| Abstrakt: |
This study introduces an advanced convolutional neural network (CNN) model tailored for building occupancy detection that accommodates 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. The 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 building management systems and building automation applications, to enhance efficiency, comfort, and energy savings. [ABSTRACT FROM AUTHOR] |
