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Submit a ProposalArtificial Intelligence and Biodiversity
One Line Description
Harness the power of the digital frontier to save our planet with this essential guide, which demonstrates how deep learning, genetic engineering, and AI-based robotics can be integrated to track biodiversity, restore genetic diversity, and rebuild fragile ecosystems with unprecedented precision.
Audience
Professors, researchers, students, AI and machine learning experts, wildlife and environmental conservationists, and policymakers working in areas like conservation biology, ecology, and ecosystem management who are looking for ways to incorporate advanced technology into their work.
Professors, researchers, students, AI and machine learning experts, wildlife and environmental conservationists, and policymakers working in areas like conservation biology, ecology, and ecosystem management who are looking for ways to incorporate advanced technology into their work.
Description
From satellite imagery to genetic sequencing, AI is helping researchers track biodiversity, predict ecosystem changes, and monitor endangered species with unprecedented precision. This book delves into the exciting ways that artificial intelligence (AI), particularly deep learning, is being used to analyze complex ecological data. It offers an in-depth look at how these AI-driven technologies are transforming how we approach biodiversity conservation on a global scale, examining the role of genetic engineering, guided by AI, in restoring genetic diversity and helping species adapt to rapidly changing environments. Additionally, the book highlights how AI is revolutionizing ecosystem restoration, using AI-based robotics and reinforcement learning to restore habitats such as forests, wetlands, and coral reefs. It looks at real-world applications where AI systems are actively being used to rebuild damaged ecosystems, suggesting new ways to restore balance to nature. Through a combination of practical case studies and theoretical insights, this guide serves as an essential resource for anyone interested in the future of conservation, whether you are an AI specialist, an environmental scientist, or simply someone passionate about protecting the planet. By blending the latest in AI research with real-world biodiversity challenges, this book paints a picture of a future where technology and nature work hand in hand to safeguard life on Earth.
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From satellite imagery to genetic sequencing, AI is helping researchers track biodiversity, predict ecosystem changes, and monitor endangered species with unprecedented precision. This book delves into the exciting ways that artificial intelligence (AI), particularly deep learning, is being used to analyze complex ecological data. It offers an in-depth look at how these AI-driven technologies are transforming how we approach biodiversity conservation on a global scale, examining the role of genetic engineering, guided by AI, in restoring genetic diversity and helping species adapt to rapidly changing environments. Additionally, the book highlights how AI is revolutionizing ecosystem restoration, using AI-based robotics and reinforcement learning to restore habitats such as forests, wetlands, and coral reefs. It looks at real-world applications where AI systems are actively being used to rebuild damaged ecosystems, suggesting new ways to restore balance to nature. Through a combination of practical case studies and theoretical insights, this guide serves as an essential resource for anyone interested in the future of conservation, whether you are an AI specialist, an environmental scientist, or simply someone passionate about protecting the planet. By blending the latest in AI research with real-world biodiversity challenges, this book paints a picture of a future where technology and nature work hand in hand to safeguard life on Earth.
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Author / Editor Details
Umesh Kumar Lilhore, PhD is a Professor in the Department of Computer Science and Engineering at Galgotias University. He has authored ten books, more than 100 research articles in journals, and filed 50 patents across India and the UK. His research interests include artificial intelligence, machine learning, and software engineering.
Umesh Kumar Lilhore, PhD is a Professor in the Department of Computer Science and Engineering at Galgotias University. He has authored ten books, more than 100 research articles in journals, and filed 50 patents across India and the UK. His research interests include artificial intelligence, machine learning, and software engineering.
Sarita Simaiya, PhD is a Professor of Computer Science and Engineering at Galgotias University with more than 17 years of teaching and research experience. She has published more than 140 articles in international journals and conferences. Her research focuses on machine learning, IoT, and AI.
Surjeet Dalal, PhD is a Professor and researcher with more than 20 years of experience in teaching and research in Computer Science and Engineering. He has published more than 140 articles in international journals and conferences. His research interests include artificial intelligence and cloud computing.
