Analyzing Machine Learning Algorithms for Real-Time Data Processing in Smart City Environments

1. Introduction 1.1 Background and Motivation 1.2 Purpose and Scope 1.3 Structure of the Thesis 2. Smart City Environments 2.1 Definition and Characteristics 2.2 Key Technologies 2.3 Data Sources in Smart Cities 3. Real-Time Data Processing 3.1 Definition and Importance 3.2 Challenges in Real-Time Processing 3.3 Tools and Technologies 4. Machine Learning Algorithms Overview 4.1 Types of Machine Learning 4.2 Common Algorithms Used 4.3 Selection Criteria 5. Algorithms for Real-Time Processing 5.1 Regression Algorithms 5.2 Classification Algorithms 5.3 Clustering Algorithms 5.4 Decision Trees 6. Evaluation Metrics and Methods 6.1 Performance Metrics 6.2 Scalability Considerations 6.3 Resource Efficiency 7. Case Studies in Smart Cities 7.1 Transportation Management 7.2 Energy Consumption Optimization 7.3 Waste Management Systems 8. Conclusion and Future Directions 8.1 Summary of Findings 8.2 Recommendations 8.3 Future Research Areas