{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T09:18:30Z","timestamp":1778059110357,"version":"3.51.4"},"reference-count":73,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Sustainable Computing: Informatics and Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.suscom.2026.101344","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T07:58:17Z","timestamp":1774339097000},"page":"101344","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DNN-XGBoost framework for CO2 emissions prediction based on binary whale optimization and grey wolf optimization algorithms"],"prefix":"10.1016","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3048-1920","authenticated-orcid":false,"given":"Ahmed M.","family":"Elshewey","sequence":"first","affiliation":[]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.suscom.2026.101344_bib1","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1038\/ngeo1022","article-title":"Update on CO2 emissions","volume":"3","author":"Friedlingstein","year":"2010","journal-title":"Nat. Geosci."},{"key":"10.1016\/j.suscom.2026.101344_bib2","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.rser.2016.04.053","article-title":"Culture, income, and CO2 emission","volume":"62","author":"Disli","year":"2016","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.suscom.2026.101344_bib3","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1016\/j.rser.2014.07.205","article-title":"CO2 emissions, energy consumption, economic and population growth in Malaysia","volume":"41","author":"Begum","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.suscom.2026.101344_bib4","doi-asserted-by":"crossref","first-page":"4231","DOI":"10.1016\/j.jclepro.2017.10.287","article-title":"Forest, agriculture, renewable energy, and CO2 emission","volume":"172","author":"Waheed","year":"2018","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.suscom.2026.101344_bib5","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1016\/j.apenergy.2016.06.142","article-title":"Determinants of global CO2 emissions growth","volume":"184","author":"Jiang","year":"2016","journal-title":"Appl. Energy"},{"issue":"4","key":"10.1016\/j.suscom.2026.101344_bib6","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.gloenvcha.2008.08.001","article-title":"The drivers of Chinese CO2 emissions from 1980 to 2030","volume":"18","author":"Guan","year":"2008","journal-title":"Glob. Environ. Change"},{"key":"10.1016\/j.suscom.2026.101344_bib7","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.scitotenv.2018.08.229","article-title":"Carbon dioxide (CO2) emissions and economic growth: A systematic review of two decades of research from 1995 to 2017","volume":"649","author":"Mardani","year":"2019","journal-title":"Sci. Total Environ."},{"issue":"55","key":"10.1016\/j.suscom.2026.101344_bib8","doi-asserted-by":"crossref","first-page":"116601","DOI":"10.1007\/s11356-022-21723-8","article-title":"Machine learning-based time series models for effective CO2 emission prediction in India","volume":"30","author":"Kumari","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"10.1016\/j.suscom.2026.101344_bib9","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.trc.2016.04.007","article-title":"Prediction of vehicle CO2 emission and its application to eco-routing navigation","volume":"68","author":"Zeng","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"10.1016\/j.suscom.2026.101344_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2024.172319","article-title":"Carbon emission prediction models: a review","author":"Jin","year":"2024","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.suscom.2026.101344_bib11","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1016\/j.energy.2013.10.017","article-title":"A small-sample hybrid model for forecasting energy-related CO2 emissions","volume":"64","author":"Meng","year":"2014","journal-title":"Energy"},{"key":"10.1016\/j.suscom.2026.101344_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.122824","article-title":"Hybrid framework combining grey system model with Gaussian process and STL for CO2 emissions forecasting in developed countries","volume":"360","author":"Yuan","year":"2024","journal-title":"Appl. Energy"},{"issue":"1","key":"10.1016\/j.suscom.2026.101344_bib13","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.apr.2018.07.005","article-title":"Impact of technological innovation on CO2 emissions and emissions trend prediction on \u2018New Normal\u2019economy in China","volume":"10","author":"Yu","year":"2019","journal-title":"Atmos. Pollut. Res."