
Mathematical Computing and Sustainability
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The book is likely intended to provide a thorough knowledge of the complex relationships between computational intelligence, mathematical computing, and sustainability. By taking an interdisciplinary approach, the author may strive to connect theoretical frameworks with practical applications, providing readers with a road map for navigating the intricacies of addressing long-term difficulties. The book could use case studies and examples to demonstrate how cutting-edge technologies and mathematical models can be used to analyse and solve real-world sustainability problems, ultimately encouraging a holistic approach that fosters innovative solutions based on computational and mathematical principles.
This book is planned to cover the comprehensive investigation into the synergies between Computational Intelligence (CI), Mathematical Computing, and Sustainability. An examination of the possible impact of intelligent systems on sustainability, new concepts and approaches for incorporating CI and mathematical computing into sustainable practices etc. There will be chapters explaining the Exploration of upcoming technologies (e.g., quantum computing, bio-inspired computing) and their potential role in promoting sustainability.
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Persons
Dr. Shalli Rani ( Director, Research & Senior Member of IEEE) completed her Post-doc from Manchester Metropolitan University, UK in June , 2023. She is Professor in Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India. She has 18+ years teaching experience. She received MCA degree from Maharishi Dyanand University, Rohtak in 2004 and the M.Tech. degree in Computer Science from Janardan Rai Nagar Vidyapeeth University, Udaipur in 2007 and Ph.D. degree in Computer Applications from Punjab Technical University, Jalandhar in 2017. Her main area of interest and research are Wireless Sensor Networks, Underwater Sensor networks, Machine Learning and Internet of Things. She has published/accepted/presented more than 100+ papers in international journals /conferences (SCI+Scopus) and edited/authored five books with international publishers. She is serving as the associate editor of IEEE Future Directions Letters. She served as a guest editor in IEEE Transaction on Industrial Informatics, Hindawi WCMC and Elsevier IoT Journals. She has also served as reviewer in many repudiated journals of IEEE, Springer, Elsevier, IET, Hindawi and Wiley. She has worked on Big Data, Underwater Acoustic Sensors and IoT to show the importance of WSN in IoT applications. She received a young scientist award in Feb. 2014 from Punjab Science Congress, Lifetime Achievement Award and Supervisor of the year award from Global Innovation and Excellence, 2021
Dr. Syed Hassan Shah (Senior Member of IEEE) is a Wi-Fi connectivity subject matter expert with the Qualcomm Inc. product management team, where he is involved in Consumer and Compute Wireless products with a focus on Mi-Fi, CPE, and UWB technologies. In addition to that, Dr. Shah is also an adjunct faculty member at California State University, Fullerton Campus, where he teaches computer science courses to graduate classes. Over the past decade, Dr. Shah held multiple industrial & academic roles such as a Product Specialist for Distributed Antenna Systems (DAS), CBRS, Private LTE, Digital Electricity, and open RAN product lines. Dr. Shah was an Assistant Professor in the Department of Computer Science at Georgia Southern University, USA followed by a Post-Doctoral Fellowship at the University of Central Florida, Orlando, USA. Before moving to the United States, he completed his BS with honors in CS from Kohat University of Science & Technology (KUST), Pakistan, and his Ph.D. Degree (combined with Masters) from the School of Computer Science and Engineering (SCSE), Kyungpook National University (KNU), Republic of Korea (South Korea).
Dr. Ayush Dogra ( Allied Member - European Society of Oncological Imaging ) is currently working as an Assistant Director -Research , CURIN , Chitkara University , Punjab , India. He is graduated in Bachelor of Engineering and Technology (ECE) from Guru Nanak Dev University, Amritsar in 2011. He has received his Master's degree in Electronics & Communication Engineering from Punjabi University, Patiala in 2013 and Master's degree in Business Management (MBA) from IGNOU, Delhi ( Central University) during 2015. He completed his Doctorate Degree from Department of Electronics and Communications engineering from Oct-2015 to Jan-2019 from Panjab University.His work area focuses on devising a novel and innovative, market-oriented mechanism for medical image fusion
He has also worked as CSIR-Nehru Post-Doctoral Researcher in Biomedical Applications Unit, CSIR-Central Scientific Instruments Organization, Chandigarh. In the past, he worked as the Junior and Senior Research Fellow in the Department of Electronics and Communication Engineering, UIET, Panjab University, Chandigarh. Also worked as Post-Doctoral fellow in the Department of Center of Biomedical Engineering, IIT Ropar .
