{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T07:16:08Z","timestamp":1766733368395,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,24]],"date-time":"2022-12-24T00:00:00Z","timestamp":1671840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fire is usually detected with fire detection systems that are used to sense one or more products resulting from the fire such as smoke, heat, infrared, ultraviolet light radiation, or gas. Smoke detectors are mostly used in residential areas while fire alarm systems (heat, smoke, flame, and fire gas detectors) are used in commercial, industrial and municipal areas. However, in addition to smoke, heat, infrared, ultraviolet light radiation, or gas, other parameters could indicate a fire, such as air temperature, air pressure, and humidity, among others. Collecting these parameters requires the development of a sensor fusion system. However, with such a system, it is necessary to develop a simple system based on artificial intelligence (AI) that will be able to detect fire with high accuracy using the information collected from the sensor fusion system. The novelty of this paper is to show the procedure of how a simple AI system can be created in form of symbolic expression obtained with a genetic programming symbolic classifier (GPSC) algorithm and can be used as an additional tool to detect fire with high classification accuracy. Since the investigation is based on an initially imbalanced and publicly available dataset (high number of samples classified as 1-Fire Alarm and small number of samples 0-No Fire Alarm), the idea is to implement various balancing methods such as random undersampling\/oversampling, Near Miss-1, ADASYN, SMOTE, and Borderline SMOTE. The obtained balanced datasets were used in GPSC with random hyperparameter search combined with 5-fold cross-validation to obtain symbolic expressions that could detect fire with high classification accuracy. For this investigation, the random hyperparameter search method and 5-fold cross-validation had to be developed. Each obtained symbolic expression was evaluated on train and test datasets to obtain mean and standard deviation values of accuracy (ACC), area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score. Based on the conducted investigation, the highest classification metric values were achieved in the case of the dataset balanced with SMOTE method. The obtained values of ACC\u00af\u00b1SD(ACC), AUC\u00af\u00b1SD(ACU), Precision\u00af\u00b1SD(Precision), Recall\u00af\u00b1SD(Recall), and F1-score\u00af\u00b1SD(F1-score) are equal to 0.998\u00b14.79\u00d710\u22125, 0.998\u00b14.79\u00d710\u22125, 0.999\u00b15.32\u00d710\u22125, 0.998\u00b14.26\u00d710\u22125, and 0.998\u00b14.796\u00d710\u22125, respectively. The symbolic expression using which best values of classification metrics were achieved is shown, and the final evaluation was performed on the original dataset.<\/jats:p>","DOI":"10.3390\/s23010169","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:55:07Z","timestamp":1672109707000},"page":"169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["The Development of Symbolic Expressions for Fire Detection with Symbolic Classifier Using Sensor Fusion Data"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0314-243X","authenticated-orcid":false,"given":"Nikola","family":"An\u0111eli\u0107","sequence":"first","affiliation":[{"name":"Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3015-1024","authenticated-orcid":false,"given":"Sandi","family":"Baressi \u0160egota","sequence":"additional","affiliation":[{"name":"Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5964-245X","authenticated-orcid":false,"given":"Ivan","family":"Lorencin","sequence":"additional","affiliation":[{"name":"Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2817-9252","authenticated-orcid":false,"given":"Zlatan","family":"Car","sequence":"additional","affiliation":[{"name":"Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Khan, F., Xu, Z., Sun, J., Khan, F.M., Ahmed, A., and Zhao, Y. (2022). Recent Advances in Sensors for Fire Detection. Sensors, 22.","DOI":"10.3390\/s22093310"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108","DOI":"10.7731\/KIFSE.2016.30.5.108","article-title":"Study of the improvement of false fire alarms in analog photoelectric type smoke detectors","volume":"30","author":"Seo","year":"2016","journal-title":"Fire Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"785","DOI":"10.3801\/IAFSS.FSS.4-785","article-title":"Modified theory for the characterization of ionization smoke detectors","volume":"4","author":"Newman","year":"1994","journal-title":"Fire Saf. Sci."