{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T02:32:42Z","timestamp":1761186762847,"version":"build-2065373602"},"reference-count":106,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union: Next Generation EU through the Program Greece 2.0 National Recovery and Resilience Plan","award":["TAEDK-06195"],"award-info":[{"award-number":["TAEDK-06195"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The spread of contemporary artificial intelligence technologies, particularly machine learning, has significantly enhanced the capacity to predict asymmetrical natural disasters. Wildfires constitute a prominent example, as machine learning can be employed to forecast not only their spatial extent but also their environmental and socio-economic impacts, propagation dynamics, symmetrical or asymmetrical patterns, and even their duration. Such predictive capabilities are of critical importance for effective wildfire management, as they inform the strategic allocation of material resources, and the optimal deployment of human personnel in the field. Beyond that, examination of symmetrical or asymmetrical patterns in fires helps us to understand the causes and dynamics of their spread. The necessity of leveraging machine learning tools has become imperative in our era, as climate change has disrupted traditional wildfire management models due to prolonged droughts, rising temperatures, asymmetrical patterns, and the increasing frequency of extreme weather events. For this reason, our research seeks to fully exploit the potential of Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (MRMR), and Grammatical Evolution, both for constructing Artificial Features and for generating Neural Network Architectures. For this purpose, we utilized the highly detailed and publicly available symmetrical datasets provided by the Hellenic Fire Service for the years 2014\u20132021, which we further enriched with meteorological data, corresponding to the prevailing conditions at both the onset and the suppression of each wildfire event. The research concluded that the Feature Construction technique, using Grammatical Evolution, combines both symmetrical and asymmetrical conditions, and that weather phenomena may provide and outperform other methods in terms of stability and accuracy. Therefore, the asymmetric phenomenon in our research is defined as the unpredictable outcome of climate change (meteorological data) which prolongs the duration of forest fires over time. Specifically, in the model accuracy of wildfire duration using Feature Construction, the mean error was 8.25%, indicating an overall accuracy of 91.75%.<\/jats:p>","DOI":"10.3390\/sym17111785","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T01:14:02Z","timestamp":1761182042000},"page":"1785","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting the Forest Fire Duration Enriched with Meteorological Data Using Feature Construction Techniques"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9895-8880","authenticated-orcid":false,"given":"Constantina","family":"Kopitsa","sequence":"first","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2343-2733","authenticated-orcid":false,"given":"Ioannis G.","family":"Tsoulos","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0675-9088","authenticated-orcid":false,"given":"Andreas","family":"Miltiadous","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, Greece"}]},{"given":"Vasileios","family":"Charilogis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Field, J.F. (2017). London, Londoners and the Great Fire of 1666: Disaster and Recovery, Routledge.","DOI":"10.4324\/9781315099323"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20150164","DOI":"10.1098\/rstb.2015.0164","article-title":"The discovery of fire by humans: A long and convoluted process","volume":"371","author":"Gowlett","year":"2016","journal-title":"Philos. Trans. Biol."},{"key":"ref_3","unstructured":"Heinonen, K. (2024, November 29). The Fire of Prometheus: More Than Just a Gift to Humanity. Greek Mythology. 19 November 2024. Available online: https:\/\/greek.mythologyworldwide.com\/the-fire-of-prometheus-more-than-just-a-gift-to-humanity\/."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1080\/08941920490452445","article-title":"Thinking of wildfire as a natural hazard","volume":"17","author":"McCaffrey","year":"2004","journal-title":"Soc. Nat. Resour."},{"key":"ref_5","unstructured":"Van Hees, P. (2024, December 03). The Burning Challenge of Fire Safety. ISO, International Organization for Standardization. Available online: https:\/\/www.iso.org\/news\/2014\/11\/Ref1906.html."},{"key":"ref_6","unstructured":"UNEP (2024, December 04). United Nations Environment Programme. Number of Wildfires to Rise by 50 per Cent by 2100 and Governments Are Not Prepared, Experts Warn. 23 February 2022. Available online: https:\/\/www.unep.org\/news-and-stories\/press-release\/number-wildfires-rise-50-cent-2100-and-governments-are-not-prepared."