Martin Margala, PhD is the Director of the School of Computing at the University of Louisiana at Lafayette with more than 25 years of experience in teaching and research. He has more than 400 publications to his credit, including in international journal articles and conference proceedings. He specializes in exascale computing, ultra-high frequency design, and design for reliability.
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Table of Contents
Preface
1. Harnessing Artificial Intelligence to Address Global Environmental Challenges: A Cross-Domain Review
Suresh K.S., Dafik, Nagendar Yamsani, Rayappan Lotus, S. Mathumohan and Anurag Singh
1.1 Introduction
1.2 Related Works
1.3 Methodology
1.4 Results
1.5 Discussions
1.6 Conclusion
References
2. Innovative AI Paradigms for Achieving Environmental Sustainability: From Concept to Practice
Sudhir Ramadass, R. Sundar, V. Elanangai, Divya Lalita, Banashree Chatterjee and Gayatri Parasa
2.1 Introduction
2.2 Related Works
2.3 Methodology
2.4 Results
2.5 Discussion
2.6 Conclusion
References
3. Deep Learning Approaches for Real-Time Climate Monitoring and Anomaly Detection in Meteorological Systems
Manyam Thaile, Baby Anusha, Nagendar Yamsani, Balakrishnan, Umasree Mariappan and Sunder R.
3.1 Introduction
3.2 Related Works
3.3 Methodology
3.4 Results
3.5 Discussions
3.6 Conclusion
References
4. Federated Learning for Privacy-Preserving Environmental Monitoring Across Distributed Sensor Networks
Kireet Muppavaram, Manyam Thaile, T. Srinivasulu, T. Srikanth, Anita Pradhan and Siva Shankar S.
4.1 Introduction
4.2 Related Works
4.3 Methodology
4.4 Results
4.5 Discussions
4.6 Conclusion
References
5. Quantum AI in Environmental Modeling: Opportunities for Accelerating Ecosystem Simulations
Fathimathul Rajeena P.P., Rahoof P. P. and Sunder R.
5.1 Introduction
5.2 Related Works
5.3 Methodology
5.4 Results
5.5 Discussions
5.6 Conclusion
Acknowledgment
References
6. Developing Digital Twin Ecosystems for Dynamic Environmental Analysis and Predictive Sustainability Planning
Ann Rija Paul, Amutha. S., M. Sakthivanitha, M. Mohamed Sirajudeen, N. Anandakrishnan and S. Suresh
6.1 Introduction
6.2 Related Works
6.3 Methodology
6.4 Results
6.5 Discussions
6.6 Conclusion
References
7. AI-Driven Optimization of Renewable Energy Systems: Forecasting, Load Balancing, and Grid Efficiency
Raghavendra Kulkarni, P. Manikandaprabhu, Disha Sushant Wankhede, Bura Vijay Kumar, M. Vasuki and Rasmi A.
7.1 Introduction
7.2 Related Works
7.3 Methodology
7.4 Results
7.5 Conclusion
References
8. Intelligent Systems for Pollution Detection and Control: Integrating AI in Urban and Industrial Environments
K. Dhana Sree Devi, Ika Hesti Agustin, Talluri Lakshmi Siva Rama Krishna, Bura Vijay Kumar, Rishabh Garg and K. Kaliraj
8.1 Introduction
8.2 Related Works
8.3 Methodology
8.4 Results
8.5 Discussions
8.6 Conclusion
References
9. Smart Agriculture Using AI: Enhancing Crop Yield, Soil Health, and Resource Efficiency
M. Vamsikrishna, Tholkapiyan M., Divya Kumari Tankala, Gotte Ranjith Kumar, Sandeep Kaur and P. Eswaran
9.1 Introduction
9.2 Related Works
9.3 Methodology
9.4 Results
9.5 Discussions
9.6 Conclusion
References
10. Preserving Biodiversity through AI: Automated Species Monitoring and Habitat Conservation Strategies
Eshwar Dara, Bui Thanh Hung, Rayappan Lotus, Gotte Ranjith Kumar, C. Parameswari and Rajakumar Perumal
10.1 Introduction
10.2 Related Works
10.3 Methodology
10.4 Results
10.5 Conclusion
References
11. Explainable AI in Environmental Decision-Making: Enhancing Trust and Transparency in Sustainability Models
Sreejith R., Kapil Aggarwal, Nagendar Yamsani, T. Amalraj Victoire, G. Susan Shiny and Rasmi A.
