About this article
- Submitted: Apr 20, 2023
- Final Revised: May 30, 2023
- Accepted: Jan 12, 2026
- Published: Jun 30, 2023
- Abstract view: 22
- PDF Download: 1
- Volumes: Vol. 1 No. 1 (2023) Pages 45-70
- DOI: 10.63208/21018-96
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright (c) 2023 Author(s)
Cite this article
Abstract
Conventional fire detection systems primarily rely on smoke and heat sensors, yet environmental parameters like air temperature, humidity, and pressure offer critical supplementary data.1 This study employs a sensor fusion approach using a Genetic Programming Symbolic Classifier (GPSC) to generate a transparent AI model in the form of a symbolic expression. To address significant class imbalance, the investigation evaluates multiple resampling techniques, including Random Sampling, Near Miss-1, ADASYN, SMOTE, and Borderline SMOTE. These datasets were processed using a custom random hyperparameter search and 5-fold cross-validation to ensure model robustness. Results indicate that the SMOTE-balanced dataset yielded superior performance, achieving an accuracy of 0.998 4.79 10-5 alongside consistently high values for AUC and F1-score. The paper concludes by presenting the optimal symbolic expression and validating its efficacy on the original dataset.
References
Khan, F.; Xu, Z.; Sun, J.; Khan, F.M.; Ahmed, A.; Zhao, Y. Recent Advances in Sensors for Fire Detection. Sensors 2022, 22, 3310.
Seo, B.K.; Nam, S.G. Study of the improvement of false fire alarms in analog photoelectric type smoke detectors. Fire Sci. Eng. 2016, 30, 108–115.
Newman, J.S. Modified theory for the characterization of ionization smoke detectors. Fire Saf. Sci. 1994, 4, 785–792.
Mohammed, K.R. A real-time forest fire and smoke detection system using deep learning. Int. J. Nonlinear Anal. Appl. 2022, 13, 2053–2063.
Zheng, X.; Chen, F.; Lou, L.; Cheng, P.; Huang, Y. Real-Time Detection of Full-Scale Forest Fire Smoke Based on Deep Convolution Neural Network. Remote Sens. 2022, 14, 536.
Ryu, J.; Kwak, D. A Study on a Complex Flame and Smoke Detection Method Using Computer Vision Detection and Convolutional Neural Network. Fire 2022, 5, 108.
Masoom, S.M.; Zhang, Q.; Dai, P.; Jia, Y.; Zhang, Y.; Zhu, J.; Wang, J. Early Smoke Detection Based on Improved YOLO-PCA Network. Fire 2022, 5, 40.
Sarwar, B.; Bajwa, I.S.; Jamil, N.; Ramzan, S.; Sarwar, N. An intelligent fire warning application using IoT and an adaptive neuro-fuzzy inference system. Sensors 2019, 19, 3150.
Muhammad, K.; Khan, S.; Palade, V.; Mehmood, I.; De Albuquerque, V.H.C. Edge intelligence-assisted smoke detection in foggy surveillance environments. IEEE Trans. Ind. Inform. 2019, 16, 1067–1075.
Choueiri, S.; Daoud, D.; Harb, S.; Achkar, R. Fire and Smoke Detection Using Artificial Neural Networks. In Proceedings of the 2020 IEEE 14th International Conference on Open Source Systems and Technologies (ICOSST), Lahore, Pakistan, 16–17 December 2020; pp. 1–6.
Andrew, A.; Shakaff, A.; Zakaria, A.; Gunasagaran, R.; Kanagaraj, E.; Saad, S. Early stage fire source classification in building using artificial intelligence. In Proceedings of the 2018 IEEE Conference on Systems, Process and Control (ICSPC), Melaka, Malaysia, 14–15 December 2018; pp. 165–169.
Solórzano, A.; Eichmann, J.; Fernandez, L.; Ziems, B.; Jiménez-Soto, J.M.; Marco, S.; Fonollosa, J. Early fire detection based on gas sensor arrays: Multivariate calibration and validation. Sens. Actuators Chem. 2022, 352, 130961.
Sasiadek, J.Z. Sensor fusion. Annu. Rev. Control. 2002, 26, 203–228.
Sucuoglu, H.S.; Bogrekci, I.; Demircioglu, P. Development of mobile robot with sensor fusion fire detection unit. IFAC-PapersOnLine 2018, 51, 430–435.