},{"key":"ref_7","unstructured":"NASA (2024, November 29). Carbon Dioxide, Vital Signs. October 2024, Available online: https:\/\/climate.nasa.gov\/vital-signs\/carbon-dioxide\/?intent=121."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1029\/2006GL025734","article-title":"Climate change hot-spots","volume":"33","author":"Giorgi","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Iliopoulos, N., Aliferis, I., and Chalaris, M. (2024). Effect of Climate Evolution on the Dynamics of the Wildfires in Greece. Fire, 7.","DOI":"10.3390\/fire7050162"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Satish, M., Babu, S.M., Kumar, P.P., Devi, S., and Reddy, K.P. (2023, January 7\u20138). Artificial Intelligence (AI) and the Prediction of Climate Change Impacts. Proceedings of the 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Hamburg, Germany.","DOI":"10.1109\/ICCCMLA58983.2023.10346636"},{"key":"ref_11","unstructured":"Walsh, D. (2024, November 29). Tackling Climate Change with Machine Learning. MIT Management Sloan School. Climate Change. 24 October 2023. Available online: https:\/\/mitsloan.mit.edu\/ideas-made-to-matter\/tackling-climate-change-machine-learning."},{"key":"ref_12","unstructured":"ISO (2024, November 30). Machine Learning (ML): All There Is to Know. International Organization for Standardization. Available online: https:\/\/www.iso.org\/artificial-intelligence\/machine-learning."},{"key":"ref_13","unstructured":"Watson, I. (2024, November 30). How Alan Turing Invented the Computer Age. Scientific American. Published: 26 April 2012. Available online: https:\/\/blogs.scientificamerican.com\/guest-blog\/how-alan-turing-invented-the-computer-age\/."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1139\/er-2020-0019","article-title":"A review of machine learning applications in wildfire science and management","volume":"28","author":"Jain","year":"2020","journal-title":"Environ. Rev."},{"key":"ref_15","unstructured":"Xiao, H. (2023). Estimating fire duration using regression methods. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"446","DOI":"10.3390\/make4020020","article-title":"Machine learning in disaster management: Recent developments in methods and applications","volume":"4","author":"Linardos","year":"2022","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"117723","DOI":"10.1016\/j.foreco.2019.117723","article-title":"A Bayesian network model for prediction and analysis of possible forest fire causes","volume":"457","author":"Sevinc","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104716","DOI":"10.1016\/j.ijdrr.2024.104716","article-title":"Modeling of the cascading impacts of drought and forest fire based on a Bayesian network","volume":"111","author":"Chen","year":"2024","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kim, B., and Lee, J. (2021). A Bayesian network-based information fusion combined with DNNs for robust video fire detection. Appl. Sci., 11.","DOI":"10.3390\/app11167624"},{"key":"ref_20","first-page":"20","article-title":"Peatland Forest Fire Prevention Using Wireless Sensor Network Based on Na\u00efve Bayes Classifier","volume":"3","author":"Nugroho","year":"2019","journal-title":"Kne Soc. Sci."},{"key":"ref_21","first-page":"555","article-title":"Classification of Hotspots Causing Forest and Land Fires Using the Naive Bayes Algorithm","volume":"1","author":"Zainul","year":"2022","journal-title":"Interdiscip. Soc. Stud."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15","DOI":"10.17509\/seict.v3i1.47537","article-title":"Wildfires Classification Using Feature Selection with K-NN, Na\u00efve Bayes, and ID3 Algorithms","volume":"3","author":"Karo","year":"2022","journal-title":"J. Softw. Eng. Inf. Commun. Technol. (SEICT)"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1007\/s10342-011-0488-2","article-title":"Logistic regression models for human-caused wildfire risk estimation: Analysing the effect of the spatial accuracy in fire occurrence data","volume":"130","year":"2011","journal-title":"Eur. J. For. Res."},{"key":"ref_24","first-page":"35","article-title":"Predicting wildfire vulnerability using logistic regression and artificial neural networks: A case study in Brazil\u2019s Federal District","volume":"28","author":"Matricardi","year":"2018","journal-title":"Int. Wildland Fire"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1080\/19475705.2019.1615559","article-title":"Probabilistic modelling of wildfire occurrence based on logistic regression, Niassa Reserve, Mozambique","volume":"10","author":"Nhongo","year":"2019","journal-title":"Geomat. Hazards Risk"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Peng, W., Wei, Y., Chen, G., Lu, G., Ye, Q., Ding, R., and Cheng, Z. (2023). Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China. Forests, 14.","DOI":"10.3390\/f14122352"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hossain, F.A., Zhang, Y., Yuan, C., and Su, C.Y. (2019, January 23\u201327). Wildfire flame and smoke detection using static image features and artificial neural network. Proceedings of the 2019 1st International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China.","DOI":"10.1109\/ICIAI.2019.8850811"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lall, S., and Mathibela, B. (2016, January 18\u201320). The application of artificial neural networks for wildfire risk prediction. Proceedings of the 2016 International Conference on Robotics and Automation for Humanitarian Applications (RAHA), Amritapuri, India.","DOI":"10.1109\/RAHA.2016.7931880"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.firesaf.2019.01.006","article-title":"Predictive modeling of wildfires: A new dataset and machine learning approach","volume":"104","author":"Sayad","year":"2019","journal-title":"Fire Saf. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6930812","DOI":"10.1155\/2022\/6930812","article-title":"Using multilayer perceptron to predict forest fires in jiangxi province, southeast china","volume":"2022","author":"Gao","year":"2022","journal-title":"Discret. Dyn. Nat. Soc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Latifah, A.L., Shabrina, A., Wahyuni, I.N., and Sadikin, R. (2019, January 23\u201324). Evaluation of Random Forest model for forest fire prediction based on climatology over Borneo. Proceedings of the 2019 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), Tangerang, Indonesia.","DOI":"10.1109\/IC3INA48034.2019.8949588"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Malik, A., Rao, M.R., Puppala, N., Koouri, P., Thota, V.A.K., Liu, Q., Chiao, S., and Gao, J. (2021). Data-driven wildfire risk prediction in northern California. Atmosphere, 12.","DOI":"10.3390\/atmos12010109"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106408","DOI":"10.1016\/j.jastp.2024.106408","article-title":"Interpretable artificial intelligence models for predicting lightning prone to inducing forest fires","volume":"267","author":"Song","year":"2025","journal-title":"J. Atmos. Sol.-Terr. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gao, C., Lin, H., and Hu, H. (2023). Forest-fire-risk prediction based on random forest and backpropagation neural network of Heihe area in Heilongjiang province, China. Forests, 14.","DOI":"10.3390\/f14020170"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111368","DOI":"10.1016\/j.ress.2025.111368","article-title":"Development and Validation of a Novel Method to Predict Flame Behavior in Tank Fires Based on CFD Modeling and Machine Learning","volume":"264","author":"Hu","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"529","DOI":"10.5194\/essd-11-529-2019","article-title":"The Global Fire Atlas of individual fire size, duration, speed and direction","volume":"11","author":"Andela","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kc, U., Aryal, J., Hilton, J., and Garg, S. (2021). A surrogate model for rapidly assessing the size of a wildfire over time. Fire, 4.","DOI":"10.3390\/fire4020020"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.jdd.2024.100002","article-title":"A deep learning-based surrogate model for spatial-temporal temperature field prediction in subway tunnel fires via CFD simulation","volume":"1","author":"Xie","year":"2025","journal-title":"J. Dyn. Disasters"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"176746","DOI":"10.1109\/ACCESS.2019.2957837","article-title":"A neural network model for wildfire scale prediction using meteorological factors","volume":"7","author":"Liang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"107127","DOI":"10.1016\/j.psep.2025.107127","article-title":"Prediction method and application of temperature distribution in typical confined space spill fires based on deep learning","volume":"198","author":"Zhai","year":"2025","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e2685","DOI":"10.1002\/env.2685","article-title":"Modeling the duration and size of wildfires using joint mixture models","volume":"32","author":"Xi","year":"2021","journal-title":"Environmetrics"},{"key":"ref_42","unstructured":"WFCA (2024, December 04). Western Fire Chiefs Association. How Long Do Wildfires Last? October 2022. Available online: https:\/\/wfca.com\/wildfire-articles\/how-long-do-wildfires-last\/."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1029\/2000GL012121","article-title":"Warming asymmetry in climate change simulations","volume":"28","author":"Flato","year":"2001","journal-title":"Geophys. Lett."},{"key":"ref_44","first-page":"13","article-title":"Behavioural responses to climate change: Asymmetry of intentions and impacts","volume":"29","author":"Whitmarsh","year":"2009","journal-title":"J. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Xu, Y., and Ramanathan, V. (2012). Latitudinally asymmetric response of global surface temperature: Implications for regional climate change. Geophys. Res. Lett., 39.","DOI":"10.1029\/2012GL052116"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.epsl.2018.07.011","article-title":"A symmetrical CO2 peak and asymmetrical climate change during the middle Miocene","volume":"499","author":"Ji","year":"2018","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Gao, C., An, R., Wang, W., Shi, C., Wang, M., Liu, K., Wu, X., Wu, G., and Shu, L. (2021). Asymmetrical lightning fire season expansion in the boreal forest of Northeast China. Forests, 12.","DOI":"10.3390\/f12081023"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s10710-005-2988-7","article-title":"Genetic Programming with a Genetic Algorithm for Feature Construction and Selection","volume":"6","author":"Smith","year":"2005","journal-title":"Genet. Program. Evolvable Mach."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/0098-3004(93)90090-R","article-title":"Principal components analysis (PCA)","volume":"19","author":"Ratajczak","year":"1993","journal-title":"Comput. Geosci."},{"key":"ref_50","unstructured":"Cadima, J., and Jolliffe, I.T. (2024, November 16). Principal Component analysis: A Review and Recent Developments, National Library of Medicine, Available online: https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC4792409\/."},{"key":"ref_51","unstructured":"(2024, November 16). i2tutorials. What Are the Pros and Cons of the PCA? 1 October 2019. Available online: https:\/\/www.i2tutorials.com\/what-are-the-pros-and-cons-of-the-pca\/."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Park, S.C. (2024). Physical Meaning of Principal Component Analysis for Lattice Systems with Translational Invariance. arXiv.","DOI":"10.1103\/PhysRevE.111.045301"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"362","DOI":"10.9734\/jabb\/2024\/v27i91306","article-title":"Principal Component Analysis of Morphometric Traits in Kashmir Merino Sheep","volume":"27","author":"Sarma","year":"2024","journal-title":"J. Adv. Biol. Biotechnol."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Gambardella, C., Parente, R., Ciambrone, A., and Casbarra, M. (2021). A Principal Components Analysis-Based Method for the Detection of Cannabis Plants Using Representation data by Remote Sensing. Data, 6.","DOI":"10.3390\/data6100108"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"121","DOI":"10.2298\/SJEE1201121S","article-title":"Face Recognition Using Eigenface Approach","volume":"9","author":"Slavkovic","year":"2012","journal-title":"Serbian J. Electr. Eng."},{"key":"ref_56","first-page":"183","article-title":"The Selection of Winning Stocks Using Principal Component Analysis","volume":"1","author":"Hargreaves","year":"2015","journal-title":"Am. J. Mark."},{"key":"ref_57","first-page":"183","article-title":"On Optimizing Hyperspectral Inversion of Soil Copper Content by Kernel Principal Component Analysis","volume":"16","author":"Xu","year":"2024","journal-title":"Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"109643","DOI":"10.1016\/j.buildenv.2022.109643","article-title":"Factors influencing indoor air pollution in buildings using PCA-LMBP neural network: A case study of a university campus","volume":"225","author":"Zhang","year":"2022","journal-title":"Build. Environ."},{"key":"ref_59","first-page":"1","article-title":"A brief description of the Levenberg-Marquardt algorithm implemented by levmar","volume":"4","author":"Lourakis","year":"2005","journal-title":"Found. Res. Technol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"100459","DOI":"10.1016\/j.imu.2020.100459","article-title":"Breast cancer risk assessment and early diagnosis using Principal Component Analysis and support vector machine techniques","volume":"21","author":"Akinnuwesi","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Awad, M., Khanna, R., Awad, M., and Khanna, R. (2015). Support vector machines for classification. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, Apress.","DOI":"10.1007\/978-1-4302-5990-9"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.54097\/hset.v44i.7159","article-title":"Predicting forest fire with linear regression and random forest","volume":"44","author":"Guan","year":"2023","journal-title":"Highlights Sci. Eng. Technol."},{"key":"ref_63","unstructured":"Nikolov, N., Bothwell, P., and Snook, J. (2022). Developing a gridded model for probabilistic forecasting of wildland-fire ignitions across the lower 48 States, USFS-CSU Joint Venture Agreement Phase 2 (2019\u20132021)-Final Report."