11.1 Introduction
11.2 Related Works
11.3 Methodology
11.4 Results
11.5 Discussions and Implications
11.6 Conclusion
References
12. Ethical Implications of AI in Environmental Policy Formulation: Balancing Innovation and Responsibility
Muralidhar Vejendla, Nor Asilah Wati Abdul Hamid, P. Jyothi, Kanegonda Ravi Chythanya, Sudheer S. Marar and Umesh Kumar Lihore
12.1 Introduction
12.2 Related Works
12.3 Methodology
12.4 Results
12.5 Discussions
12.6 Conclusion
References
13. Shaping a Sustainable Future: The Role of AI in Driving Green Innovation and Environmental Equity
Madhura S., P. Sridhar, R. Karthikeyan, Patil Mounica, R. Archana Reddy and Umesh Kumar Lihore
13.1 Introduction
13.2 Related Works
13.3 The Role of AI in Driving Green Innovation
13.4 The Role of AI in Environmental Equity
13.5 Discussions
13.6 Conclusion
References
14. Decades of Transformation: Predictive Analysis of Land Use Changes in Dhanbad Using Deep Learning and Remote Sensing
A. Anitha and Nikhil Raj
14.1 Introduction
14.2 Related Work
14.3 Background Fundamental
14.4 Proposed Work
14.4.1 Research Methodology
14.5 Experiment, Analysis and Result
14.5.1 Long Short-Term Memory Trends
14.5.2 Bi-Directional Long Short-Term Memory Trends
14.5.3 Error Metrics
14.6 Conclusion
References
15. AI for Biodiversity and Ecosystem Conservation
Hina Hashmi, Aman Kumar and Danish Raza Rizvi
15.1 Introduction
15.1.1 Importance of Biodiversity and Ecosystem Conservation
15.1.2 Role of AI in Environmental Sustainability
15.1.3 Challenges in Traditional Conservation Methods and How AI Addresses Them
15.2 AI for Wildlife Monitoring and Anti-Poaching Efforts
15.2.1 Computer Vision for Species Identification
15.2.2 Acoustic Monitoring with AI
15.2.3 AI for Anti-Poaching Strategies
15.3 Deep Learning for Habitat Protection and Restoration
15.3.1 Remote Sensing for Deforestation and Land Use Changes
15.3.2 Ecosystem Health Monitoring with AI
15.3.3 Restoration Planning with AI
15.4 AI for Marine Conservation and Ocean Monitoring
15.4.1 Automated Marine Life Detection
15.4.2 Predicting and Preventing Ocean Pollution
15.4.3 AI for Sustainable Fisheries Management
15.5 AI in Climate Change and Biodiversity Interactions
15.5.1 Predicting Species Migration Patterns Due to Climate Change
15.5.2 AI Models for Tracking Climate-Induced Biodiversity Loss
15.5.3 Machine Learning for Assessing the Impact of Global Warming on Ecosystems
15.6 Challenges and Ethical Considerations in AI for Conservation
15.6.1 Data Limitations and Biases in AI-Based Conservation Models
15.6.2 Ethical Concerns in Wildlife Surveillance and Data Privacy
15.6.3 Ensuring AI Solutions Align with indigenous and Local Conservation Efforts
15.7 Future Directions and Emerging Technologies
15.8 Conclusion
References
16. Pneumonia Detection in Chest Based on Respiratory Variability Using Deep Learning
Ritu Aggarwal and Eshaan Aggarwal
16.1 Introduction
16.2 Related Work
16.3 Proposed Methodology
16.3.1 Proposed Flow Work
16.3.2 Learning Process through Model
16.3.2.1 Pre-Processing Stage
16.3.2.2 Feature-Extraction Stage
16.3.2.3 DenseNet121
16.3.2.4 VGG-16