Content
- Intro
- Preface
- Contents
- List of contributors
- 1 Leveraging computational intelligence and mathematical modeling for sustainable future in agriculture: a unified paradigm for recognizing tomato leaf diseases
- 1.1 Introduction
- 1.1.1 Motivation
- 1.1.2 Problem statement
- 1.1.3 Contribution
- 1.2 Literature review
- 1.3 Proposed methodology
- 1.3.1 Preprocessing
- 1.3.1.1 Local brighten
- 1.3.1.2 Averaging filter
- 1.3.1.3 Reduce haze
- 1.3.1.4 De-noise network
- 1.3.2 Data augmentation
- 1.3.2.1 Dataset
- 1.3.3 Pretrained deep models
- 1.3.3.1 MobileNetV2
- 1.3.3.2 EfficientNetB0
- 1.3.4 Deep transfer learning
- 1.3.5 Feature extraction
- 1.3.6 Feature selection
- 1.3.6.1 Moth flame optimization
- 1.3.6.2 MFO algorithm
- 1.3.6.3 Generating the initial population of moths
- 1.3.6.4 Updating the moth's position
- 1.3.6.5 Updating the number of flames
- 1.3.7 Serial based fusion
- 1.3.8 Classification
- 1.4 Results and discussion
- 1.4.1 Experimental setup
- 1.4.2 Results and analysis
- 1.4.2.1 Experiment no. 1
- 1.4.2.2 Experiment No. 2
- 1.4.2.3 Experiment No. 3
- 1.4.2.4 Experiment No. 4
- 1.4.2.5 Experiment No. 5
- 1.4.3 Comparison table
- 1.4.4 Analysis
- 1.5 Conclusion
- References
- 2 Digital dawn: how immersive technologies are shaping a sustainable future
- 2.1 Introduction
- 2.2 The role of emerging technologies in sustainable development
- 2.2.1 Artificial intelligence
- 2.2.2 Blockchain technology
- 2.2.3 Internet of Things (IoT)
- 2.2.4 Virtual reality and augmented reality
- 2.2.5 5G and telecommunication
- 2.3 The metaverse as a tool for sustainable development
- 2.3.1 Metaverse application aligned with the SDGs
- 2.3.1.1 Remote collaboration and work
- 2.3.1.2 Accessible environmental education
- 2.3.1.3 Virtual economics
- 2.4 Real-world use cases and projects in the metaverse for sustainability
- 2.4.1 Environmental concerns
- 2.4.2 Social impact initiatives
- 2.4.3 Digital twin for urban planning
- 2.5 Challenges and limitations of emerging technologies in sustainable development
- 2.5.1 Energy consumption and environmental cost
- 2.5.2 Ethical and privacy concerns
- 2.5.3 Digital divide and accessibility
- 2.6 Future directions and potential innovations
- 2.6.1 Emerging trends in sustainability focused technologies
- 2.6.2 Cross-sector collaboration
- 2.6.3 Path toward a responsible tech ecosystem
- 2.7 Conclusion
- References
- 3 Sustainable intelligence: ethical issues in the evolution of intelligent systems
- 3.1 Introduction
- 3.1.1 Intelligence systems and their significance in sustainability
- 3.1.2 Application of intelligent systems in sustainability
- 3.1.3 Examples of intelligent systems advancing sustainability
- 3.1.4 Ethical issues and transformative potential
- 3.1.5 Structure of the chapter
- 3.2 Ethics in intelligent systems for sustainability
- 3.2.1 Key ethical principles
- 3.2.2 Addressing ethical concerns importance
- 3.3 Environmental sustainability and ethical challenges in intelligent system
- 3.3.1 Role of IS in advancing environmental sustainability
- 3.3.2 Ethical constraints in integrating IS for environmental sustainability
- 3.4 Addressing ethical challenges in intelligent systems for sustainability
- 3.4.1 Enhancing explainability and transparency
- 3.4.2 Ensuring governance and accountability
- 3.4.3 Reducing bias
- 3.4.4 Balancing sustainability goals with data security
- 3.4.5 Mitigating environmental effects
