A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist’s perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future. This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms.
1. A Biologist's Guide to Artificial Intelligence - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page v 7. List of Illustrations - p. Page vi 8. List of Tables - p. Page vii 9. Chapter 1 : Exploring artificial intelligence through a biologist's lens - p. Page vii 10. Chapter 2 : The synergy of AI and biology: A transformative partnership - p. Page 11 11. Chapter 3 : Understanding life and evolution using AI - p. Page 30 12. Chapter 4 : Decoding life: Genetics, bioinformatics, and artificial intelligence - p. Page 42 13. Chapter 5 : AI in healthcare: Pioneering innovations for a healthier tomorrow - p. Page 64 14. Chapter 6 : Reimagining occupational health and safety in the era of AI - p. Page 76 15. Chapter 7 : From data to insights: Leveraging machine learning for diabetes management - p. Page 93 16. Chapter 8 : Smiles 2.0: The AI dentistry frontier - p. Page 120 17. Chapter 9 : Applications and impact of artificial intelligence in veterinary sciences - p. Page 133 18. Chapter 10 : Advancing precision agriculture through artificial intelligence: Exploring the future of cultivation - p. Page 148 19. Chapter 11 : Artificial intelligence in animal farms for management and breeding - p. Page 163 20. Chapter 12 : Food manufacturing, processing, storage, and marketing using artificial intelligence - p. Page 179 21. Chapter 13 : Use of AI in conservation and for understanding climate change - p. Page 198 22. Chapter 14 : Artificial intelligence in marine biology - p. Page 229 23. Chapter 15 : Advances in robotics for biological sciences - p. Page 252 24. Chapter 16 : Robotics and computer vision for health, food security, and environment - p. Page 266 25. Chapter 17 : Artificial intelligence in classrooms: How artificial intelligence can aid in teaching biology - p. Page 281 26. Chapter 18 : Ethical issues around artificial intelligence - p. Page 295 27. Chapter 19 : A meshwork of artificial intelligence and biology: The future of science - p. Page 313 28. Index - p. Page 330
A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist’s perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future. This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms.
1. A Biologist's Guide to Artificial Intelligence - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page v 7. List of Illustrations - p. Page vi 8. List of Tables - p. Page vii 9. Chapter 1 : Exploring artificial intelligence through a biologist's lens - p. Page vii 10. Chapter 2 : The synergy of AI and biology: A transformative partnership - p. Page 11 11. Chapter 3 : Understanding life and evolution using AI - p. Page 30 12. Chapter 4 : Decoding life: Genetics, bioinformatics, and artificial intelligence - p. Page 42 13. Chapter 5 : AI in healthcare: Pioneering innovations for a healthier tomorrow - p. Page 64 14. Chapter 6 : Reimagining occupational health and safety in the era of AI - p. Page 76 15. Chapter 7 : From data to insights: Leveraging machine learning for diabetes management - p. Page 93 16. Chapter 8 : Smiles 2.0: The AI dentistry frontier - p. Page 120 17. Chapter 9 : Applications and impact of artificial intelligence in veterinary sciences - p. Page 133 18. Chapter 10 : Advancing precision agriculture through artificial intelligence: Exploring the future of cultivation - p. Page 148 19. Chapter 11 : Artificial intelligence in animal farms for management and breeding - p. Page 163 20. Chapter 12 : Food manufacturing, processing, storage, and marketing using artificial intelligence - p. Page 179 21. Chapter 13 : Use of AI in conservation and for understanding climate change - p. Page 198 22. Chapter 14 : Artificial intelligence in marine biology - p. Page 229 23. Chapter 15 : Advances in robotics for biological sciences - p. Page 252 24. Chapter 16 : Robotics and computer vision for health, food security, and environment - p. Page 266 25. Chapter 17 : Artificial intelligence in classrooms: How artificial intelligence can aid in teaching biology - p. Page 281 26. Chapter 18 : Ethical issues around artificial intelligence - p. Page 295 27. Chapter 19 : A meshwork of artificial intelligence and biology: The future of science - p. Page 313 28. Index - p. Page 330
