Artificial Intelligence and Machine Learning in Smart City Planning shows the reader practical applications of AIML techniques and describes recent advancements in this area in various sectors. Owing to the multidisciplinary nature, this book primarily focuses on the concepts of AIML and its methodologies such as evolutionary techniques, neural networks, machine learning, deep learning, block chain technology, big data analytics, and image processing in the context of smart cities. The text also discusses possible solutions to different challenges posed by smart cities by presenting cutting edge AIML techniques using different methodologies, as well as future directions for those same techniques.
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. xiii 7. Chapter One. A study on the perceptions of officials on their duties and responsibilities at various levels of the organizational structure in order to accomplish artificial intelligence-based smart city implementation - p. 1 8. Part One: Smart city framework and implementation - p. 10 9. Chapter Two. Integration of IoT with big data analytics for the development of smart society - p. 13 10. Chapter Three. Deep learning model for flood estimate and relief management system using hybrid algorithm - p. 29 11. Chapter Four. Powering data-driven decision-making for the development of urban economies in India - p. 50 12. Chapter Five. An investigation into the effectiveness of smart city projects by identifying the framework for measuring performance - p. 71 13. Part Two: Smart water management - p. 84 14. Chapter Six. Waste water-based pico-hydro power for automatic street light control through IOT-based sensors in smart cities: A pecuniary assessment - p. 87 15. Part Three: Smart education - p. 99 16. Chapter Seven. Reigniting the power of artificial intelligence in education sector for the educators and students competence - p. 103 17. Chapter Eight. A study of postgraduate students’ perceptions of key components in ICCC to be used in artificial intelligence-based smart cities - p. 117 18. Part Four: Smart environment - p. 133 19. Chapter Nine. Renewable energy based hybrid power quality compensator based on deep learning network for smart cities - p. 137 20. Chapter Ten. Predicting subgrade and subbase California bearing ratio (CBR) failure at Calabar-Itu highway using AI (GP, ANN, and EPR) techniques for effective maintenance - p. 159 21. Chapter Eleven. Machine learning algorithms-based solar power forecasting in smart cities - p. 171 22. Chapter Twelve. Smart grid: Solid-state transformer and load forecasting techniques using artificial intelligence - p. 181 23. Chapter Thirteen. Machine learning and predictive control-based energy management system for smart buildings - p. 199 24. Chapter Fourteen. Effective prediction of solar energy using a machine learning technique - p. 221 25. Chapter Fifteen. Experience in using sensitivity analysis and ANN for predicting the reinforced stone columns’ bearing capacity sited in soft clays - p. 231 26. Chapter Sixteen. Sensitivity analysis and estimation of improved unsaturated soil plasticity index using SVM, M5P, and random forest regression - p. 243 27. Chapter Seventeen. Forecasting off-grid solar power generation using case-based reasoning algorithm for a small-scale system - p. 257 28. Chapter Eighteen. Implementing an ANN model and relative importance for predicting the under drained shear strength of fine-grained soil - p. 267 29. Part Five: Smart transportation - p. 277 30. Chapter Nineteen. Smart transportation based on AI and ML technology - p. 281 31. Part Six: Tackling cyber attacks - p. 299 32. Chapter Twenty. Generative adversarial network-based deep learning technique for smart grid data security - p. 303 33. Part Seven: Smart communications - p. 315 34. Chapter Twenty-one. An overview of smart city planning—The future technology - p. 319 35. Index - p. 337
Artificial Intelligence and Machine Learning in Smart City Planning shows the reader practical applications of AIML techniques and describes recent advancements in this area in various sectors. Owing to the multidisciplinary nature, this book primarily focuses on the concepts of AIML and its methodologies such as evolutionary techniques, neural networks, machine learning, deep learning, block chain technology, big data analytics, and image processing in the context of smart cities. The text also discusses possible solutions to different challenges posed by smart cities by presenting cutting edge AIML techniques using different methodologies, as well as future directions for those same techniques.
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. xiii 7. Chapter One. A study on the perceptions of officials on their duties and responsibilities at various levels of the organizational structure in order to accomplish artificial intelligence-based smart city implementation - p. 1 8. Part One: Smart city framework and implementation - p. 10 9. Chapter Two. Integration of IoT with big data analytics for the development of smart society - p. 13 10. Chapter Three. Deep learning model for flood estimate and relief management system using hybrid algorithm - p. 29 11. Chapter Four. Powering data-driven decision-making for the development of urban economies in India - p. 50 12. Chapter Five. An investigation into the effectiveness of smart city projects by identifying the framework for measuring performance - p. 71 13. Part Two: Smart water management - p. 84 14. Chapter Six. Waste water-based pico-hydro power for automatic street light control through IOT-based sensors in smart cities: A pecuniary assessment - p. 87 15. Part Three: Smart education - p. 99 16. Chapter Seven. Reigniting the power of artificial intelligence in education sector for the educators and students competence - p. 103 17. Chapter Eight. A study of postgraduate students’ perceptions of key components in ICCC to be used in artificial intelligence-based smart cities - p. 117 18. Part Four: Smart environment - p. 133 19. Chapter Nine. Renewable energy based hybrid power quality compensator based on deep learning network for smart cities - p. 137 20. Chapter Ten. Predicting subgrade and subbase California bearing ratio (CBR) failure at Calabar-Itu highway using AI (GP, ANN, and EPR) techniques for effective maintenance - p. 159 21. Chapter Eleven. Machine learning algorithms-based solar power forecasting in smart cities - p. 171 22. Chapter Twelve. Smart grid: Solid-state transformer and load forecasting techniques using artificial intelligence - p. 181 23. Chapter Thirteen. Machine learning and predictive control-based energy management system for smart buildings - p. 199 24. Chapter Fourteen. Effective prediction of solar energy using a machine learning technique - p. 221 25. Chapter Fifteen. Experience in using sensitivity analysis and ANN for predicting the reinforced stone columns’ bearing capacity sited in soft clays - p. 231 26. Chapter Sixteen. Sensitivity analysis and estimation of improved unsaturated soil plasticity index using SVM, M5P, and random forest regression - p. 243 27. Chapter Seventeen. Forecasting off-grid solar power generation using case-based reasoning algorithm for a small-scale system - p. 257 28. Chapter Eighteen. Implementing an ANN model and relative importance for predicting the under drained shear strength of fine-grained soil - p. 267 29. Part Five: Smart transportation - p. 277 30. Chapter Nineteen. Smart transportation based on AI and ML technology - p. 281 31. Part Six: Tackling cyber attacks - p. 299 32. Chapter Twenty. Generative adversarial network-based deep learning technique for smart grid data security - p. 303 33. Part Seven: Smart communications - p. 315 34. Chapter Twenty-one. An overview of smart city planning—The future technology - p. 319 35. Index - p. 337