- 3.4.6 Developing trust and involving stakeholders
- 3.4.7 Novel strategies for ethical management
- 3.5 Case studies
- 3.5.1 Agriculture sector and its ethical challenges
- 3.5.2 Energy sector and its ethical challenges
- 3.5.3 Urban planning and its ethical challenges
- 3.6 Future direction and emerging ethical issues
- 3.6.1 Advanced intelligent autonomous systems
- 3.6.2 IoT integration
- 3.6.3 Energy-efficient IS systems
- 3.7 Conclusion
- References
- 4 Energy sustainability and computational intelligence based routing protocols in WSN: an analytical survey
- 4.1 Introduction
- 4.2 Consumption of energy and waste in WSNs
- 4.3 Hardware-based energy sustainability in WSNs
- 4.3.1 The architecture of WSN nodes
- 4.3.2 Hardware-based methods for energy sustainability
- 4.3.2.1 Energy-saving techniques applied in submodules
- 4.3.2.2 Energy harvesting
- 4.3.2.3 Wireless energy transfer
- 4.4 Algorithm-based energy sustainability in WSNs
- 4.4.1 Protocol stack of sensor nodes and BSs
- 4.4.2 Algorithm-based methods for energy sustainability in WSNs
- 4.5 Routing protocol classification using an intelligent algorithm
- 4.6 Computational intelligent algorithms
- 4.6.1 RL
- 4.6.2 Fuzzy logic (FL)
- 4.6.3 Ant colony optimization (ACO)
- 4.6.4 Genetic algorithm
- 4.6.5 Neural networks
- 4.7 CI-based representative routing protocols
- 4.8 Conclusions
- References
- 5 Harnessing the metaverse for healthcare innovation: exploring predictive analytics and AI-driven personalization
- 5.1 Introduction
- 5.1.1 Overview of the metaverse and its impact on healthcare
- 5.1.2 Importance of predictive analytics and AI in modern healthcare
- 5.1.3 Chapter objectives and key focus areas
- 5.2 Definition and elements of the metaverse in healthcare
- 5.2.1 Defining the metaverse
- 5.2.2 Key components: virtual reality, augmented reality, and AI
- 5.2.2.1 Virtual reality (VR)
- 5.2.2.2 Augmented reality (AR)
- 5.2.2.3 Artificial intelligence (AI)
- 5.2.3 How the metaverse is reshaping healthcare
- 5.2.3.1 Telemedicine and remote consultations
- 5.2.3.2 Medical training and education
- 5.2.3.3 Personalized medicine and patient engagement
- 5.2.3.4 Collaborative care and research
- 5.2.3.5 Ethical and legal considerations
- 5.3 Predictive analytics in the metaverse
- 5.3.1 Introduction to predictive analytics
- 5.3.2 Role of Big Data and machine learning in predictive analytics
- 5.3.3 Applications in personalized medicine
- 5.3.4 Case studies: Predictive analytics in healthcare within the metaverse
- 5.3.4.1 Virtual health assistants
- 5.3.4.2 Remote surgery simulations
- 5.3.4.3 Chronic disease management
- 5.4 AI-driven personalization in healthcare
- 5.4.1 Understanding AI-driven personalization
- 5.4.2 How AI enhances patient care through personalization
- 5.4.3 Integration of AI and the metaverse in healthcare solutions
- 5.4.4 Examples of AI-driven personalized healthcare in the metaverse
- 5.4.4.1 Personalized virtual therapy sessions
- 5.4.4.2 AI-powered health monitoring
- 5.5 Challenges and ethical considerations
- 5.5.1 Challenges in implementing predictive analytics and AI in healthcare
- 5.5.2 Data privacy and security concerns in the metaverse
- 5.5.3 Ethical implications of AI-driven decisions in healthcare
- 5.5.4 Legal aspects of metaverse-integrated healthcare solutions
- 5.6 Future trends and innovations
- 5.6.1 Emerging trends in metaverse-enabled healthcare