16.4 Evaluation Metrics
16.5 Results and Discussion
Conclusion
References
17. Integrated Optimization Strategies for High-Efficiency Solar PV Plants: From AI to Bifacial Technologies
S. Dayana Priyadharshini and M. Arvindhan
17.1 Introduction
17.1.1 Classical Optimization Methods
17.2 Hybrid and Multi-Objective Optimization
17.2.1 Artificial Intelligence and Machine Learning
17.2.2 Explainable AI (XAI)
17.3 Technology Trends in Indian Solar PV Plants
17.3.1 Optimization Techniques in Renewable Energy
17.3.2 Hybrid Renewable Energy Systems
17.3.3 Advances in High-Efficiency PV Modules
17.3.4 TOPC on and Tandem Technologies
17.4 System-Level Design and Solar Tracking
17.4.1 Radiative and Passive Cooling
17.4.2 Optical Coatings and Spectrum Management
17.5 Power Electronics and MPPT Optimization
17.5.1 Advanced MPPT Algorithms
17.5.2 String-Level and Module-Level Optimization
17.6 Digitalization, AI, and Smart Monitoring
17.6.1 AI-Driven AI&M Platforms
17.6.2 Key Innovative Approaches Include
17.7 Case Studies Illustrating the Impact of Optimization
17.8 Emerging Trends and Future Directions
17.8.1 Solar Forecasting and Grid Integration
17.8.2 Environmental Adaptation and Dual-Use Land Strategies
17.8.2.1 Agrivoltaics
17.8.2.2 Floating Solar (Floatovoltaics)
17.8.2.3 Advanced System Integration
17.8.2.4 Smart Grids and Digitalization
17.9 Lifecycle Considerations and Sustainability
17.9.1 Material Longevity and Recycling
17.9.2 Reducing Embodied Carbon
17.9.3 Role of IoT in Enhancing Renewable Energy Efficiency
17.10 Future Directions
17.10.1 Scalable AI and Digital Platforms
17.10.2 Advanced Energy Storage and Sector Coupling
17.10.3 Integrated Planning and Policy Support
17.10.4 Resilience and Sustainability
17.11 Conclusion
Bibliography
Index
Back to Top
Preface
1. Harnessing Artificial Intelligence to Address Global Environmental Challenges: A Cross-Domain Review
Suresh K.S., Dafik, Nagendar Yamsani, Rayappan Lotus, S. Mathumohan and Anurag Singh
1.1 Introduction
1.2 Related Works
1.3 Methodology
1.4 Results
1.5 Discussions
1.6 Conclusion
References
2. Innovative AI Paradigms for Achieving Environmental Sustainability: From Concept to Practice
Sudhir Ramadass, R. Sundar, V. Elanangai, Divya Lalita, Banashree Chatterjee and Gayatri Parasa
2.1 Introduction
2.2 Related Works
2.3 Methodology
2.4 Results
2.5 Discussion
2.6 Conclusion
References
3. Deep Learning Approaches for Real-Time Climate Monitoring and Anomaly Detection in Meteorological Systems
Manyam Thaile, Baby Anusha, Nagendar Yamsani, Balakrishnan, Umasree Mariappan and Sunder R.
3.1 Introduction
3.2 Related Works
3.3 Methodology
3.4 Results
3.5 Discussions
3.6 Conclusion
References
4. Federated Learning for Privacy-Preserving Environmental Monitoring Across Distributed Sensor Networks
Kireet Muppavaram, Manyam Thaile, T. Srinivasulu, T. Srikanth, Anita Pradhan and Siva Shankar S.
4.1 Introduction
4.2 Related Works
4.3 Methodology
4.4 Results
4.5 Discussions
4.6 Conclusion
References
5. Quantum AI in Environmental Modeling: Opportunities for Accelerating Ecosystem Simulations
Fathimathul Rajeena P.P., Rahoof P. P. and Sunder R.