},{"issue":"9","key":"10.1016\/j.suscom.2026.101344_bib14","doi-asserted-by":"crossref","first-page":"7648","DOI":"10.3390\/su15097648","article-title":"Application of artificial intelligence to predict CO2 emissions: critical step towards sustainable environment","volume":"15","author":"Nassef","year":"2023","journal-title":"Sustainability"},{"issue":"58","key":"10.1016\/j.suscom.2026.101344_bib15","doi-asserted-by":"crossref","first-page":"87983","DOI":"10.1007\/s11356-022-21277-9","article-title":"A daily carbon emission prediction model combining two-stage feature selection and optimized extreme learning machine","volume":"29","author":"Kong","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"issue":"1","key":"10.1016\/j.suscom.2026.101344_bib16","doi-asserted-by":"crossref","first-page":"142","DOI":"10.3390\/su16010142","article-title":"Neural network predictive models for alkali-activated concrete carbon emission using metaheuristic optimization algorithms","volume":"16","author":"Ayd\u0131n","year":"2023","journal-title":"Sustainability"},{"issue":"11","key":"10.1016\/j.suscom.2026.101344_bib17","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.3390\/atmos13111871","article-title":"Predicting CO2 emission footprint using AI through machine learning","volume":"13","author":"Meng","year":"2022","journal-title":"Atmosphere"},{"key":"10.1016\/j.suscom.2026.101344_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2023.139207","article-title":"Current status, future prediction and offset potential of fossil fuel CO2 emissions in China","volume":"426","author":"Cao","year":"2023","journal-title":"J. Clean. Prod."},{"issue":"1","key":"10.1016\/j.suscom.2026.101344_bib19","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1109\/TIV.2021.3102400","article-title":"Deep learning model based CO2 emissions prediction using vehicle telematics sensors data","volume":"8","author":"Singh","year":"2021","journal-title":"IEEE Trans. Intell. Veh."},{"key":"10.1016\/j.suscom.2026.101344_bib20","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jclepro.2016.02.053","article-title":"Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China","volume":"122","author":"Sun","year":"2016","journal-title":"J. Clean. Prod."},{"issue":"10","key":"10.1016\/j.suscom.2026.101344_bib21","doi-asserted-by":"crossref","first-page":"3169","DOI":"10.1007\/s13762-020-03079-z","article-title":"Prediction of CO2 emission from greenhouse to atmosphere with artificial neural networks and deep learning neural networks","volume":"18","author":"Altikat","year":"2021","journal-title":"Int. J. Environ. Sci. Technol."},{"issue":"3","key":"10.1016\/j.suscom.2026.101344_bib22","first-page":"9149","article-title":"An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO2 emission","volume":"45","author":"Nguyen","year":"2023","journal-title":"Energy Sources Part A Recovery Util. Environ. Eff."},{"issue":"15","key":"10.1016\/j.suscom.2026.101344_bib23","doi-asserted-by":"crossref","first-page":"19260","DOI":"10.1007\/s11356-020-12294-7","article-title":"Modeling and predicting city-level CO2 emissions using open access data and machine learning","volume":"28","author":"Li","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"issue":"3","key":"10.1016\/j.suscom.2026.101344_bib24","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1504\/IJGW.2022.124202","article-title":"Prediction of CO2 emission in transportation sector by computational intelligence techniques","volume":"27","author":"Cansiz","year":"2022","journal-title":"Int. J. Glob. Warm."},{"issue":"3","key":"10.1016\/j.suscom.2026.101344_bib25","doi-asserted-by":"crossref","first-page":"302","DOI":"10.4491\/eer.2016.153","article-title":"Prediction of carbon dioxide emissions based on principal component analysis with regularized extreme learning machine: The case of China","volume":"22","author":"Sun","year":"2017","journal-title":"Environ. Eng. Res."