- 5.6.2 Future potential of predictive analytics and AI in personalized medicine
- 5.6.3 The role of blockchain and other emerging technologies
- 5.6.4 Vision for the future: a fully integrated metaverse healthcare system
- 5.7 Case study: real-world applications
- 5.7.1 Detailed analysis of a case study implementing predictive analytics and AI in the metaverse
- 5.7.2 Outcomes, lessons learned, and best practices
- 5.8 Conclusion
- 5.8.1 Summary of key points
- 5.8.2 The future of healthcare in the metaverse
- 5.8.3 Final thoughts on the role of predictive analytics and AI in healthcare innovation
- References
- 6 Toward a sustainable future: a computational intelligence fusion framework of color and darknet features for the classification of crop leaf diseases
- 6.1 Introduction
- 6.2 Literature review
- 6.3 Proposed methodology
- 6.3.1 Datasets
- 6.3.2 Proposed contrast enhancement technique
- 6.3.2.1 Brightness-preserving bi-histogram equalization (BBHE)
- 6.3.2.2 Dualistic sub-image histogram equalization (DSIHE)
- 6.3.3.3 Color features extraction
- Mean
- Variance
- Standard deviation
- Skewness and kurtosis
- Harmonic mean
- 6.3.3.4 DarkNet-53 features
- 6.3.3.5 Henry gas solubility optimization (HGSO)
- 6.4 Experimental results and analysis
- 6.4.1 Wheat dataset results
- 6.4.2 Cotton dataset results
- 6.4.3 t-Test-based analysis
- t-Test table (two-tailed) for wheat
- t-Test table (two-tailed) for cotton
- 6.5 Conclusion
- References
- 7 Sustainable computing approaches for complex medical image analysis: a neurodiagnostic perspective
- 7.1 Introduction
- 7.2 Background and motivation
- 7.3 Sustainable computing in medical imaging
- 7.4 Neurodiagnostic imaging: techniques and challenges
- 7.5 Deep learning and interpretability in neurodiagnostics
- 7.6 Performance evaluation metrics and mathematical modeling
- 7.7 Case study: lightweight interpretable AI for neurodiagnostic imaging
- 7.8 Comparative sustainability of CNN architectures in neurodiagnostic imaging
- 7.9 Impact of compression techniques on model efficiency and performance
- 7.10 Hardware-aware deployment strategies for sustainable AI
- 7.11 Regulatory, ethical, and environmental considerations in sustainable neurodiagnostics
- 7.12 Future trends and research roadmap in sustainable medical AI
- 7.13 Sustainable data strategies in medical imaging
- 7.14 Life cycle assessment of AI models in medical imaging
- 7.15 Responsible governance and green benchmarking in medical AI
- 7.16 Discussion
- 7.17 Conclusion
- References
- 8 Sustainability with artificial intelligence: obstacles, opportunities, and research agenda
- 8.1 Introduction
- 8.2 Literature review
- 8.2.1 Key topics in studies regarding AI for sustainability
- 8.2.2 AI for sustainability
- 8.2.2.1 Ecological sustainability
- 8.2.2.2 Smart cities and AI
- 8.3 Challenges in researching AI for sustainability
- 8.3.1 Dependency on ML
- 8.3.2 Human behavioral reactions
- 8.3.3 Risks to cybersecurity
- 8.3.4 AI's adverse effects
- 8.4 Research roadmap
- 8.4.1 Multilevel AI perspective
- 8.4.2 A perspective of system dynamics
- 8.4.3 A strategy related to design thinking
- 8.5 Real-world applications
- 8.5.1 Enhanced governance of the environment
- 8.5.2 Enhanced environmental performance in industry
- 8.5.3 Safety and lowering environmental hazards
- 8.5.4 Enhanced sensemaking inside the organization
- 8.6 Conclusion
