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Artificial Intelligence-Driven Models for Environmental Management
| Artificial Intelligence-Driven Models for Environmental Management |
| Autore | Kulkarni Shrikaant |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (349 pages) |
| Disciplina | 363.7363028563 |
| Soggetto topico |
Environmental monitoring - Technological innovations
Pollution - Measurement - Technological innovations Artificial intelligence - Scientific applications |
| ISBN | 9781394282548 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Part I Foundations of AI in Environmental Management -- Chapter 1 Application of AI in Environmental Sustainability -- 1.1 Introduction -- 1.1.1 Importance of AI in Addressing Environmental Challenges -- 1.2 AI Applications in Environmental Monitoring -- 1.2.1 Remote Sensing and Satellite Imaging -- 1.2.2 IoT Sensors and Data Collection -- 1.2.3 Predictive Analytics for Environmental Health -- 1.2.4 Real-Time Monitoring of Air and Water Quality -- 1.3 AI in Climate Change Mitigation -- 1.3.1 Predicting and Analyzing Climate Trends -- 1.3.2 AI-Driven Carbon Footprint Reduction Strategies -- 1.3.3 Renewable Energy Optimization Through AI -- 1.3.4 AI in Forest Conservation and Reforestation -- 1.4 AI in Resource Management -- 1.4.1 Sustainable Agriculture and AI-Assisted Precision Farming -- 1.4.2 AI in Water Resource Management and Conservation -- 1.4.3 Waste Management and Recycling Optimization -- 1.4.4 Circular Economy and Resource Efficiency -- 1.5 AI in Biodiversity Conservation -- 1.5.1 Wildlife Monitoring and Poaching Prevention -- 1.5.2 AI-Assisted Habitat Restoration -- 1.5.3 Species Identification and Population Tracking -- 1.5.4 Marine Ecosystem Management Through AI -- 1.6 AI in Sustainable Urban Planning -- 1.6.1 Smart Cities and Sustainable Infrastructure -- 1.6.2 AI in Reducing Urban Energy Consumption -- 1.6.3 Optimizing Urban Traffic for Reduced Emissions -- 1.6.4 AI-Enabled Green Building Design -- 1.7 Ethical and Governance Considerations -- 1.7.1 Ethical Implications of AI in Environmental Management -- 1.7.2 AI and Environmental Justice -- 1.7.3 Regulatory Frameworks for AI in Sustainability -- 1.7.4 Data Privacy and Security in Environmental AI Applications -- 1.7.5 Case Study -- 1.7.5.1 Background -- 1.7.5.2 Conclusion.
1.8 Challenges and Future Prospects -- 1.8.1 Technological and Resource Limitations -- 1.8.2 Potential Risks and Unintended Consequences -- 1.8.3 AI's Role in Achieving Global Sustainability Goals -- 1.8.4 Future Directions in AI for Environmental Sustainability -- 1.9 Conclusion -- References -- Chapter 2 The Role of AI in Environmental Research and Sustainability -- 2.1 Introduction -- 2.1.1 Overview of AI in Environmental Research -- 2.1.2 Importance of AI in Sustainability Efforts -- 2.1.3 Scope and Objectives of the Study -- 2.2 AI Applications in Environmental Monitoring -- 2.2.1 Remote Sensing and Satellite Imaging -- 2.2.2 AI for Climate Modeling and Forecasting -- 2.2.3 Real-Time Environmental Data Collection -- 2.3 AI in Natural Resource Management -- 2.3.1 Optimizing Water and Energy Use -- 2.3.2 Smart Agriculture and Precision Farming -- 2.3.3 AI for Sustainable Fisheries and Forest Management -- 2.4 AI for Biodiversity and Ecosystem Conservation -- 2.4.1 AI-Powered Species Identification and Tracking -- 2.4.2 Monitoring and Protecting Endangered Species -- 2.4.3 Predictive Analytics in Habitat Restoration -- 2.5 AI in Urban Sustainability -- 2.5.1 AI in Smart Cities and Sustainable Urban Planning -- 2.5.2 Optimizing Transportation and Energy Grids -- 2.5.3 Waste Management and Recycling Innovations -- 2.6 Reducing Environmental Footprints with AI -- 2.6.1 AI for Energy Efficiency in Industries -- 2.6.2 AI and Carbon Emissions Reduction -- 2.6.3 AI in the Circular Economy and Waste Reduction -- 2.7 Ethical Considerations in AI-Driven Environmental Research -- 2.7.1 AI Ethics and Environmental Justice -- 2.7.2 Data Privacy and Security in Environmental Monitoring -- 2.7.3 Accountability and Transparency in AI Models -- 2.8 Case Study -- 2.8.1 Background -- 2.8.2 AI Implementation -- 2.8.3 Quantitative Analysis. 