},{"key":"10.1016\/j.suscom.2026.101344_bib26","doi-asserted-by":"crossref","unstructured":"Safa, M., Nejat, M., Nuthall, P. and Greig, B., 2016. Predicting CO\u2082 emissions from farm inputs in wheat production using artificial neural networks and linear regression models-Case study in Canterbury, New Zealand.","DOI":"10.14569\/IJACSA.2016.070938"},{"key":"10.1016\/j.suscom.2026.101344_bib27","series-title":"2018 3rd International Conference for Convergence in Technology (I2CT)","first-page":"1","article-title":"Prediction model: CO 2 emission using machine learning","author":"Kadam","year":"2018"},{"issue":"9","key":"10.1016\/j.suscom.2026.101344_bib28","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.3390\/en11092475","article-title":"Forecasting carbon emissions related to energy consumption in Beijing-Tianjin-Hebei region based on grey prediction theory and extreme learning machine optimized by support vector machine algorithm","volume":"11","author":"Li","year":"2018","journal-title":"Energies"},{"key":"10.1016\/j.suscom.2026.101344_bib29","doi-asserted-by":"crossref","first-page":"4222","DOI":"10.1016\/j.egypro.2017.03.906","article-title":"Prediction of CO2 emissions based on multiple linear regression analysis","volume":"105","author":"Libao","year":"2017","journal-title":"Energy Procedia"},{"key":"10.1016\/j.suscom.2026.101344_bib30","article-title":"Carbon dioxide emission prediction using support vector machine","volume":"114","author":"Saleh","year":"2016"},{"issue":"23","key":"10.1016\/j.suscom.2026.101344_bib31","doi-asserted-by":"crossref","first-page":"10335","DOI":"10.3390\/su162310335","article-title":"Assessment of CO2 Emissions for Light-Duty Vehicles Using Dynamic Perturbation Additive Regression Trees","volume":"16","author":"Vu","year":"2024","journal-title":"Sustainability"},{"key":"10.1016\/j.suscom.2026.101344_bib32","series-title":"2022 25th International Conference on Computer and Information Technology (ICCIT)","first-page":"384","article-title":"Toward a Machine Learning Approach to Predict the CO 2 Rating of Fuel-Consuming Vehicles in Canada","author":"Bappon","year":"2022"},{"issue":"10","key":"10.1016\/j.suscom.2026.101344_bib33","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.3390\/math8101821","article-title":"Binary whale optimization algorithm for dimensionality reduction","volume":"8","author":"Hussien","year":"2020","journal-title":"Mathematics"},{"issue":"2","key":"10.1016\/j.suscom.2026.101344_bib34","doi-asserted-by":"crossref","first-page":"2833","DOI":"10.1016\/j.asoc.2010.11.013","article-title":"A binary particle swarm optimization for continuum structural topology optimization","volume":"11","author":"Luh","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.suscom.2026.101344_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107218","article-title":"Adaptive crossover operator based multi-objective binary genetic algorithm for feature selection in classification","volume":"227","author":"Xue","year":"2021","journal-title":"Knowl. -Based Syst."},{"key":"10.1016\/j.suscom.2026.101344_bib36","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.ins.2017.08.047","article-title":"A return-cost-based binary firefly algorithm for feature selection","volume":"418","author":"Zhang","year":"2017","journal-title":"Inf. Sci."},{"key":"10.1016\/j.suscom.2026.101344_bib37","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey wolf optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"issue":"5","key":"10.1016\/j.suscom.2026.101344_bib38","doi-asserted-by":"crossref","first-page":"24","DOI":"10.3905\/jpm.2021.1.219","article-title":"Mean-variance optimization for asset allocation","volume":"47","author":"Kim","year":"2021","journal-title":"J. Portf. Manag."},{"key":"10.1016\/j.suscom.2026.101344_bib39","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.applthermaleng.2019.04.038","article-title":"Design of heat exchangers using falcon optimization algorithm","volume":"156","author":"de Vasconcelos Segundo","year":"2019","journal-title":"Appl. Therm. Eng."},{"issue":"1","key":"10.1016\/j.suscom.2026.101344_bib40","doi-asserted-by":"crossref","first-page":"23368","DOI":"10.1038\/s41598-024-73559-6","article-title":"Orthopedic disease classification based on breadth-first search algorithm","volume":"14","author":"Elshewey","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.suscom.2026.101344_bib41","unstructured":"Government of Canada. CO2 emissions dataset. Open Government Portal. Available at: \u3008https:\/\/open.canada.ca\/data\/en\/dataset\/98f1a129-f628-4ce4-b24d-6f16bf24dd64\u3009."},{"issue":"2","key":"10.1016\/j.suscom.2026.101344_bib42","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/S1525-1578(10)60455-2","article-title":"Analysis of microarray data using Z score transformation","volume":"5","author":"Cheadle","year":"2003","journal-title":"J. Mol. Diagn."