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Nikolov, N., Bothwell, P., and Snook, J. (2024). Probalistic forecasting of lightning strikes over the Continental USA and Alaska: Model development and verification. Fire, 7.","DOI":"10.20944\/preprints202401.1281.v1"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1142\/S0219720005001004","article-title":"Minimum redundancy feature selection from microarray gene expression data","volume":"3","author":"Ding","year":"2005","journal-title":"J. Bioinform. Comput. Biol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1002\/int.21833","article-title":"Fast-mRMR: Fast Minimum Redundancy Maximum Relevance Algorithm for High-Dimensional Big Data","volume":"32","author":"Lastra","year":"2017","journal-title":"Int. J. Intell. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Anand, R., and Wang, M. (2019, January 5\u20138). Maximum relevance and minimum redundancy feature selection methods for a marketing machine learning platform. Proceedings of the 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Washington, DC, USA.","DOI":"10.1109\/DSAA.2019.00059"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","article-title":"Random forest","volume":"47","author":"Rigatti","year":"2017","journal-title":"J. Insur."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wu, H., Yang, T., Li, H., and Zhou, Z. (2023). Air quality prediction model based on mRMR\u2013RF feature selection and ISSA\u2013LSTM. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-39838-4"},{"key":"ref_70","first-page":"1419316","article-title":"Hyperspectral indices data fusion-based machine learning enhanced by MRMR algorithm for estimating maize chlorophyll content","volume":"15","author":"Elbeltagi","year":"2024","journal-title":"Front. Plant Sci. Sect. Tech. Adv. Plant Sci."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Liu, J., Sun, H., Li, Y., Fang, W., and Niu, S. (2020). An improved power system transient stability prediction model based on mRMR feature selection and WTA ensemble learning. Appl. Sci., 10.","DOI":"10.3390\/app10072255"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000). Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems, Springer.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"117026","DOI":"10.1109\/ACCESS.2024.3449096","article-title":"A New Approach based on Deep Features of Convolutional Neural Networks for Partial Discharge Detection in Power Systems","volume":"12","author":"Eristi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_74","first-page":"173470","article-title":"Recognition algorithm of acoustic emission signals based on conditional random field model in storage tank floor inspection using inner detector","volume":"2015","author":"Li","year":"2015","journal-title":"Shock Vib."},{"key":"ref_75","first-page":"3697","article-title":"An evaluation of climate change impacts on extreme sea level variability: Coastal area of New York City","volume":"28","author":"Karamouz","year":"2014","journal-title":"Water Resour."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1109\/4235.942529","article-title":"Grammatical evolution","volume":"5","author":"Ryan","year":"2001","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.neucom.2008.01.017","article-title":"Neural network construction and training using grammatical evolution","volume":"72","author":"Tsoulos","year":"2008","journal-title":"Neurocomputing"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"12210","DOI":"10.1016\/j.eswa.2009.04.065","article-title":"Location of amide I mode of vibration in computed data utilizing constructed neural networks","volume":"36","author":"Papamokos","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1016\/j.neucom.2008.12.004","article-title":"Solving differential equations with constructed neural networks","volume":"72","author":"Tsoulos","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3389\/fict.2019.00010","article-title":"Application of Machine Learning in a Parkinson\u2019s Disease Digital Biomarker Dataset Using Neural Network Construction (NNC) Methodology Discriminates Patient Motor Status","volume":"6","author":"Tsoulos","year":"2019","journal-title":"Front. ICT"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"120079","DOI":"10.1016\/j.eswa.2023.120079","article-title":"Performance and early drop prediction for higher education students using machine learning","volume":"225","author":"Christou","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Toki, E.I., Pange, J., Tatsis, G., Plachouras, K., and Tsoulos, I.G. (2024). Utilizing Constructed Neural Networks for Autism Screening. Appl. Sci., 14.","DOI":"10.3390\/app14073053"},{"key":"ref_83","unstructured":"Backus, J.W. (1959, January 15\u201320). The Syntax and Semantics of the Proposed International Algebraic Language of the Zurich ACM-GAMM Conference. Proceedings of the International Conference on Information Processing, UNESCO, Paris, France."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1016\/j.patrec.2008.02.007","article-title":"Selecting and constructing features using grammatical evolution","volume":"29","author":"Gavrilis","year":"2008","journal-title":"Pattern Recognit. Lett."