Deep Learning with Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for AI-Driven Smart Home Automation

This research proposes a hybrid deep learning framework that incorporates convolutional neural networks (CNN) and long short-term memory (LSTM) networks to design intelligent and responsive smart home automation systems. Training is performed in spatial patterns of camera frames, like visual data through CNNs and time-series sensor inputs through LSTMs, to identify patterns across time and human behavior models. The approach includes pre-processing multimodal datasets, building a CNN-LSTM model, and integrating the model into an online smart home simulator. Experimental results show that the integrated model of CNN-LSTM performs superior to isolated CNN and LSTM models in terms of all performance parameters. The system achieved 93.4% accuracy, 92.7% precision, and 91.9% F1 score using hindsight on a well-balanced and robust prediction platform. Feature importance estimation revealed motion sensor and temperature as the prevailing parameters with the highest impact. In addition, execution time estimation rendered the model feasible to implement on real-time systems with no delay. Results confirm the efficacy of using a combination of spatial and temporal learning techniques in context-aware, user-centric smart home system design. This book provides a basis for further research on home automation with deep learning, including edge deployment and real-time adaptive control.