LEADER 06401nam 22007815 450 001 9911007364703321 005 20250522130300.0 010 $a981-9634-48-2 024 7 $a10.1007/978-981-96-3448-4 035 $a(CKB)38891309500041 035 $a(DE-He213)978-981-96-3448-4 035 $a(MiAaPQ)EBC32127805 035 $a(Au-PeEL)EBL32127805 035 $a(EXLCZ)9938891309500041 100 $a20250522d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence in Microbial Research $eBridging the Gap /$fedited by Babita Pandey, Devendra Pandey, Aditya Khamparia, Venkatesh Dutta, Valentina E. Balas 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (XVI, 450 p. 104 illus., 94 illus. in color.) 225 1 $aMicroorganisms for Sustainability,$x2512-1898 ;$v45 311 08$a981-9634-47-4 327 $aChapter 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. 330 $aThis book explores the convergence of microbiology and artificial intelligence (AI) and delves into the intricate world of microbial systems enhanced by cutting-edge AI technologies. The book begins by establishing a foundation in the fundamentals of microbial ecosystems and AI principles. It elucidates the integration of AI in microbial genomics, demonstrating how advanced algorithms analyze genomic data and contribute to genetic engineering. Bioinformatics and computational microbiology are explored, showcasing AI's role in predictive modeling and computational tools. The intersection of AI and microbial applications extends to drug discovery, precision agriculture, and pathogen detection. Readers gain insights into AI-driven drug development, the optimization of agricultural practices using microbial biostimulants, and early warning systems for crop diseases. The book highlights AI's role in microbial biotechnology, elucidating its impact on bioprocessing, fermentation, and other biotechnological applications. Climate-smart agriculture and microbial adaptations to environmental challenges are discussed, emphasizing sustainable practices. This book caters to a diverse audience including teachers, researchers, microbiologist, computer bioinformaticians, plant and environmental scientists. The book serves as additional reading material for undergraduate and graduate students of computer science, biomedical, agriculture, human science, forestry, ecology, soil science, and environmental sciences and policy makers to be a useful to read. 410 0$aMicroorganisms for Sustainability,$x2512-1898 ;$v45 606 $aMicrobial populations 606 $aMicrobiology 606 $aCytology 606 $aMicrobial ecology 606 $aArtificial intelligence 606 $aMachine learning 606 $aArtificial intelligence$xData processing 606 $aMicrobial Communities 606 $aCellular Microbiology 606 $aEnvironmental Microbiology 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aData Science 615 0$aMicrobial populations. 615 0$aMicrobiology. 615 0$aCytology. 615 0$aMicrobial ecology. 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aArtificial intelligence$xData processing. 615 14$aMicrobial Communities. 615 24$aCellular Microbiology. 615 24$aEnvironmental Microbiology. 615 24$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aData Science. 676 $a579.1788 702 $aPandey$b Babita$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPandey$b Devendra$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKhamparia$b Aditya$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDutta$b Venkatesh$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBalas$b Valentina E$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911007364703321 996 $aArtificial Intelligence in Microbial Research$94389726 997 $aUNINA