},{"key":"10.1016\/j.suscom.2026.101344_bib43","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1007\/s11192-014-1294-7","article-title":"Comparison of the effect of mean-based method and z-score for field normalization of citations at the level of Web of Science subject categories","volume":"101","author":"Zhang","year":"2014","journal-title":"Scientometrics"},{"key":"10.1016\/j.suscom.2026.101344_bib44","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The whale optimization algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"10.1016\/j.suscom.2026.101344_bib45","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.1007\/s00521-018-3796-3","article-title":"Binary whale optimization algorithm and its application to unit commitment problem","volume":"32","author":"Kumar","year":"2020","journal-title":"Neural Comput. Appl."},{"issue":"3","key":"10.1016\/j.suscom.2026.101344_bib46","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s13042-019-00996-5","article-title":"Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm","volume":"11","author":"Tawhid","year":"2020","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.suscom.2026.101344_bib47","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey wolf optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"10.1016\/j.suscom.2026.101344_bib48","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s00521-017-3272-5","article-title":"Grey wolf optimizer: a review of recent variants and applications","volume":"30","author":"Faris","year":"2018","journal-title":"Neural Comput. Appl."},{"issue":"6","key":"10.1016\/j.suscom.2026.101344_bib49","doi-asserted-by":"crossref","first-page":"2713","DOI":"10.1007\/s00521-023-09202-8","article-title":"Review of the grey wolf optimization algorithm: variants and applications","volume":"36","author":"Liu","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.suscom.2026.101344_bib50","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.knosys.2019.01.018","article-title":"The defect of the Grey Wolf optimization algorithm and its verification method","volume":"171","author":"Niu","year":"2019","journal-title":"Knowl. -Based Syst."},{"key":"10.1016\/j.suscom.2026.101344_bib51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","article-title":"Methods for interpreting and understanding deep neural networks","volume":"73","author":"Montavon","year":"2018","journal-title":"Digit. Signal Process."},{"issue":"11","key":"10.1016\/j.suscom.2026.101344_bib52","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1038\/s41380-019-0365-9","article-title":"Deep neural networks in psychiatry","volume":"24","author":"Durstewitz","year":"2019","journal-title":"Mol. Psychiatry"},{"issue":"1","key":"10.1016\/j.suscom.2026.101344_bib53","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","article-title":"A survey of uncertainty in deep neural networks","volume":"56","author":"Gawlikowski","year":"2023","journal-title":"Artif. Intell. Rev."},{"issue":"4","key":"10.1016\/j.suscom.2026.101344_bib54","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.tics.2019.01.009","article-title":"Deep neural networks as scientific models","volume":"23","author":"Cichy","year":"2019","journal-title":"Trends Cogn. Sci."},{"key":"10.1016\/j.suscom.2026.101344_bib55","doi-asserted-by":"crossref","first-page":"24797","DOI":"10.1109\/ACCESS.2022.3154386","article-title":"Encoding and tuning of THz metasurface-based refractive index sensor with behavior prediction using XGBoost Regressor","volume":"10","author":"Patel","year":"2022","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.suscom.2026.101344_bib56","first-page":"2151","article-title":"Prediction of house price using xgboost regression algorithm","volume":"12","author":"Avanijaa","year":"2021","journal-title":"Turk. J. Comput. Math. Educ. (TURCOMAT)"},{"key":"10.1016\/j.suscom.2026.101344_bib57","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109067","article-title":"A neural network boosting regression model based on XGBoost","volume":"125","author":"Dong","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.suscom.2026.101344_bib58","doi-asserted-by":"crossref","DOI":"10.3389\/feart.2021.761990","article-title":"Developing an XGBoost regression model for predicting young\u2019s modulus of intact sedimentary rocks for the stability of surface and subsurface structures","volume":"9","author":"Shahani","year":"2021","journal-title":"Front. Earth Sci."},{"issue":"17","key":"10.1016\/j.suscom.2026.101344_bib59","doi-asserted-by":"crossref","first-page":"6416","DOI":"10.3390\/en15176416","article-title":"An optimized gradient boosting model by genetic algorithm for forecasting crude oil production","volume":"15","author":"Alkhammash","year":"2022","journal-title":"Energies"},{"key":"10.1016\/j.suscom.2026.101344_bib60","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1016\/j.egyr.2021.02.006","article-title":"Prediction of home energy consumption based on gradient boosting regression tree","volume":"7","author":"Nie","year":"2021","journal-title":"Energy Rep."