Mert NakipCuneyt GuzelisGuzelis, CuneytNakip, Mert2025-10-062019978-1-7281-2868-9978172812868910.1109/asyu48272.2019.89464462-s2.0-85078344966http://dx.doi.org/10.1109/asyu48272.2019.8946446https://gcris.yasar.edu.tr/handle/123456789/6106https://doi.org/10.1109/asyu48272.2019.8946446https://doi.org/10.1109/ASYU48272.2019.8946446This paper proposes a method to reduce false positive fire alarms by fusing data from different sensors using a specific machine learning model. We design an electronic circuit with 6 sensors to detect 7 physical sensory inputs. We experimentally collect dataset for training and testing of machine learning models which are used for the implementation of fusing and classifying sensor data. An algorithm which employs the trained machine learning model for the classification of sensor data and then the thresholding is designed. Machine learning models are selected based on the results of comparisons among multi-layer perceptron support vector machine and radial basis function network. We use classification accuracy percentage false negative error and false positive error as measures for comparison. Multi-layer perceptron is observed as the best model according to its 96.875% classification accuracy.Turkishinfo:eu-repo/semantics/closedAccessfire detection, multi-sensor, machine learningFire DetectionMachine LearningMulti-sensorMachine Learning.Conference Object