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Introduction

Anomaly detection in smart homes has become an essential research area due to the increasing adoption of smart devices and Internet of Things (IoT) technologies in residential settings. With the proliferation of sensors and connected devices in homes, there is a need to develop intelligent algorithms that can effectively detect abnormal activities and events in real-time to ensure the security and wellbeing of residents. This thesis aims to explore different approaches to anomaly detection in smart homes and evaluate their effectiveness in detecting anomalies accurately and efficiently.

1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter Two: Literature Review
2.1 Introduction to Anomaly Detection in Smart Homes
2.2 Existing Anomaly Detection Techniques
2.3 Machine Learning Algorithms for Anomaly Detection
2.4 IoT Technologies in Smart Homes
2.5 Application of Anomaly Detection in Smart Homes
2.6 Challenges in Anomaly Detection in Smart Homes
2.7 Evaluation Metrics for Anomaly Detection
2.8 Anomaly Detection Datasets
2.9 Comparative Analysis of Anomaly Detection Techniques
2.10 Conclusion

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Preprocessing Techniques
3.4 Feature Selection and Engineering
3.5 Model Training and Evaluation
3.6 Performance Metrics
3.7 Experimental Setup
3.8 Ethical Considerations

Chapter Four: Discussion of Findings
4.1 Analysis of Anomaly Detection Algorithms
4.2 Performance Comparison of Different Models
4.3 Interpretation of Results
4.4 Limitations of the Study
4.5 Future Research Directions

Chapter Five: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview on Anomaly Detection in Smart Homes

Anomaly detection in smart homes is a critical research area that involves the development of algorithms and techniques to identify abnormal activities and events in residential settings. With the increasing adoption of IoT technologies and smart devices in homes, there is a growing need for effective anomaly detection systems to ensure the security and safety of residents. This thesis aims to investigate various approaches to anomaly detection in smart homes and evaluate their performance in detecting anomalies accurately and efficiently.

The literature review will provide an overview of existing anomaly detection techniques, machine learning algorithms, IoT technologies, and applications of anomaly detection in smart homes. The research methodology will outline the research design, data collection methods, preprocessing techniques, model training, and evaluation procedures. The discussion of findings will analyze the performance of different anomaly detection algorithms, compare their effectiveness, and highlight any limitations of the study.

Overall, this thesis will contribute to the existing body of knowledge on anomaly detection in smart homes and provide valuable insights for researchers, practitioners, and policymakers in the field. By evaluating the performance of various anomaly detection techniques, this study aims to enhance the security and safety of smart homes and improve the overall quality of life for residents.

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