5.1 Introduction
5.2 Related Works
5.3 Methodology
5.4 Results
5.5 Discussions
5.6 Conclusion
Acknowledgment
References
6. Developing Digital Twin Ecosystems for Dynamic Environmental Analysis and Predictive Sustainability Planning
Ann Rija Paul, Amutha. S., M. Sakthivanitha, M. Mohamed Sirajudeen, N. Anandakrishnan and S. Suresh
6.1 Introduction
6.2 Related Works
6.3 Methodology
6.4 Results
6.5 Discussions
6.6 Conclusion
References
7. AI-Driven Optimization of Renewable Energy Systems: Forecasting, Load Balancing, and Grid Efficiency
Raghavendra Kulkarni, P. Manikandaprabhu, Disha Sushant Wankhede, Bura Vijay Kumar, M. Vasuki and Rasmi A.
7.1 Introduction
7.2 Related Works
7.3 Methodology
7.4 Results
7.5 Conclusion
References
8. Intelligent Systems for Pollution Detection and Control: Integrating AI in Urban and Industrial Environments
K. Dhana Sree Devi, Ika Hesti Agustin, Talluri Lakshmi Siva Rama Krishna, Bura Vijay Kumar, Rishabh Garg and K. Kaliraj
8.1 Introduction
8.2 Related Works
8.3 Methodology
8.4 Results
8.5 Discussions
8.6 Conclusion
References
9. Smart Agriculture Using AI: Enhancing Crop Yield, Soil Health, and Resource Efficiency
M. Vamsikrishna, Tholkapiyan M., Divya Kumari Tankala, Gotte Ranjith Kumar, Sandeep Kaur and P. Eswaran
9.1 Introduction
9.2 Related Works
9.3 Methodology
9.4 Results
9.5 Discussions
9.6 Conclusion
References
10. Preserving Biodiversity through AI: Automated Species Monitoring and Habitat Conservation Strategies
Eshwar Dara, Bui Thanh Hung, Rayappan Lotus, Gotte Ranjith Kumar, C. Parameswari and Rajakumar Perumal
10.1 Introduction
10.2 Related Works
10.3 Methodology
10.4 Results
10.5 Conclusion
References
11. Explainable AI in Environmental Decision-Making: Enhancing Trust and Transparency in Sustainability Models
Sreejith R., Kapil Aggarwal, Nagendar Yamsani, T. Amalraj Victoire, G. Susan Shiny and Rasmi A.
11.1 Introduction
11.2 Related Works
11.3 Methodology
11.4 Results
11.5 Discussions and Implications
11.6 Conclusion
References
12. Ethical Implications of AI in Environmental Policy Formulation: Balancing Innovation and Responsibility
Muralidhar Vejendla, Nor Asilah Wati Abdul Hamid, P. Jyothi, Kanegonda Ravi Chythanya, Sudheer S. Marar and Umesh Kumar Lihore
12.1 Introduction
12.2 Related Works
12.3 Methodology
12.4 Results
12.5 Discussions
12.6 Conclusion
References
13. Shaping a Sustainable Future: The Role of AI in Driving Green Innovation and Environmental Equity
Madhura S., P. Sridhar, R. Karthikeyan, Patil Mounica, R. Archana Reddy and Umesh Kumar Lihore
13.1 Introduction
13.2 Related Works
13.3 The Role of AI in Driving Green Innovation
13.4 The Role of AI in Environmental Equity
13.5 Discussions
13.6 Conclusion
References
14. Decades of Transformation: Predictive Analysis of Land Use Changes in Dhanbad Using Deep Learning and Remote Sensing
A. Anitha and Nikhil Raj
14.1 Introduction
14.2 Related Work
14.3 Background Fundamental
14.4 Proposed Work
14.4.1 Research Methodology
14.5 Experiment, Analysis and Result
14.5.1 Long Short-Term Memory Trends
14.5.2 Bi-Directional Long Short-Term Memory Trends
14.5.3 Error Metrics
14.6 Conclusion
References
15. AI for Biodiversity and Ecosystem Conservation
Hina Hashmi, Aman Kumar and Danish Raza Rizvi
15.1 Introduction
15.1.1 Importance of Biodiversity and Ecosystem Conservation
15.1.2 Role of AI in Environmental Sustainability