},{"key":"ref_4","first-page":"2053","article-title":"A real-time forest fire and smoke detection system using deep learning","volume":"13","author":"Mohammed","year":"2022","journal-title":"Int. J. Nonlinear Anal. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zheng, X., Chen, F., Lou, L., Cheng, P., and Huang, Y. (2022). Real-Time Detection of Full-Scale Forest Fire Smoke Based on Deep Convolution Neural Network. Remote Sens., 14.","DOI":"10.3390\/rs14030536"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ryu, J., and Kwak, D. (2022). A Study on a Complex Flame and Smoke Detection Method Using Computer Vision Detection and Convolutional Neural Network. Fire, 5.","DOI":"10.3390\/fire5040108"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Masoom, S.M., Zhang, Q., Dai, P., Jia, Y., Zhang, Y., Zhu, J., and Wang, J. (2022). Early Smoke Detection Based on Improved YOLO-PCA Network. Fire, 5.","DOI":"10.3390\/fire5020040"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Bajwa, I.S., Jamil, N., Ramzan, S., and Sarwar, N. (2019). An intelligent fire warning application using IoT and an adaptive neuro-fuzzy inference system. Sensors, 19.","DOI":"10.3390\/s19143150"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1109\/TII.2019.2915592","article-title":"Edge intelligence-assisted smoke detection in foggy surveillance environments","volume":"16","author":"Muhammad","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Choueiri, S., Daoud, D., Harb, S., and Achkar, R. (2020, January 16\u201317). Fire and Smoke Detection Using Artificial Neural Networks. Proceedings of the 2020 IEEE 14th International Conference on Open Source Systems and Technologies (ICOSST), Lahore, Pakistan.","DOI":"10.1109\/ICOSST51357.2020.9332990"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Andrew, A., Shakaff, A., Zakaria, A., Gunasagaran, R., Kanagaraj, E., and Saad, S. (2018, January 14\u201315). Early stage fire source classification in building using artificial intelligence. Proceedings of the 2018 IEEE Conference on Systems, Process and Control (ICSPC), Melaka, Malaysia.","DOI":"10.1109\/SPC.2018.8704155"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"130961","DOI":"10.1016\/j.snb.2021.130961","article-title":"Early fire detection based on gas sensor arrays: Multivariate calibration and validation","volume":"352","author":"Eichmann","year":"2022","journal-title":"Sens. Actuators Chem."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S1367-5788(02)00045-7","article-title":"Sensor fusion","volume":"26","author":"Sasiadek","year":"2002","journal-title":"Annu. Rev. Control."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.ifacol.2018.11.324","article-title":"Development of mobile robot with sensor fusion fire detection unit","volume":"51","author":"Sucuoglu","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_15","unstructured":"Chen, S., Bao, H., Zeng, X., and Yang, Y. (2003, January 8). A fire detecting method based on multi-sensor data fusion. Proceedings of the SMC\u201903 Conference Proceedings, 2003 IEEE International Conference on Systems, Man and Cybernetics, Conference Theme-System Security and Assurance (Cat. No. 03CH37483), Washington, DC, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liang, Y.H., and Tian, W.M. (2016, January 7\u20139). Multi-sensor fusion approach for fire alarm using BP neural network. Proceedings of the 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS), Ostrawva, Czech Republic.","DOI":"10.1109\/INCoS.2016.38"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sol\u00f3rzano, A., Fonollosa, J., Fern\u00e1ndez, L., Eichmann, J., and Marco, S. (2017, January 28\u201331). Fire detection using a gas sensor array with sensor fusion algorithms. Proceedings of the 2017 ISOCS\/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), Montreal, QC, Canada.","DOI":"10.1109\/ISOEN.2017.7968889"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1587\/transinf.2016IIP0005","article-title":"A data fusion-based fire detection