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Smart room occupancy detection using neural networks and the puma optimization algorithm.

El-Sayed M El-Kenawy^1,2, Ahmed Mohamed Zaki³, Ebrahim A Mattar⁴

¹Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt. sayed.kenawy@dhiet.edu.eg.

Scientific Reports

|December 20, 2025

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This summary is machine-generated.

This study introduces an optimized machine learning model for accurate room occupancy detection using environmental data. The novel approach enhances intelligent building automation and energy efficiency.

Area of Science:

Artificial Intelligence
Building Automation
Machine Learning

Background:

Traditional room occupancy detection methods face challenges with cost, scalability, and adaptability.
Accurate occupancy detection is crucial for energy-efficient buildings, enhanced security, and occupant comfort.

Purpose of the Study:

To propose an optimized machine learning approach for accurate room occupancy detection.
To address limitations of existing methods using a novel optimization technique.

Main Methods:

Utilized a Neural Network (NN) model optimized with the Puma Optimizer Sine Cosine Optimizer (POSC) metaheuristic.
Employed environmental sensor data including temperature, humidity, light intensity, and CO2 levels for training and evaluation.
Compared the POSC-optimized NN model against Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) on a public dataset.

Keywords:

Energy Efficiency Machine Learning Metaheuristic Optimization Neural Networks Puma Optimizer Room Occupancy Detection Sine Cosine Optimizer Smart Buildings

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Main Results:

The POSC-optimized NN model demonstrated superior classification accuracy compared to conventional methods.
Achieved significant improvements in precision, recall, and F1-score.
The optimization technique facilitated faster convergence and better classification through balanced exploration and exploitation.

Conclusions:

The combination of metaheuristic optimization and deep learning offers a practical solution for intelligent occupancy detection.
This approach can significantly contribute to energy-efficient systems and smart building automation.
The proposed model shows potential for real-world applications in intelligent environments.