15.1.3 Challenges in Traditional Conservation Methods and How AI Addresses Them
15.2 AI for Wildlife Monitoring and Anti-Poaching Efforts
15.2.1 Computer Vision for Species Identification
15.2.2 Acoustic Monitoring with AI
15.2.3 AI for Anti-Poaching Strategies
15.3 Deep Learning for Habitat Protection and Restoration
15.3.1 Remote Sensing for Deforestation and Land Use Changes
15.3.2 Ecosystem Health Monitoring with AI
15.3.3 Restoration Planning with AI
15.4 AI for Marine Conservation and Ocean Monitoring
15.4.1 Automated Marine Life Detection
15.4.2 Predicting and Preventing Ocean Pollution
15.4.3 AI for Sustainable Fisheries Management
15.5 AI in Climate Change and Biodiversity Interactions
15.5.1 Predicting Species Migration Patterns Due to Climate Change
15.5.2 AI Models for Tracking Climate-Induced Biodiversity Loss
15.5.3 Machine Learning for Assessing the Impact of Global Warming on Ecosystems
15.6 Challenges and Ethical Considerations in AI for Conservation
15.6.1 Data Limitations and Biases in AI-Based Conservation Models
15.6.2 Ethical Concerns in Wildlife Surveillance and Data Privacy
15.6.3 Ensuring AI Solutions Align with indigenous and Local Conservation Efforts
15.7 Future Directions and Emerging Technologies
15.8 Conclusion
References
16. Pneumonia Detection in Chest Based on Respiratory Variability Using Deep Learning
Ritu Aggarwal and Eshaan Aggarwal
16.1 Introduction
16.2 Related Work
16.3 Proposed Methodology
16.3.1 Proposed Flow Work
16.3.2 Learning Process through Model
16.3.2.1 Pre-Processing Stage
16.3.2.2 Feature-Extraction Stage
16.3.2.3 DenseNet121
16.3.2.4 VGG-16
16.4 Evaluation Metrics
16.5 Results and Discussion
Conclusion
References
17. Integrated Optimization Strategies for High-Efficiency Solar PV Plants: From AI to Bifacial Technologies
S. Dayana Priyadharshini and M. Arvindhan
17.1 Introduction
17.1.1 Classical Optimization Methods
17.2 Hybrid and Multi-Objective Optimization
17.2.1 Artificial Intelligence and Machine Learning
17.2.2 Explainable AI (XAI)
17.3 Technology Trends in Indian Solar PV Plants
17.3.1 Optimization Techniques in Renewable Energy
17.3.2 Hybrid Renewable Energy Systems
17.3.3 Advances in High-Efficiency PV Modules
17.3.4 TOPC on and Tandem Technologies
17.4 System-Level Design and Solar Tracking
17.4.1 Radiative and Passive Cooling
17.4.2 Optical Coatings and Spectrum Management
17.5 Power Electronics and MPPT Optimization
17.5.1 Advanced MPPT Algorithms
17.5.2 String-Level and Module-Level Optimization
17.6 Digitalization, AI, and Smart Monitoring
17.6.1 AI-Driven AI&M Platforms
17.6.2 Key Innovative Approaches Include
17.7 Case Studies Illustrating the Impact of Optimization
17.8 Emerging Trends and Future Directions
17.8.1 Solar Forecasting and Grid Integration
17.8.2 Environmental Adaptation and Dual-Use Land Strategies
17.8.2.1 Agrivoltaics
17.8.2.2 Floating Solar (Floatovoltaics)
17.8.2.3 Advanced System Integration
17.8.2.4 Smart Grids and Digitalization
17.9 Lifecycle Considerations and Sustainability
17.9.1 Material Longevity and Recycling
17.9.2 Reducing Embodied Carbon
17.9.3 Role of IoT in Enhancing Renewable Energy Efficiency
17.10 Future Directions
17.10.1 Scalable AI and Digital Platforms
17.10.2 Advanced Energy Storage and Sector Coupling
17.10.3 Integrated Planning and Policy Support
17.10.4 Resilience and Sustainability
17.11 Conclusion
Bibliography
Index
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