system","volume":"101","author":"Ting","year":"2018","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, L., Chen, L., and Hao, X. (2021). Multi-sensor data fusion algorithm for indoor fire early warning based on BP neural network. Information, 12.","DOI":"10.3390\/info12020059"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hsu, Y.L., Chou, P.H., Chang, H.C., Lin, S.L., Yang, S.C., Su, H.Y., Chang, C.C., Cheng, Y.S., and Kuo, Y.C. (2017). Design and implementation of a smart home system using multisensor data fusion technology. Sensors, 17.","DOI":"10.3390\/s17071631"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jondhale, S.R., Sharma, M., Maheswar, R., Shubair, R., and Shelke, A. (2020). comparison of neural network training functions for rssi based indoor localization problem in WSN. Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario\u2019s, Springer.","DOI":"10.1007\/978-3-030-40305-8_7"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Singh, P.K., Bhargava, B.K., Paprzycki, M., Kaushal, N.C., and Hong, W.C. (2020). Data Mining and Fusion Techniques for Wireless Intelligent Sensor Networks. Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario\u2019s, Springer International Publishing.","DOI":"10.1007\/978-3-030-40305-8"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"14643","DOI":"10.1109\/ACCESS.2022.3145972","article-title":"On the Integration of Enabling Wireless Technologies and Sensor Fusion for Next-Generation Connected and Autonomous Vehicles","volume":"10","author":"Butt","year":"2022","journal-title":"IEEE Access"},{"key":"ref_24","first-page":"1","article-title":"Optical solitons with nonlinear dispersion in parabolic law medium and three-component coupled nonlinear Schr\u00f6dinger equation","volume":"54","author":"Yusuf","year":"2022","journal-title":"Opt. Quan. Electron."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ren, X., Li, C., Ma, X., Chen, F., Wang, H., Sharma, A., Gaba, G.S., and Masud, M. (2021). Design of multi-information fusion based intelligent electrical fire detection system for green buildings. Sustainability, 13.","DOI":"10.3390\/su13063405"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.comcom.2022.06.032","article-title":"IoT enabled HELMET to safeguard the health of mine workers","volume":"193","author":"Singh","year":"2022","journal-title":"Comput. Commun."},{"key":"ref_27","first-page":"1","article-title":"Employing multimodal co-learning to evaluate the robustness of sensor fusion for industry 5.0 tasks","volume":"2022","author":"Rahate","year":"2022","journal-title":"Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MAES.2020.3006410","article-title":"A survey of multimodal sensor fusion for passive RF and EO information integration","volume":"36","author":"Vakil","year":"2021","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_29","first-page":"661","article-title":"A sensor fusion based approach for bearing fault diagnosis of rotating machine","volume":"236","author":"Mian","year":"2022","journal-title":"Proc. Inst. Mech. Eng. Part O J. Risk Reliab."},{"key":"ref_30","unstructured":"Contractor, D. (2022, November 22). Kaggle: Smoke Detection Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/deepcontractor\/smoke-detection-dataset."},{"key":"ref_31","unstructured":"Sensirion Company (2022, November 22). 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."},{"key":"ref_32","unstructured":"Bosch (2022, November 22). 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\/."},{"key":"ref_33","unstructured":"(2022, November 22). \u00b12% (0\u2013100%RH) Digital Humidity and Temperature Sensor. Available online: https:\/\/sensirion.com\/products\/catalog\/SHT31DISB\/."},{"key":"ref_34","unstructured":"(2022, November 22). Bosch BMP390 Barometric Pressure Sensor. Available online: https:\/\/eu.mouser.com\/new\/bosch\/bosch-bmp390-pressure-sensor\/."},{"key":"ref_35","unstructured":"(2022, November 22). Available online: https:\/\/www.bosch-sensortec.com\/products\/environmental-sensors\/pressure-sensors\/bmp388\/."},{"key":"ref_36","unstructured":"(2022, November 22). Multi-Gas (VOC and CO2eq) Sensor. Available online: https:\/\/sensirion.com\/products\/catalog\/SGP30\/."},{"key":"ref_37","unstructured":"(2022, November 22). Arduino Officia: NICLA Sense Me. Available online: https:\/\/store.arduino.cc\/products\/nicla-sense-me."