},{"key":"ref_85","first-page":"498","article-title":"Neural Recognition and Genetic Features Selection for Robust Detection of E-Mail Spam","volume":"Volume 3955","author":"Gavrilis","year":"2006","journal-title":"Hellenic Conference on Artificial Intelligence"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.bspc.2007.05.003","article-title":"Novel approach for fetal heart rate classification introducing grammatical evolution","volume":"2","author":"Georgoulas","year":"2007","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"9991","DOI":"10.1016\/j.eswa.2011.02.009","article-title":"Grammatical evolution for features of epileptic oscillations in clinical intracranial electroencephalograms","volume":"38","author":"Smart","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Tzallas, A.T., Tsoulos, I., Tsipouras, M.G., Giannakeas, N., Androulidakis, I., and Zaitseva, E. (2016, January 22\u201323). Classification of EEG signals using feature creation produced by grammatical evolution. Proceedings of the 24th Telecommunications Forum (TELFOR), Belgrade, Serbia.","DOI":"10.1109\/TELFOR.2016.7818809"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1162\/neco.1991.3.2.246","article-title":"Universal Approximation Using Radial-Basis-Function Networks","volume":"3","author":"Park","year":"1991","journal-title":"Neural Comput."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"5438","DOI":"10.1109\/TIE.2011.2164773","article-title":"Advantages of Radial Basis Function Networks for Dynamic System Design","volume":"58","author":"Yu","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Bishop, C. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation by superpositions of a sigmoidal function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math. Control Signals Syst."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/BF01589118","article-title":"A Tolerant Algorithm for Linearly Constrained Optimization Calculations","volume":"45","author":"Powell","year":"1989","journal-title":"Math. Program."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"7584","DOI":"10.21105\/joss.07584","article-title":"OPTIMUS: A Multidimensional Global Optimization Package","volume":"10","author":"Tsoulos","year":"2025","journal-title":"J. Open Source Softw."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM Sigkdd Explor. Newsl."},{"key":"ref_96","first-page":"20","article-title":"Data mining in educational system using weka. In International conference on emerging technology trends","volume":"3","author":"Aher","year":"2011","journal-title":"Found. Comput. Sci."},{"key":"ref_97","first-page":"447","article-title":"Educational data mining and analysis of students\u2019 academic performance using WEKA","volume":"9","author":"Hussain","year":"2018","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.pec.2007.03.007","article-title":"Outcomes of educational interventions in type 2 diabetes: WEKA data-mining analysis","volume":"67","author":"Sigurdardottir","year":"2007","journal-title":"Patient Educ. Couns."},{"key":"ref_99","first-page":"55","article-title":"Comparison of different classification techniques using WEKA for hematological data","volume":"4","author":"Amin","year":"2015","journal-title":"Am. J. Eng. Res."},{"key":"ref_100","first-page":"713","article-title":"Na\u00efve Bayes","volume":"15","author":"Webb","year":"2010","journal-title":"Encycl. Mach. Learn."},{"key":"ref_101","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). ADAM: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA."},{"key":"ref_102","first-page":"801","article-title":"MLPACK: A Scalable C++ Machine Learning Library","volume":"14","author":"Curtin","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"ref_103","first-page":"3735","article-title":"BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits","volume":"15","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_104","first-page":"1","article-title":"Adagrad stepsizes: Sharp convergence over nonconvex landscapes","volume":"21","author":"Ward","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"100297","DOI":"10.1016\/j.softx.2019.100297","article-title":"NNC: A tool based on Grammatical Evolution for data classification and differential equation solving","volume":"10","author":"Tsoulos","year":"2019","journal-title":"SoftwareX"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Tsoulos, I.G. (2022). QFC: A Parallel Software Tool for Feature Construction, Based on Grammatical Evolution. Algorithms, 15.","DOI":"10.3390\/a15080295"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/11\/1785\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T01:37:23Z","timestamp":1761183443000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/11\/1785"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,22]]},"references-count":106,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["sym17111785"],"URL":"https:\/\/doi.org\/10.3390\/sym17111785","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,22]]}}}