},{"key":"10.1016\/j.suscom.2026.101344_bib61","doi-asserted-by":"crossref","first-page":"17760","DOI":"10.1109\/ACCESS.2024.3359115","article-title":"Deep learning for multi-output regression using gradient boosting","volume":"12","author":"Emami","year":"2024","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.suscom.2026.101344_bib62","doi-asserted-by":"crossref","DOI":"10.1029\/2020JB020135","article-title":"Predicting global marine sediment density using the random forest regressor machine learning algorithm","volume":"126","author":"Graw","year":"2021","journal-title":"J. Geophys. Res. Solid Earth"},{"issue":"1","key":"10.1016\/j.suscom.2026.101344_bib63","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/1536867X20909688","article-title":"The random forest algorithm for statistical learning","volume":"20","author":"Schonlau","year":"2020","journal-title":"Stata J."},{"issue":"3","key":"10.1016\/j.suscom.2026.101344_bib64","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.cj.2016.01.008","article-title":"Estimation of biomass in wheat using random forest regression algorithm and remote sensing data","volume":"4","author":"Wang","year":"2016","journal-title":"Crop J."},{"issue":"3","key":"10.1016\/j.suscom.2026.101344_bib65","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2005.05.008","article-title":"Decision tree regression for soft classification of remote sensing data","volume":"97","author":"Xu","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.suscom.2026.101344_bib66","first-page":"3571","article-title":"Comparative study of regressor and classifier with decision tree using modern tools","volume":"56","author":"Kushwah","year":"2022"},{"key":"10.1016\/j.suscom.2026.101344_bib67","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemosphere.2022.134250","article-title":"Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression","volume":"299","author":"Balogun","year":"2022","journal-title":"Chemosphere"},{"key":"10.1016\/j.suscom.2026.101344_bib68","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"10.1016\/j.suscom.2026.101344_bib69","first-page":"67","article-title":"Support vector regression","author":"Awad","year":"2015","journal-title":"Effic. Learn. Mach. Theor. Concepts Appl. Eng. Syst. Des."},{"issue":"10","key":"10.1016\/j.suscom.2026.101344_bib70","first-page":"203","article-title":"Support vector regression","volume":"11","author":"Basak","year":"2007","journal-title":"Neural Inf. Process. Lett. Rev."},{"key":"10.1016\/j.suscom.2026.101344_bib71","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.neucom.2017.04.018","article-title":"An efficient instance selection algorithm for k nearest neighbor regression","volume":"251","author":"Song","year":"2017","journal-title":"Neurocomputing"},{"issue":"10","key":"10.1016\/j.suscom.2026.101344_bib72","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/j.spl.2007.11.014","article-title":"A k-nearest neighbor approach for functional regression","volume":"78","author":"Lalo\u00eb","year":"2008","journal-title":"Stat. Probab. Lett."},{"key":"10.1016\/j.suscom.2026.101344_bib73","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s10489-014-0518-0","article-title":"K-nearest neighbor-based weighted twin support vector regression","volume":"41","author":"Xu","year":"2014","journal-title":"Appl. Intell."}],"container-title":["Sustainable Computing: Informatics and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2210537926000545?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2210537926000545?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T08:44:13Z","timestamp":1778057053000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2210537926000545"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":73,"alternative-id":["S2210537926000545"],"URL":"https:\/\/doi.org\/10.1016\/j.suscom.2026.101344","relation":{},"ISSN":["2210-5379"],"issn-type":[{"value":"2210-5379","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DNN-XGBoost framework for CO2 emissions prediction based on binary whale optimization and grey wolf optimization algorithms","name":"articletitle","label":"Article Title"},{"value":"Sustainable Computing: Informatics and Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.suscom.2026.101344","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"101344"}}