- References
- 9 Smart solutions to a sustainable future
- 9.1 Introduction
- 9.2 Foundations of sustainability
- 9.2.1 Sustainability principles on key issues
- 9.2.2 Environmental, economic, and social dimensions
- 9.2.3 The role of computers in sustainability
- 9.3 Technological innovations for a sustainable future
- 9.3.1 Smart energy solutions
- 9.3.2 Sustainable agriculture and food systems
- 9.3.3 The role of AI and computational intelligence
- 9.4 Mathematical computing for smart sustainability
- 9.4.1 Applications of mathematical computing for sustainability
- 9.4.2 Advanced techniques in mathematical computing
- 9.5 Computational intelligence in sustainable solutions
- 9.6 Challenges and ethical considerations
- 9.6.1 Challenges in implementing sustainable solutions
- 9.6.2 Ethical considerations
- 9.6.3 Addressing challenges and ethical concerns
- 9.7 Future directions in smart sustainability
- 9.8 Conclusion
- References
- 10 Ethical issues in intelligent systems for sustainability
- 10.1 Introduction
- 10.1.1 Importance of ethics
- 10.2 Defining the ethical landscape of intelligent systems
- 10.2.1 Ethical principles in technology
- 10.2.2 Ethical dimensions of sustainability
- 10.2.3 The potential for unintended consequences in sustainability-oriented AI systems
- 10.3 Case studies: ethical dilemmas in sustainability-focused intelligent systems
- 10.3.1 Environmental systems
- 10.3.2 Social equity
- 10.3.3 Resource allocation systems
- 10.4 Regulatory and policy frameworks
- 10.4.1 Existing frameworks
- 10.4.2 Gaps and limitations
- Emerging ethical concerns not yet addressed by policymakers
- 10.4.3 Need for sustainability-specific guidelines
- 10.5 Methods for addressing ethical issues in intelligent systems
- 10.5.1 Ethical AI design approaches
- 10.5.2 Stakeholder involvement
- 10.5.3 Monitoring and accountability mechanisms
- 10.6 Future directions and challenges
- References
- 11 CPS: cyber-physical system security for the Industrial Internet of Things in smart grid
- 11.1 Introduction
- 11.2 Background
- 11.2.1 Cyber-physical system
- 11.2.2 Cyber-physical system communication standard and protocols
- 11.2.3 CPS compatibility with Industry 4.0
- 11.2.4 Security in CPS
- 11.3 Related works
- 11.4 Problems in security control of the system
- 11.4.1 Safety sensing
- 11.4.2 Cyber security
- 11.4.3 Security control
- 11.4.4 Issues and challenges of cyber-physical systems security
- 11.5 Methods and discussion
- 11.5.1 Simulation and configuration
- 11.5.2 Discussion
- 11.6 Conclusion
- References
- 12 Integrating mathematical computing and deep learning for efficient monkeypox skin lesion detection: A pathway to sustainable health solutions
- 12.1 Introduction
- 12.2 DL techniques
- 12.2.1 Neural network techniques
- 12.2.1.1 Feedforward neural network (FNN)
- 12.2.1.2 Examples of real-world FNN applications
- 12.2.1.3 Structure of FNN
- 12.2.1.3.1 Activation function: mathematical computation
- 12.2.1.3.2 Training of FNN
- 12.2.1.4 Convolutional neural networks
- 12.2.1.4.1 Structure of CNN
- 12.2.1.4.2 Training of CNN
- 12.3 DL in monkeypox
- 12.3.1 Literature review
- 12.4 Challenges and limitations
- 12.5 Discussion
- 12.6 Conclusion
- References
- 13 Leveraging heuristics based on CK Metrics Suite for quality enhancement in sustainable quantum software development
- 13.1 Introduction
- 13.2 Background study
- 13.3 Proposed heuristics for object-oriented metrics
