Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. xi 7. Editor Bio - p. xiii 8. Preface - p. xvii 9. Chapter 1. Smart sensing technologies for wastewater treatment plants - p. 1 10. Chapter 2. Advancements and artificial intelligence approaches in antennas for environmental sensing - p. 19 11. Chapter 3. Intelligent geo-sensing for moving toward smart, resilient, low emission, and less carbon transport - p. 39 12. Chapter 4. Language of response surface methodology as an experimental strategy for electrochemical wastewater treatment process optimization - p. 57 13. Chapter 5. Artificial intelligence and sustainability: solutions to social and environmental challenges - p. 93 14. Chapter 6. Application of multi-criteria decision-making tools for a site analysis of offshore wind turbines - p. 109 15. Chapter 7. Recent advances of image processing techniques in agriculture - p. 129 16. Chapter 8. Tuning swarm behavior for environmental sensing tasks represented as coverage problems - p. 155 17. Chapter 9. Machine learning applications for developing sustainable construction materials - p. 179 18. Chapter 10. The AI-assisted removal and sensor-based detection of contaminants in the aquatic environment - p. 211 19. Chapter 11. Recent progress in biosensors for wastewater monitoring and surveillance - p. 245 20. Chapter 12. Machine learning in surface plasmon resonance for environmental monitoring - p. 269 21. Index - p. 299
Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. xi 7. Editor Bio - p. xiii 8. Preface - p. xvii 9. Chapter 1. Smart sensing technologies for wastewater treatment plants - p. 1 10. Chapter 2. Advancements and artificial intelligence approaches in antennas for environmental sensing - p. 19 11. Chapter 3. Intelligent geo-sensing for moving toward smart, resilient, low emission, and less carbon transport - p. 39 12. Chapter 4. Language of response surface methodology as an experimental strategy for electrochemical wastewater treatment process optimization - p. 57 13. Chapter 5. Artificial intelligence and sustainability: solutions to social and environmental challenges - p. 93 14. Chapter 6. Application of multi-criteria decision-making tools for a site analysis of offshore wind turbines - p. 109 15. Chapter 7. Recent advances of image processing techniques in agriculture - p. 129 16. Chapter 8. Tuning swarm behavior for environmental sensing tasks represented as coverage problems - p. 155 17. Chapter 9. Machine learning applications for developing sustainable construction materials - p. 179 18. Chapter 10. The AI-assisted removal and sensor-based detection of contaminants in the aquatic environment - p. 211 19. Chapter 11. Recent progress in biosensors for wastewater monitoring and surveillance - p. 245 20. Chapter 12. Machine learning in surface plasmon resonance for environmental monitoring - p. 269 21. Index - p. 299