Chen, S.; Bao, H.; Zeng, X.; Yang, Y. A fire detecting method based on multi-sensor data fusion. In Proceedings of the SMC’03 Conference Proceedings, 2003 IEEE International Conference on Systems, Man and Cybernetics, Conference Theme-System Security and Assurance (Cat. No. 03CH37483), Washington, DC, USA, 8 October 2003; Volume 4, pp. 3775–3780.
Liang, Y.H.; Tian, W.M. Multi-sensor fusion approach for fire alarm using BP neural network. In Proceedings of the 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS), Ostrawva, Czech Republic, 7–9 September 2016; pp. 99–102.
Solórzano, A.; Fonollosa, J.; Fernández, L.; Eichmann, J.; Marco, S. Fire detection using a gas sensor array with sensor fusion algorithms. In Proceedings of the 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), Montreal, QC, Canada, 28–31 May 2017; pp. 1–3.
Ting, Y.Y.; Hsiao, C.W.; Wang, H.S. A data fusion-based fire detection system. IEICE Trans. Inf. Syst. 2018, 101, 977–984.
Wu, L.; Chen, L.; Hao, X. Multi-sensor data fusion algorithm for indoor fire early warning based on BP neural network. Information 2021, 12, 59.
Hsu, Y.L.; Chou, P.H.; Chang, H.C.; Lin, S.L.; Yang, S.C.; Su, H.Y.; Chang, C.C.; Cheng, Y.S.; Kuo, Y.C. Design and implementation of a smart home system using multisensor data fusion technology. Sensors 2017, 17, 1631.
Jondhale, S.R.; Sharma, M.; Maheswar, R.; Shubair, R.; Shelke, A. comparison of neural network training functions for rssi based indoor localization problem in WSN. In Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario’s; Springer: Berlin, Germany, 2020; pp. 112–133.
Farooqui, N.A.; Tyagi, A. Data Mining and Fusion Techniques for Wireless Intelligent Sensor Networks. In Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario’s; Singh, P.K., Bhargava, B.K., Paprzycki, M., Kaushal, N.C., Hong, W.C., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 592–615.
Butt, F.A.; Chattha, J.N.; Ahmad, J.; Zia, M.U.; Rizwan, M.; Naqvi, I.H. On the Integration of Enabling Wireless Technologies and Sensor Fusion for Next-Generation Connected and Autonomous Vehicles. IEEE Access 2022, 10, 14643–14668.
Yusuf, A.; Sulaiman, T.A.; Alshomrani, A.S.; Baleanu, D. Optical solitons with nonlinear dispersion in parabolic law medium and three-component coupled nonlinear Schrödinger equation. Opt. Quan. Electron. 2022, 54, 1–13.
Ren, X.; Li, C.; Ma, X.; Chen, F.; Wang, H.; Sharma, A.; Gaba, G.S.; Masud, M. Design of multi-information fusion based intelligent electrical fire detection system for green buildings. Sustainability 2021, 13, 3405.
Singh, N.; Gunjan, V.K.; Chaudhary, G.; Kaluri, R.; Victor, N.; Lakshmanna, K. IoT enabled HELMET to safeguard the health of mine workers. Comput. Commun. 2022, 193, 1–9.
Rahate, A.; Mandaokar, S.; Chandel, P.; Walambe, R.; Ramanna, S.; Kotecha, K. Employing multimodal co-learning to evaluate the robustness of sensor fusion for industry 5.0 tasks. Soft Comput. 2022, 2022, 1–17.
Vakil, A.; Liu, J.; Zulch, P.; Blasch, E.; Ewing, R.; Li, J. A survey of multimodal sensor fusion for passive RF and EO information integration. IEEE Aerosp. Electron. Syst. Mag. 2021, 36, 44–61.
Mian, T.; Choudhary, A.; Fatima, S. A sensor fusion based approach for bearing fault diagnosis of rotating machine. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 2022, 236, 661–675.
Contractor, D. Kaggle: Smoke Detection Dataset. 2022. Available online: https://www.kaggle.com/datasets/deepcontractor/smoke-detection-dataset (accessed on 22 November 2022).
Sensirion Company. Sensor for HVAC and Air Quality Applications SPS30 Datasheet. Available online: https://cdn.sparkfun.com/assets/2/d/2/a/6/Sensirion_SPS30_Particulate_Matter_Sensor_v0.9_D1__1_.pdf (accessed on 22 November 2022).
