Neutrosophic enhanced convolutional neural network for occupancy detection: structured model development and evaluation

Abstrak

In this study, we introduce an advanced convolutional neural network (CNN) model tailored for house occupancy detection, designed to accommodate the inherent uncertainties and contradictory information often encountered in sensor data. By integrating neutrosophic layers into the CNN architecture, we enable the model to effectively handle indeterminacy, vagueness, and inconsistency present in real-world sensor readings. Our approach employs neutrosophic convolutional, max-pooling, and logic layers, providing a comprehensive framework for feature extraction and decision-making. Through a structured methodology encompassing data preprocessing, model initialization, training, evaluation, and optimization, we demonstrate the efficacy of the proposed model in accurately detecting occupancy status within residential environments. This enhanced CNN model offers improved accuracy, robustness, and interpretability, thereby facilitating its integration into smart home systems and building automation applications, contributing to enhanced efficiency, comfort, and energy savings.