Mathematical Computing and Sustainability : Predictive Modeling and Ramifications of Intelligent Systems.

Format:

Book

Author/Creator:

Rani, Shalli.

Series:

Mathematical Methods in the Digital Age Series

Language:

English

Subjects (All):

Artificial intelligence.

Sustainability.

Physical Description:

1 online resource (372 pages)

Edition:

1st ed.

Place of Publication:

Berlin/Boston : Walter de Gruyter GmbH, 2025.

Summary:

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.

Contents:

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

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

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

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

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

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.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

3-11-161203-1

OCLC:

1536152030