Intelligent Systems and Sustainable Computational Models
Auerbach (Verlag)
978-1-032-52703-1 (ISBN)
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.
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 Systems, Vellore Institute of Technology, Vellore, India. He obtained Ph. D degree from Anna University,Chennai during the year 2010 for his thesis entitled “Synthesizing Global Association Rules in Multi-Database Mining”. Having 22 years of experience in Higher education & research, regularly he contributes papers in various reputed international Journals. Some of his research works have been published in journals from Springer, Elsevier, Wiley, World-Scientific and other publishers. He has functioned as resource person in more than 20 Faculty Development Programme and organized seminars and workshops which are funded by ISTE, AICTE and others. He has successfully guided three research scholars to lead to Ph. D degree. Dr. C. Pethuru Raj is working as a Vice President and Chief Architect at Reliance Jio Platforms Ltd. (JPL) Bangalore. Previously. worked in IBM Global Cloud Centre of Excellence (CoE), Wipro consulting services (WCS), and Robert Bosch Corporate Research (CR). I have gained over 22 years of IT industry experience and 9 years of research experience. Finished the CSIR-sponsored PhD degree at Anna University, Chennai and continued with the UGC-sponsored postdoctoral research in the Department of Computer Science and Automation, Indian Institute of Science (IISc), Bangalore. After that, I was granted two international research fellowships (JSPS and JST) to work as a research scientist for 3.5 years in two leading Japanese universities. He is focusing on some of the emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI) Model Optimization Techniques, Prompt Engineering for Large Language Models (LLMs), Efficient, Explainable and Edge AI, Blockchain, Digital Twins, Cloud-native computing, Edge and Serverless computing, Site Reliability Engineering (SRE), Platform Engineering, 5G, etc.
1. Smart Power Management in Data Centers Using Machine-Learning Techniques, 2. Exploring the Power of Deep Learning and Big Data in Flood Forecasting: State-of-the-Art Techniques and Insights, 3. Storage Management Techniques for Medical Internet of Things (MIoT), 4. A Study on Trending Technologies for IoT Use Cases Aspires to Build Sustainable Smart Cities, 5. Hydro-Meteorological Disaster Prediction Using Deep Learning Techniques, 6. Assessment of ICT for Sustainable Developments with Reference to Fog and Cloud Computing, 7. Explainable Artificial Intelligence (XAI) for Computational Sustainability: Concepts, Opportunities, Challenges, and Future Directions, 8. Edge Computing-Based Intrusion Detection Systems: A Review of Applications, Challenges, and Opportunities, 9. Recent Advancements in IoT Security-Based Challenges: A Brief Review, 10. An Approach to Smart Targeted Advertising Using Deep Convolutional Neural Networks, 11. Text Classification of Customer and Salesperson Conversations to Predict Sales Using Ensemble Models, 12. Sentimental Analysis on Amazon Book Reviews: A Deep Learning Approach, 13. A Deep LSTM Recurrent Learning Approach for Sentiment Analysis on Movie Reviews, 14. Cognitive Intelligent Personal Learning Assistants for Enriching Personalized Learning, 15. Natural Language Processing for Fake News Detection Using Hybrid Deep Learning Techniques, 16. A Comparative Analysis of Deep Learning Models for Fake News Detection and Popularity Prediction of Articles, 17. Internet of Things (IoT)-Based Smart Maternity Healthcare Services, 18. A Real-Time Automated Face Recognition and Detection System for Competitive Examination, 19. Medical Image Analysis with Vision Transformers for Downstream Tasks and Clinical Report Generation, 0. Ensemble Embedding and Convolutional Neural Network-Based Big Data Framework for Structure Prediction of Proteins, 21. Deep Learning-Based Automated Diagnosis and Prescription of Plant Diseases, 22. Intelligent Farming Through Weather Forecasting Using Deep Learning Techniques for Enhancing Crop Productivity, 23. Plant Disease Detection and Classification Using a Deep Learning Approach for Image-Based Data, 24. Deep Learning-Based Object Detection in Real-Time Video, 25. Prediction of COVID Stages Using Data Analysis and Machine Learning, 26. A Statistical Analysis of Suitable Drugs for Major Drug Resistant Mutations in the HIV-1 Group M Virus
| Erscheinungsdatum | 03.05.2024 |
|---|---|
| Zusatzinfo | 82 Tables, black and white; 120 Line drawings, color; 30 Line drawings, black and white; 59 Halftones, color; 5 Halftones, black and white; 179 Illustrations, color; 35 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 948 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Recht / Steuern ► Privatrecht / Bürgerliches Recht ► IT-Recht | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 1-032-52703-X / 103252703X |
| ISBN-13 | 978-1-032-52703-1 / 9781032527031 |
| Zustand | Neuware |
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