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Raju, V.G., Lakshmi, K.P., Jain, V.M., Kalidindi, A., and Padma, V. (2020, January 20\u201322). Study the influence of normalization\/transformation process on the accuracy of supervised classification. Proceedings of the 2020 IEEE Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India.","DOI":"10.1109\/ICSSIT48917.2020.9214160"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Benesty, J., Chen, J., Huang, Y., and Cohen, I. (2009). Pearson correlation coefficient. Noise Reduction in Speech Processing, Springer.","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jeni, L.A., Cohn, J.F., and De La Torre, F. (2013, January 2\u20135). Facing imbalanced data\u2013Recommendations for the use of performance metrics. Proceedings of the 2013 IEEE Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland.","DOI":"10.1109\/ACII.2013.47"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Prusa, J., Khoshgoftaar, T.M., Dittman, D.J., and Napolitano, A. (2015, January 13\u201315). Using random undersampling to alleviate class imbalance on tweet sentiment data. Proceedings of the 2015 IEEE International Conference on Information Reuse and Integration, San Francisco, CA, USA.","DOI":"10.1109\/IRI.2015.39"},{"key":"ref_42","unstructured":"Babikir, M. (2021). Imbalanced Data Classification Enhancement Using SMOTE and NearMiss Sampling Techniques. [Ph.D. Thesis, Sudan University of Science & Technology]."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Pang, Y., Chen, Z., Peng, L., Ma, K., Zhao, C., and Ji, K. (2019, January 5\u20138). A signature-based assistant random oversampling method for malware detection. 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.","DOI":"10.1109\/TrustCom\/BigDataSE.2019.00042"},{"key":"ref_44","unstructured":"He, H., Bai, Y., Garcia, E.A., and Li, S. (2008, January 1\u20138). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Han, H., Wang, W.Y., and Mao, B.H. (2005, January 12\u201315). Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning. Proceedings of the International Conference on Intelligent Computing, Springer, Shenzhen, China.","DOI":"10.1007\/11538059_91"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lorencin, I., Baressi \u0160egota, S., An\u0111eli\u0107, N., Mrzljak, V., \u0106abov, T., \u0160panjol, J., and Car, Z. (2021). On urinary bladder cancer diagnosis: Utilization of deep convolutional generative adversarial networks for data augmentation. Biology, 10.","DOI":"10.3390\/biology10030175"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lorencin, I., An\u0111eli\u0107, N., Mrzljak, V., and Car, Z. (2019). Genetic algorithm approach to design of multi-layer perceptron for combined cycle power plant electrical power output estimation. Energies, 12.","DOI":"10.3390\/en12224352"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"An\u0111eli\u0107, N., Lorencin, I., Glu\u010dina, M., and Car, Z. (2022). Mean Phase Voltages and Duty Cycles Estimation of a Three-Phase Inverter in a Drive System Using Machine Learning Algorithms. Electronics, 11.","DOI":"10.3390\/electronics11162623"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Sammut, C., and Webb, G.I. (2011). Encyclopedia of Machine Learning, Springer Science & Business Media.","DOI":"10.1007\/978-0-387-30164-8"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s10844-013-0250-y","article-title":"Classification accuracy is not enough","volume":"41","author":"Sturm","year":"2013","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1023\/A:1010920819831","article-title":"A simple generalisation of the area under the ROC curve for multiple class classification problems","volume":"45","author":"Hand","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_53","first-page":"1","article-title":"Precision-recall-gain curves: PR analysis done right","volume":"28","author":"Flach","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/169\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:49:59Z","timestamp":1760147399000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/169"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,24]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010169"],"URL":"https:\/\/doi.org\/10.3390\/s23010169","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,12,24]]}}}