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Artificial Intelligence in Microbial Research Bridging the Gap /

This book explores the convergence of microbiology and artificial intelligence (AI) and delves into the intricate world of microbial systems enhanced by cutting-edge AI technologies. The book begins by establishing a foundation in the fundamentals of microbial ecosystems and AI principles. It elucid...

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Соавтор: SpringerLink (Online service)
Другие авторы: Pandey, Babita (Редактор), Pandey, Devendra (Редактор), Khamparia, Aditya (Редактор), Dutta, Venkatesh (Редактор), Balas, Valentina E. (Редактор)
Формат: Электронный ресурс eКнига
Язык:английский
Опубликовано: Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
Редактирование:1st ed. 2025.
Серии:Microorganisms for Sustainability, 45
Предметы:
Microbial populations.
Microbiology.
Cytology.
Microbial ecology.
Artificial intelligence.
Machine learning.
Artificial intelligence > Data processing.
Microbial Communities.
Cellular Microbiology.
Environmental Microbiology.
Artificial Intelligence.
Machine Learning.
Data Science.
Online-ссылка:https://doi.org/10.1007/978-981-96-3448-4
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Оглавление:
  • Chapter 1. Thematic Analysis of Media Influence on the Adoption of AI Climate Prediction Models in Microbial Agriculture Practices: A Case Study of Uttar Pradesh Using Diffusion of Innovations Theory
  • Chapter 2. Understanding Media Influence on the Adoption of AI Climate Prediction Models in Microbiological Agricultural Practices: A Study of Uttarakhand
  • Chapter 3. Advancements in Precision Agriculture: Integrating Machine Learning Techniques for Crop Monitoring and Management
  • Chapter 4. Advances in Agricultural Analytics Machine Learning Applications for Crop Monitoring and Management
  • Chapter 5. AI Driven Strategies for Microbial Infection from Discovery to Therapeutic Design
  • Chapter 6. Use of Artificial Intelligence for Monitoring Algal Blooms in Aquatic Ecosystem
  • Chapter 7. Ai-Yolact Model for Automatic Severity Grading Of Microbial Based Anthracnose Infection in Camellia Leaves
  • Chapter 8. An Explainable AI Based CNN model for Plant Disease Diagnosis
  • Chapter 9. Artificial Intelligent Enable Intelligent Bio-Sensor for Microbial Analysisfor Lung Health
  • Chapter 10. Biosensors Guided Ai Interventions in Personalized Medicines
  • Chapter 11. Education and Training for Developing Responsible AI Solutions in Healthcare
  • Chapter 12. Automation of Drug Discovery & Development
  • Chapter 13. Genome Studies and Disease Diagnosis
  • Chapter 14. Exploring Explainable Artificial Intelligence in Healthcare: Issues, Challenges and Opportunities
  • Chapter 15. Investigating Integron as the Principal Factor of Antibiotic Resistance in the Human Gut: A Holistic Perspective
  • Chapter 16. Hybrid Deep Learning for Predictive Modelling of Microbial Biostimulants in Precision Agriculture
  • Chapter 17. Challenges and Opportunities In Integrating Generative Al With Wearable Devices
  • Chapter 18. Medical Image Analysis and Morphology Using Artificial Intelligence
  • Chapter 19. Simulation of Biological Structures Using Generative Artificial Intelligence
  • Chapter 20. Neuromuscular Disease Classification: Leveraging Deep Learning Feature Extractors and Applications.

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