2.8.4 Challenges and Opportunities -- 2.9 Conclusion -- References -- Chapter 3 AI and Environmental Data Science -- 3.1 Introduction -- 3.1.1 Background of AI in Environmental Science -- 3.1.2 Importance of Data Science in Environmental Studies -- 3.1.3 Objectives of the Study -- 3.2 Fundamentals of Artificial Intelligence -- 3.2.1 Overview of AI Techniques -- 3.2.2 Machine Learning vs. Traditional Approaches -- 3.2.3 Deep Learning and its Applications -- 3.3 Environmental Data Science -- 3.3.1 Definition and Scope -- 3.3.2 Types of Environmental Data -- 3.3.2.1 Satellite Imagery -- 3.3.2.2 Sensor Data -- 3.3.2.3 Climate and Weather Data -- 3.3.3 Data Collection and Management -- 3.4 AI Applications in Environmental Science -- 3.4.1 Predictive Modeling of Climate Change -- 3.4.2 Ecosystem Monitoring and Assessment -- 3.4.3 Biodiversity Conservation Efforts -- 3.4.4 Pollution Detection and Management -- 3.5 Case Studies -- 3.5.1 AI in Climate Resilience Planning -- 3.5.1.1 Case Study: City of San Francisco's Climate Resilience Strategy -- 3.5.2 Machine Learning for Wildlife Conservation -- 3.5.2.1 Case Study: African Wildlife Foundation's (AWF) Anti-poaching Initiative -- 3.5.3 Applications in Water Quality Monitoring -- 3.5.3.1 Case Study: The United Nations' "Water Quality and Ecosystems" Project -- 3.6 Challenges and Limitations -- 3.6.1 Data Quality and Availability -- 3.6.2 Interpretability of AI Models -- 3.6.3 Ethical Considerations -- 3.7 Case Study -- 3.7.1 Objective -- 3.7.2 Data Collection and AI Model Deployment -- 3.7.3 Results and Quantitative Analysis -- 3.7.4 Discussion -- 3.7.5 Challenges and Limitations -- 3.8 Future Directions -- 3.8.1 Emerging Trends in AI and Environmental Science -- 3.8.2 Integrating AI with Traditional Environmental Practices -- 3.8.3 Policy Implications and Recommendations -- 3.9 Conclusion -- References. Part II AI in Natural Resource Management -- Chapter 4 Application of AI for Natural Source Management -- 4.1 Introduction -- 4.1.1 Importance of Natural Resource Management -- 4.1.2 Role of AI in Enhancing Resource Management -- 4.2 AI Technologies in NRM -- 4.2.1 Machine Learning Applications -- 4.2.2 Remote Sensing and Data Analysis -- 4.2.3 Predictive Analytics for Resource Forecasting -- 4.2.4 Geographic Information Systems (GIS) -- 4.3 Applications of AI in Specific Natural Resource Sectors -- 4.3.1 Water Resource Management -- 4.3.2 Forest Management and Conservation -- 4.3.3 Biodiversity Monitoring and Conservation -- 4.3.4 Agriculture and Land Use Optimization -- 4.4 Case Studies -- 4.4.1 AI in Water Quality Monitoring -- 4.4.2 Machine Learning for Forest Fire Prediction -- 4.4.3 AI-Driven Biodiversity Assessment -- 4.4.4 Smart Agriculture Solutions -- 4.5 Challenges and Limitations -- 4.5.1 Data Quality and Availability -- 4.5.2 Ethical Considerations -- 4.5.3 Implementation Barriers -- 4.5.4 Need for Interdisciplinary Collaboration -- 4.6 Future Directions -- 4.6.1 Innovations in AI Technologies -- 4.6.2 Enhancing Policy Frameworks -- 4.6.3 Public Engagement and Awareness -- 4.6.4 Integration of AI with Other Technologies -- 4.7 Case Study: Application of AI in NRM -- 4.7.1 Introduction -- 4.7.2 Objective -- 4.7.3 Approach -- 4.7.4 Results -- 4.7.4.1 Region A (Water Resource Management) -- 4.7.5 Discussion -- 4.7.6 Key Takeaways -- 4.7.7 Conclusion -- 4.7.8 Future Work -- References -- Chapter 5 Future Prospects of AI for Management of Natural Resources -- 5.1 Introduction -- 5.1.1 Importance of AI in Natural Resource Management -- 5.1.2 Objectives of the Study -- 5.2 Overview of AI Technologies -- 5.2.1 Machine Learning -- 5.2.2 Predictive Analytics -- 5.2.3 Real-Time Data Collection -- 5.2.4 Case Studies of AI Applications. 