FL-DL: Fuzzy Logic with Deep Learning, Hybrid Anomaly Detection and Activity Prediction in Smart Homes Data-Sets

In the era of digitization and smart technologies, the generation of huge amounts of data has become ubiquitous. Extracting and mining valuable information from this data is both crucial and challenging. This paper proposes a hybrid approach that integrates fuzzy logic with deep learning techniques for anomaly detection and next-activity prediction in smart homes. The proposed methodology is particularly designed to support elderly residents by accurately detecting anomalous patterns and predicting daily activities, thereby enhancing the system's accuracy and reliability. The hybrid model uses fuzzy logic to define membership functions, rule-based decision making, and effectively handle abnormalities, while deep learning techniques are employed for predictive analysis. Experiments conducted on a real-world smart home data-set demonstrate that the proposed approach significantly outperforms traditional methods in terms of accuracy, correctness, and loss. The proposed research study makes a valuable contribution to the field of smart home technologies.