Applying Computational Intelligence for Social Good
Track, Understand and Build a Better world
- 1st Edition, Volume 132 - January 14, 2024
- Editors: Preetha Evangeline David, P Anandhakumar
- Language: English
- Hardback ISBN:9 7 8 - 0 - 3 2 3 - 8 8 5 4 4 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 8 8 5 4 5 - 4
Applying Computational Intelligence for Social Good: Track, Understand and Build a Better World, Volume 132 presents views on how Computational Intelligent and ICT technolog… Read more
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Request a sales quoteApplying Computational Intelligence for Social Good: Track, Understand and Build a Better World, Volume 132 presents views on how Computational Intelligent and ICT technologies can be applied to ease or solve social problems by sharing examples of research results from studies of social anxiety, environmental issues, mobility of the disabled, and problems in social safety. Sample chapters in this release include Why is implementing Computational Intelligence for social good so challenging? Principles and its Application, Smart crisis management system for road accidents using Geo-Spacial Machine Learning Techniques, Residential Energy Management System (REMS) Using Machine Learning, Text-Based Personality Prediction using XLNet, and much more.
- Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation for solving socially relevant problems
- Focuses on Forecasting applications, Human Behavior and Critics response analysis in social forums, Healthcare monitoring Systems, Disaster Management, Industrial management, and most recently, Epidemics and Outbreaks
- Brings together many different aspects of the current research on intelligence technologies, such as neural networks, support vector machines, fuzzy logic, and evolutionary computation
Researchers from Industries, Academicians, carrying out transdisciplinary research and working in the field of Epidemics and outbreaks, Healthcare monitoring, Smart Agriculture, IoT, Intelligent Systems etc. ICTs Academicians and UG/PG Computer science students involved in doing projects in the fields of Data Science, Internet of Things and Artificial Intelligence and Machine and Deep Learning. Cloud/Edge/Fog and IoT Architects. Artificial Intelligence experts
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Chapter One Why is implementing computational intelligence for social good so challenging? Principles and its application
- Abstract
- 1 Introduction
- 2 What is computational intelligence?
- 3 What is the difference between artificial intelligence and computational intelligence?
- 4 Science and engineering
- 5 Relationship to other disciplines
- 6 Representation and reasoning
- 7 Ontology and conceptualization
- 8 What is the impact of computational intelligence on society?
- 9 Challenges to be faced
- 10 Positive impacts of computational intelligence on society
- 11 Mapping CI use cases to domains of social good
- 12 Perceptions in depth
- Further reading
- Chapter Two Smart crisis management system for road accidents based on Modified Convolutional Neural Networks–Particle Swarm Optimization hybrid algorithm
- Abstract
- 1 Background
- 2 Implementation
- 3 Post-detection steps
- 4 Conclusion
- References
- Further reading
- Chapter Three Residential energy management system (REMS) using machine learning
- Abstract
- 1 Introduction
- 2 Proposed smart energy management system
- 3 Proposed smart REMS model
- 4 Load shifting algorithm
- 5 Solar power generation prediction using ANN
- 6 Battery optimization algorithm
- 7 Results and discussions
- 8 Conclusion
- References
- Chapter Four Text-based personality prediction using XLNet
- Abstract
- 1 Introduction
- 2 Related works
- 3 Method
- 4 Results and discussion
- 5 Conclusion
- References
- Chapter Five Articulating the power of reasoning and gathering data for information security and justice
- Abstract
- 1 Introduction
- 2 Five challenges to AI security
- 3 Typical AI security attacks
- 4 AI Security layered defense
- 5 AI model security
- 6 Security architectures of AI services
- 7 Collaborating for a safe and a smart future
- References
- Chapter Six Bayesian network-based quality assessment of blockchain smart contracts
- Abstract
- 1 Introduction
- 2 Literature work
- 3 Smart contract quality metrics
- 4 Proposed work
- 5 Experimental and comparison results
- 6 Conclusion
- References
- Chapter Seven Short-term wind power prediction using deep learning approaches
- Abstract
- 1 Introduction
- 2 Dataset and components of wind turbine
- 3 Proposed work
- 4 Conclusion and future works
- Conflicts of Interest
- References
- Chapter Eight Cyber data trend and intelligent computing
- Abstract
- 1 Introduction
- 2 AI defense against today's cyber threats
- 3 Understanding cyber security data
- 4 AI-driven cybercrimes
- 5 Executive perspectives
- 6 Zero trust
- 7 Beefing up basic cyber hygiene
- 8 Engineered security automation and orchestration
- 9 Machine learning tasks in cyber security
- 10 Research issues and future directions
- 11 Discussion
- 12 Conclusion
- Further reading
- Chapter Nine Reshaping agriculture using intelligent edge computing
- Abstract
- 1 Introduction
- 2 Smart agriculture
- 3 Smart farming initiatives is the need of the hour
- 4 Commonly used sensors for smart farming and heavy metal identification
- 5 High performance computing on edge (HPCE)
- 6 Processing in agriculture
- 7 The proposed system
- References
- Further reading
- Chapter Ten An automatic path navigation for visually challenged people using deep Q learning
- Abstract
- 1 Introduction
- 2 Literature survey
- 3 Proposed work
- 4 Implementation and algorithm
- 5 Results and output
- 6 Conclusion
- References
- Chapter Eleven Delineating computational intelligence during epidemic emergencies and outbreaks
- Abstract
- 1 Introduction
- 2 Literature review
- 3 Method
- 4 Results
- 5 Limitations
- 6 Conclusion
- References
- Chapter Twelve ClubNet: Deep learning model for computation, calibration and estimation of biotic stress in crops
- Abstract
- 1 Introduction
- 2 Related works
- 3 Dataset and pre-processing
- 4 Proposed ClubNet model
- 5 Experimental setup
- 6 Results and discussion
- 7 Conclusion
- References
- Further reading
- Chapter Thirteen Automatic programming (source code generator) based on an ontological model
- Abstract
- 1 Introduction
- 2 Automatic code generation use cases
- 3 Materials and methods
- 4 Experimentation details
- 5 Results
- 6 Training results
- 7 Discussion
- 8 Conclusions
- References
- Further reading
- No. of pages: 503
- Language: English
- Edition: 1
- Volume: 132
- Published: January 14, 2024
- Imprint: Academic Press
- Hardback ISBN: 9780323885447
- eBook ISBN: 9780323885454
PD
Preetha Evangeline David
Dr. Preetha Evangeline David is currently working as an Associate Professor and Head of the Department in the Department of Artificial Intelligence and Machine Learning at Chennai Institute of Technology, Chennai, India. She holds a PhD from Anna University, Chennai in the area of Cloud Computing. She has published many research papers and Patents focusing on Artificial Intelligence, Digital Twin Technology, High Performance Computing, Computational Intelligence and Data Structures. She is currently working on Multi-disciplinary areas in collaboration with other technologies to solve socially relevant challenges and provide solutions to human problems.
Affiliations and expertise
Associate Professor and Head of the Department in the Department of Artificial Intelligence and Machine Learning at Chennai Institute of Technology, Chennai, India.PA
P Anandhakumar
Dr. Anandhakumar is a professor in the Department of Information Technology at Anna University, Chennai. He has completed his doctorate in the year 2006 from Anna University. He has produced 17 PhD's in the field of Image Processing, Cloud Computing, Multimedia technology and Machine Learning. His ongoing research lies in the field of Digital Twin Technology, Machine Learning and Artificial Intelligence. He has published more than 150 papers indexed in SCI, SCOPUS, WOS etc.
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