5.3 AI in Water Management -- 5.3.1 Water Resource Allocation -- 5.3.2 Predicting Water Demand -- 5.3.3 Monitoring Water Quality -- 5.4 AI in Forestry -- 5.4.1 Forest Inventory and Monitoring -- 5.4.2 Predictive Modeling for Forest Health -- 5.4.3 Enhancing Reforestation Efforts -- 5.5 AI in Agriculture -- 5.5.1 Precision Agriculture -- 5.5.2 Crop Yield Prediction -- 5.5.3 Pest and Disease Management -- 5.6 AI in Biodiversity Conservation -- 5.6.1 Species Monitoring -- 5.6.2 Habitat Assessment -- 5.6.3 Predictive Conservation Planning -- 5.7 Challenges and Barriers to AI Implementation -- 5.7.1 Data Privacy Concerns -- 5.7.2 Ethical Considerations -- 5.7.3 The Digital Divide -- 5.8 Case Study -- 5.8.1 Objectives of the Case Study -- 5.8.2 Methodology -- 5.8.3 Quantitative Analysis -- 5.9 Conclusion -- References -- Part III AI Models for Climate Change Mitigation and Adaptation -- Chapter 6 AI in Climate Change Prediction -- 6.1 Introduction -- 6.1.1 Role of AI in Climate Science -- 6.1.2 How AI Enhances Climate Change Prediction -- 6.1.3 Real-World Applications of AI in Climate Prediction -- 6.1.4 AI and Climate Mitigation -- 6.1.5 Challenges and Limitations of AI in Climate Prediction -- 6.2 AI Technologies in Climate Prediction -- 6.2.1 Machine Learning for Climate Data Analysis -- 6.2.2 Deep Learning in Climate Models -- 6.2.3 AI-Powered Satellite Imagery Analysis -- 6.2.4 AI in Weather Forecasting and Extreme Event Prediction -- 6.3 AI Applications in Climate Science -- 6.3.1 Predicting Extreme Weather Events -- 6.3.2 Long-Term Climate Projections -- 6.3.3 AI in Ocean and Polar Ice Monitoring -- 6.3.4 AI in Air Quality and Pollution Forecasting -- 6.4 AI for Climate Mitigation and Adaptation -- 6.4.1 Optimizing Energy Consumption and Emission Reduction -- 6.4.2 AI in Renewable Energy Integration -- 6.4.3 AI in Smart Grids and Infrastructure. 6.4.4 AI for Carbon Sequestration and Natural Resource Management. |
| Record Nr. | UNINA-9911018886203321 |
Kulkarni Shrikaant
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biosystems, Biomedical & Drug Delivery Systems : Characterization, Restoration and Optimization / / edited by Shrikaant Kulkarni, A. K. Haghi, Sonali Manwatkar
| Biosystems, Biomedical & Drug Delivery Systems : Characterization, Restoration and Optimization / / edited by Shrikaant Kulkarni, A. K. Haghi, Sonali Manwatkar |
| Autore | Kulkarni Shrikaant |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (376 pages) |
| Disciplina | 571.4 |
| Altri autori (Persone) |
HaghiA. K
ManwatkarSonali |
| Soggetto topico |
Biophysics
Imaging systems in biology Biomaterials Pharmacology Bioanalysis and Bioimaging Biological Imaging Biomedical Materials |
| ISBN |
9789819725960