Bosch. BME688 4-in-1 Air Quality Breakout (Gas, Temperature, Pressure, Humidity) Datasheet. Available online: https://www.bosch-sensortec.com/products/environmental-sensors/gas-sensors/bme688/ (accessed on 22 November 2022).
±2% (0–100%RH) Digital Humidity and Temperature Sensor. Available online: https://sensirion.com/products/catalog/SHT31DISB/ (accessed on 22 November 2022).
Bosch BMP390 Barometric Pressure Sensor. Available online: https://eu.mouser.com/new/bosch/bosch-bmp390-pressure-sensor/ (accessed on 22 November 2022).
Available online: https://www.bosch-sensortec.com/products/environmental-sensors/pressure-sensors/bmp388/ (accessed on 22 November 2022).
Multi-Gas (VOC and CO2eq) Sensor. Available online: https://sensirion.com/products/catalog/SGP30/ (accessed on 22 November 2022).
Arduino Officia: NICLA Sense Me. Available online: https://store.arduino.cc/products/nicla-sense-me (accessed on 22 November 2022).
Raju, V.G.; Lakshmi, K.P.; Jain, V.M.; Kalidindi, A.; Padma, V. Study the influence of normalization/transformation process on the accuracy of supervised classification. In Proceedings of the 2020 IEEE Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 20–22 August 2020; pp. 729–735.
Benesty, J.; Chen, J.; Huang, Y.; Cohen, I. Pearson correlation coefficient. In Noise Reduction in Speech Processing; Springer: Berlin, Germany, 2009; pp. 1–4.
Jeni, L.A.; Cohn, J.F.; De La Torre, F. Facing imbalanced data–Recommendations for the use of performance metrics. In Proceedings of the 2013 IEEE Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland, 2–5 September 2013; pp. 245–251.
Prusa, J.; Khoshgoftaar, T.M.; Dittman, D.J.; Napolitano, A. Using random undersampling to alleviate class imbalance on tweet sentiment data. In Proceedings of the 2015 IEEE International Conference on Information Reuse and Integration, San Francisco, CA, USA, 13–15 August 2015; pp. 197–202.
Babikir, M. Imbalanced Data Classification Enhancement Using SMOTE and NearMiss Sampling Techniques. Ph.D. Thesis, Sudan University of Science & Technology, Khartoum, Sudan, 2021.
Pang, Y.; Chen, Z.; Peng, L.; Ma, K.; Zhao, C.; Ji, K. A signature-based assistant random oversampling method for malware detection. In Proceedings of the 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), Rotorua, New Zealand, 5–8 August 2019; pp. 256–263.
He, H.; Bai, Y.; Garcia, E.A.; Li, S. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China, 1–8 June 2008; pp. 1322–1328.
Chawla, N.V.; Bowyer, K.W.; Hall, L.O.; Kegelmeyer, W.P. SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 2002, 16, 321–357.
Han, H.; Wang, W.Y.; Mao, B.H. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning. In Proceedings of the International Conference on Intelligent Computing, Springer, Shenzhen, China, 12–15 August 2005; pp. 878–887.
Lorencin, I.; Baressi Šegota, S.; Anđelić, N.; Mrzljak, V.; Ćabov, T.; Španjol, J.; Car, Z. On urinary bladder cancer diagnosis: Utilization of deep convolutional generative adversarial networks for data augmentation. Biology 2021, 10, 175.
Lorencin, I.; Anđelić, N.; Mrzljak, V.; Car, Z. Genetic algorithm approach to design of multi-layer perceptron for combined cycle power plant electrical power output estimation. Energies 2019, 12, 4352.
Anđelić, N.; Lorencin, I.; Glučina, M.; Car, Z. Mean Phase Voltages and Duty Cycles Estimation of a Three-Phase Inverter in a Drive System Using Machine Learning Algorithms. Electronics 2022, 11, 2623.
Sammut, C.; Webb, G.I. Encyclopedia of Machine Learning; Springer Science & Business Media: Berlin, Germany, 2011.
Sturm, B.L. Classification accuracy is not enough. J. Intell. Inf. Syst. 2013, 41, 371–406.
Hand, D.J.; Till, R.J. A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach. Learn. 2001, 45, 171–186.
Flach, P.; Kull, M. Precision-recall-gain curves: PR analysis done right. Adv. Neural Inf. Process. Syst. 2015, 28, 1–9.
Chicco, D.; Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 2020, 21, 6.