- 13.4 Bibliometric analysis
- 13.5 Conclusion
- References
- 14 Intelligent systems for fire management and sustainability
- 14.1 Introduction
- 14.1.1 Contextual overview
- 14.1.1.1 Purpose and scope
- 14.1.2 The role of intelligent systems in fire management
- 14.1.2.1 Fire detection and monitoring
- 14.1.2.2 Predictive analytics for fire behavior
- 14.1.2.3 Advanced deep learning applications in fire management
- 14.1.2.3.1 CNN for fire detection
- 14.1.2.3.2 LSTM for fire spread prediction
- 14.1.3 Sustainability considerations in fire management
- 14.1.3.1 Ecological impact assessments
- 14.1.3.2 Resource conservation
- 14.1.4 Responsible considerations in fire management
- 14.1.4.1 Data privacy and ethical surveillance
- 14.1.4.2 Inclusivity in fire management strategies
- 14.1.5 Case studies in intelligent fire management and sustainability
- 14.1.5.1 Successful implementations
- 14.1.5.1.1 Integration of deep learning in Australia's bushfire monitoring system
- 14.1.5.1.2 Fire management in the Amazon rainforests using AI and remote sensing
- 14.1.5.2 Lessons from failures
- 14.1.6 Framework for responsible fire management using intelligent systems
- 14.1.6.1 Best practices for implementation
- 14.1.6.2 Engaging stakeholders
- 14.1.7 Future directions and recommendations
- 14.1.7.1 Innovations in fire management technologies
- 14.1.7.2 Policy recommendations
- 14.1.8 Conclusions
- References
- 15 Transparent and sustainable AI for brain tumor detection: from conventional to hybrid models in predictive healthcare
- 15.1 Introduction
- 15.2 Conventional machine learning approaches
- 15.2.1 Feature extraction techniques
- 15.2.2 Classical machine learning classifiers
- 15.2.3 Advantages and limitations
- 15.2.4 Applications and relevance in modern pipelines
- 15.3 Deep learning approaches
- 15.3.1 Convolutional neural networks
- 15.3.2 Transfer learning in brain tumor detection
- 15.3.3 Autoencoders for feature learning
- 15.3.4 U-Net for brain tumor segmentation
- 15.3.5 Attention mechanisms in medical imaging
- 15.3.6 Advantages and limitations
- 15.3.7 Applications and relevance in modern pipelines
- 15.4 Hybrid AI models for brain tumor detection
- 15.4.1 Motivation and theoretical basis
- 15.4.2 Description of common architectures and workflow
- 15.4.3 Performance evaluation and case studies
- 15.4.4 Sustainability and clinical relevance
- 15.4.5 Advantages and limitations
- 15.4.6 Applications
- 15.4.7 Conclusion and future directions
- 15.5 Explainable AI (XAI) techniques for brain tumor detection
- 15.5.1 Importance of explainability in medical imaging
- 15.5.2 Common XAI techniques
- 15.5.3 Gradient-weighted class activation mapping (Grad-CAM)
- 15.5.4 Local interpretable model-agnostic explanations (LIME)
- 15.5.5 SHapley additive exPlanations (SHAP)
- 15.5.6 Layer-wise relevance propagation (LRP)
- 15.5.7 Comparison of XAI techniques
- 15.5.8 Advantages and limitations
- 15.5.9 Applications and relevance in modern pipelines
- 15.6 Sustainability considerations in AI for healthcare
- 15.6.1 Environmental sustainability
- 15.6.2 Economic sustainability
- 15.6.3 Social and ethical sustainability
- 15.6.4 Sustainable deployment and lifelong learning
- 15.6.5 Sustainability through explainability
- 15.7 Conclusion
- References
- Index
- Mathematical Methods in the Digital Age
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