9819725968 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Editorial: Future of Novel Technologies in Biosystems, Biomedicine, and Drug Delivery -- Part I; Novel Technologies in Biosystems, Biomedicine, and Drug Delivery: Characterization -- 2. Characterization Tools for Current Drug Delivery Systems -- 3. Characterization of Transdermal Drug Delivery Systems: Retrospect and Future Prospects -- 4. Analytical Tools for the Characterization of Nasal Spray Drug Products -- Part II: Novel Technologies in Biosystems, Biomedicine, and Drug Delivery: Restoration -- 5. AI-Enabled Models in the Restoration of Drug Efficacy and Drug Design -- 6. Restoration and Sustenance of Nano drug delivery systems: potential, Challenges, and limitations -- 7. Artificial Intelligence and Machine Learning in Restoring and Strengthening HealthCare -- Part III: Novel Technologies in Biosystems, Biomedicine, and Drug Delivery: Optimization -- 8. Optimizing Oncology Tools: Organ-on-a-clip alternative to animal model -- 9. Optimizing Drug Synthesis: AI-Powered Kinetics study in Pharmaceutical Research -- 10. In Silico Toxicological Protocols Optimization for the Prediction of Toxicity of Drugs -- 11. Optimizing Healthcare Throughput: The Role of Machine Learning and Data Analytics -- Part IV: Novel Technologies in Biosystems, Biomedicine, and Drug Delivery: Applications -- 12. Applications of AI-based Models in the field of Biomedicine -- 13. Application of New Biological Entities (NBEs) as Therapeutics -- 14. Applications of Computational Tools in the Prediction of Toxicity -- 15. Application of Peptides for the treatment of diabetes: A plant-based bioactive material -- 16. Regenerative Medicines: Application to Degenerative Diseases and Disorders. |
| Record Nr. | UNINA-9910865242803321 |
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Kulkarni Shrikaant
|
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Global Sustainability : Trends, Challenges & Case Studies / / edited by Shrikaant Kulkarni, A. K. Haghi
| Global Sustainability : Trends, Challenges & Case Studies / / edited by Shrikaant Kulkarni, A. K. Haghi |
| Autore | Kulkarni Shrikaant |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (299 pages) |
| Disciplina | 304.2 |
| Altri autori (Persone) | HaghiA. K |
| Collana | World Sustainability Series |
| Soggetto topico |
Sustainability
Environmental management Ecology Environmental Management |
| ISBN | 3-031-57456-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1.Editorial: Global Sustainability: Trends, Challenges, and Case studies -- Part I. Global Sustainability: Trends -- Chapter 2. Emerging Trends in Sustainability: A Conceptual Exploration (Anitha. K) -- Chapter 3. Sustainable Solutions to Combat Soil Erosion Using Biogenic Agents (Evangelin Ramani Sujatha) -- Chapter 4. Circular Economy for Sustainable Development in India (Vijai) -- Chapter 5. Evaluation of Sustainability Practices in Higher Education: A Study on Assessment Tools GASU and AISHE (Parvez Ahmad) -- Part II. Global Sustainability: Challenges -- Chapter 6. Soil Conservation for Global Sustainability (Prasann Kumar) -- Chapter 7. Designing a Framework for Sustainable Supply Chain Management of Coal Transportation (Suchismita Satapathy) -- Chapter 8. Workplace Wellbeing of LGBT Individuals: Impact on Sustainability (Vaishnavi Nambiar) -- Part III. Global Sustainability: Case Studies -- Chapter 9. Impact of Urban Expansion onUrban Heat: A Case Study of Greater London (Semudara, Oluwaseun Moses) -- Chapter 10. Sustainable Cassava: A Case Study of Global Sustainability (Shrikaant Kulkarni) -- Chapter 11. Case Studies in Sustainable Business Management in India (Amit Kumar Marwah) -- Chapter 12. Role of Agricultural Science Centres in attaining Sustainability in India: A Case Study (A. K. Wavare) -- Chapter 13. Collective Action for Transformative Change: The Case of Helston Climate Action Group (UK) (Kolade Victor Otokiti). |
| Record Nr. | UNINA-9910864200903321 |
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Kulkarni Shrikaant
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||