Author(s)

Joe Essien

Department of Computer and Information Technology, Veritas University, Abuja, Nigeria.

Sensors for fire alarms require a high level of predictive variables to ensure accurate detection, injury prevention, and loss prevention. Bayesian networks can aid in enhancing early fire detection capabilities and reducing the frequency of erroneous fire alerts, thereby enhancing the effectiveness of numerous safety monitoring systems. This research explores the development of optimized probabilistic graphic models for the discretization thresholds of alarm system predictor variables. The study presents a statistical model framework that increases the efficacy of fire detection by predicting the discretization thresholds of alarm system predictor variable fluctuations used to detect the onset of fire. The work applies the Bayesian networks and probabilistic visual models to reveal the specific characteristics required to cope with fire detection strategies and patterns. The adopted methodology utilizes a combination of prior knowledge and statistical data to draw conclusions from observations. Utilizing domain knowledge to compute conditional dependencies between network variables enabled predictions to be made through the application of specialized analytical and simulation techniques.

Neural Network, Discretization, Alarm Systems, Graphical Models, Machine Learning

Essien, J. (2023) Enhancing Feature Discretization in Alarm and Fire Detection Systems Using Probabilistic Inference Models. Journal of Computer and Communications, 11, 140-155. doi: 10.4236/jcc.2023.117009.