intelligent Farming through Weather Forecasting Using Deep Learning Techniques for Enhancing Crop Productivity
Ezhilarasi V, Selvamuthukumaran S, and Srinivasan N

23. Plant Disease Detection and Classification using Deep Learning Approach for Image-based Data
Tamil Priya. D and Vijayarani. A

24. Deep Learning based Object Detection in Real Time Video
T Sukumar

25. Prediction of Covid Stages Using Data Analysis and Machine Learning
Rajalakshmi Gurusamy, Siva Ranjani S, and Susan Shiny G

26. A Statistical Analysis of Suitable Drug for Major Drug Resistance Mutations in HIV–1 Group M
Durga Shree N, Steve Mathew D A, Ramkumar Thirunavukarasu, and Arun Pandian J

Dr. N. Rajganesh is presently working as Associate Professor in the Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Tamilnadu, India. He obtained Ph. D degree from Anna University during the year 2018 for his thesis entitled “Fuzzy based Intelligent Semantic Cloud Service Discovery for Effective Utilization of Services”. Having 18 years of experience in Teaching and contributes research findings in various reputed international Journals. During his career, he has attended more than 20 Faculty Development Program/Workshop/Seminar, which are sponsored by AICTE, UGC, ISTE, and Anna University. He has functioned as a resource person in more than 10 Faculty Development Programme and organized seminars and workshops. He is functioning as an active reviewer for top-notch journals from IEEE, Springer, Elsevier, and other publishers.

Dr. Senthil Kumar N is an Assistant Professor (Senior) in the Department of Computer Applications, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology (VIT), Vellore. He has been working at VIT for more than 15+ years and totally, he has 18+ years of teaching experience. Currently, he is the RAAC coordinator for the school. Prior to that, he was the Proctor Coordinator and Project Coordinator of the school. He has delivered guest lectures, special talks and webinars at various engineering colleges on the topics of Natural Language Processing, Big Data Analytics, Data Science and Cyber Security. His research interests are NLP, Machine Learning and Semantic Web. In this connection, he has published more research articles in SCOPUS indexed journals and also published research papers at various conferences as well. He is an avid reader and always encourages others to read a lot on the subject that they are really inclined.

Dr. T. Ramkumar is presently working as Professor in the School of Computer Science Engineering and Information Sy